CN114740891B - Aircraft towing path planning method and device combining heuristic and optimal control - Google Patents

Abstract

The present application relates to a method and device for aircraft towing path planning combining heuristic and optimal control, the method comprising: using an A* algorithm for path planning to obtain an initial path; starting from the first node, re-searching each node in the initial path; when there is an obstacle within the preset range of the path between the first node and the second node, the second node is used as the key point, otherwise it is determined whether there is an obstacle within the preset range of the path between the first node and the third node, until it is determined whether there is an obstacle within the preset range of the path between the last node and the adjacent node, and a key point set is obtained; a kinematic model is established for the process of aircraft transportation on the platform, and constraints are met to obtain an optimal control model, and converted into a nonlinear programming model; key points are selected for geometric solution to obtain the corresponding key point posture; and the optimal path is output. The present method can be used to perform path planning and obtain the shortest path under the consideration of kinematic and terminal posture constraints.

Description

Aircraft traction path planning method and device combining heuristic and optimal control

Technical Field

The application relates to the technical field of path planning, in particular to an aircraft traction path planning method and device combining heuristic and optimal control.

Background

The dispatching efficiency of the aircraft is commonly influenced by a plurality of factors, such as equipment factors, human factors, environmental factors and the like, and a narrow platform space is one of important factors for limiting the dispatching efficiency, so that the research of a path planning method suitable for the narrow platform space is a basis for ensuring safe and efficient dispatching operation of the aircraft on the platform, and has important significance for realizing automation of dispatching of the aircraft and improving dispatching efficiency of a cluster.

At present, the research on the plane traction path planning is still in a starting stage, and the research method comprises a geometric method, an optimal control method, a unit decomposition method, an intelligent algorithm, an artificial potential field method, reinforcement learning, a combination algorithm and the like aiming at various path planning problems.

The unit decomposition method performs path planning by taking the shortest path as a target, and can not well show the motion state of a planning object although the shortest path is found, when the map is large and the number of units is large, the algorithm operand is increased rapidly, and the terminal pose constraint of the planning target can not be considered.

The artificial potential field method has the advantages that the operation efficiency is high, when an object is far away from a target point, attractive force is extremely large, relatively small repulsive force possibly hits an obstacle on the path of the object under the condition of even being negligible, when the obstacle is near the target point, the repulsive force is extremely large, the attractive force is relatively small, the object is hard to reach the target point, at a certain point, the attractive force and the repulsive force are just equal in size and opposite in direction, and the object is easy to sink into a local optimal solution or shake.

The intelligent algorithm is based on a large population by setting an objective function, and under a certain constraint condition, the optimal solution meeting the constraint is gradually searched through continuous iterative screening, but the algorithm is easy to fall into a local optimal solution, the calculation amount of the algorithm is large along with the increase of the population and the iteration times, the solution is slow, and the terminal pose constraint of a planning target is not considered.

The optimal control method can plan the path under the condition of considering kinematics, dynamics and terminal pose constraint, but because the algorithm is sensitive to initial state input, when the initial position is more and smaller in space or the initial position is far away (for constant step length) or the obstacle condition in the path is complex, the path is difficult to converge and cannot be obtained or the path which does not accord with the actual condition is obtained when the multiple steering is involved.

Disclosure of Invention

Based on the above, it is necessary to provide an aircraft traction path planning method combining heuristic and optimal control, which can perform path planning under the condition of considering kinematics and terminal pose constraint, find the shortest path quickly, and can well converge to obtain global optimal when the obstacle environment is complex.

An aircraft traction path planning method combining heuristic and optimal control, comprising:

The method comprises the steps of obtaining a path planning task of the aircraft on a platform, wherein the path planning task comprises an initial position of the aircraft, a target position of the aircraft and an obstacle;

adopting an A-algorithm to plan a path for avoiding an obstacle from an initial position to a target position of an aircraft to obtain an initial path, wherein the initial path comprises a plurality of nodes;

when an obstacle exists in a preset range of a path between the first node and the second node, taking the second node as a key point, otherwise judging whether the obstacle exists in the preset range of the path between the first node and the third node, and judging whether the obstacle exists in the preset range of the path between the last node and the adjacent node until judging whether the obstacle exists in the preset range of the path between the last node and the adjacent node, so as to obtain a key point set;

establishing a kinematic model for the process of the airplane on the platform, and meeting barrier constraint, kinematic constraint and terminal pose constraint to obtain an optimal control model;

And selecting more than one key point from the key point set to perform geometric calculation to obtain a corresponding key point gesture, and obtaining an optimal path according to the key point gesture and the nonlinear programming model.

In one embodiment, a method is used to plan a path for an aircraft to avoid an obstacle from an initial position to a target position, and obtaining the initial path includes:

Adopting an A-algorithm, and obtaining the total cost of the current node according to the cost from the initial position to the current node, the estimated cost from the current node to the target position and the dynamic weighing operator;

and according to the minimum value of the total cost of all the nodes, avoiding the obstacle, and obtaining the initial path.

In one embodiment, using an a-x algorithm to plan a path for the aircraft to avoid an obstacle from an initial position to a target position, the obtaining an initial path further includes:

when an obstacle is positioned in the horizontal axial direction of the current node, the path of the current node does not comprise the vertical adjacent node of the obstacle;

when an obstacle is located in the vertical axial direction of the current node, the path of the current node does not include the horizontal adjacent node of the obstacle.

In one embodiment, the dynamic scaling factor is greater than or equal to one and gradually decreases from the initial stage to the final stage of the a-algorithm.

In one embodiment, the a-algorithm satisfies a barrier convex hull expansion constraint;

Connecting each maximum salient point of the airplane contour into a convex polygon to obtain a convex hull model;

Adopting a polygonal expansion algorithm to expand the convex hull model outwards for a certain safe buffer distance to obtain a convex hull transition model;

taking the top point of the convex hull model as a circle center and the safe buffer distance as a radius to obtain a plurality of round models tangent to the convex hull transition model;

and obtaining the expansion constraint of the obstacle convex hull according to the convex hull transition model and the round model.

In one embodiment, selecting more than one keypoint for geometric calculation, obtaining the corresponding keypoint pose includes:

Selecting more than one key point to obtain angular bisectors of two tracks taking the key point as an intersection point, taking a normal line of the angular bisectors as a speed direction of the airplane at the key point, and taking a maximum threshold speed as a speed of the airplane at the key point;

And obtaining more than one key point gesture according to the speed direction and the speed, wherein the key point gestures are in one-to-one correspondence with the key points.

In one embodiment, obtaining the optimal path according to the keypoint pose and the nonlinear programming model includes:

inputting the key point gesture into the nonlinear programming model, and solving the nonlinear programming model by adopting a solver to obtain connection points;

Obtaining a corresponding segmented path according to a key point and a connection point corresponding to the key point, and obtaining an optimal path according to all the segmented paths.

In one embodiment, the kinematic constraints and the terminal pose constraints include:

Speed relationship constraints between the aircraft and the tractor, steering angle and speed constraints of the aircraft, and control variable constraints of the tractor.

In one embodiment, the transformation of the optimal control model uses a Radau pseudo-spectrum algorithm.

An aircraft towing path planning apparatus combining heuristics and optimal control, comprising:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a path planning task of an aircraft on a platform, and the path planning task comprises an initial position of the aircraft, a target position of the aircraft and an obstacle;

the system comprises an initial path establishing module, a target position setting module and a control module, wherein the initial path establishing module is used for planning a path for avoiding an obstacle from an initial position to the target position by adopting an A-type algorithm to obtain an initial path, and the initial path comprises a plurality of nodes;

When an obstacle exists in a preset range of a path between the first node and the second node, the second node is taken as a key point, otherwise, whether the obstacle exists in the preset range of the path between the first node and the third node is judged, and until whether the obstacle exists in the preset range of the path between the last node and the adjacent node is judged, the key point set is obtained;

the model building module is used for building a kinematic model for the process of the transfer of the aircraft on the platform, and meeting barrier constraint, kinematic constraint and terminal pose constraint to obtain an optimal control model;

the optimal path establishing module is used for selecting more than one key point in the key point set to carry out geometric solution to obtain a corresponding key point gesture, and obtaining an optimal path according to the key point gesture and the nonlinear programming model.

According to the aircraft traction path planning method combining heuristic and optimal control, in order to obtain a path which meets the constraints of kinematics and terminal pose and is short in distance and time consumption, and avoid the problem of initial value sensitivity of an optimal control method under a complex obstacle environment, a track re-search algorithm is designed in an algorithm A, an improved algorithm A is utilized to conduct preliminary path planning on the initial position and the target position of an aircraft, the shortest path for avoiding obstacles is obtained, a shortest path key point of the shortest path is solved in the path, a proper amount of key points are selected to solve the movement state of the key points, the key points are used as the middle point of final path planning, the initial position and the target position of the aircraft are planned in a segmented mode, road conditions are simplified through segmented planning, the problem of initial value sensitivity of the optimal control algorithm is effectively solved, and the problem that the optimal control algorithm cannot solve due to the complex obstacle condition, and the fact that the optimal control algorithm falls into an iterative dead zone is obtained.

Drawings

FIG. 1 is a flow diagram of an aircraft tow path planning method combining heuristics and optimal control in one embodiment;

FIG. 2 is a schematic diagram of platform functional area partitioning in one embodiment;

FIG. 3 is a schematic diagram of a path traversing a continuously sloped obstacle in one embodiment;

FIG. 4 is a schematic illustration of an aircraft nose shell model in one embodiment;

FIG. 5 is a schematic diagram of a polygon expansion algorithm in one embodiment;

FIG. 6 is a schematic diagram of a convex hull model expansion process in one embodiment, (a) polygonal expansion, (b) vertex circle, and (c) obstacle expansion model;

FIG. 7 is a schematic diagram of the kinematic relationship of a rodless traction system in one embodiment;

FIG. 8 is a schematic diagram of a kinematic solution of keypoints in one embodiment;

FIG. 9 is a diagram of dynamic weighting of pre-optimization search ranges (retrieved node portions of a graph) in one embodiment;

FIG. 10 is a diagram of dynamically measuring the search range after optimization (the portion of nodes retrieved in the diagram) in one embodiment;

FIG. 11 is a schematic diagram of a re-search pre-optimization trajectory (gray lines in the figure) in one embodiment;

FIG. 12 is a schematic diagram of a re-search optimized trajectory (black lines in the figure) in one embodiment;

FIG. 13 is a schematic diagram of an aircraft path plan based on a modified A algorithm in one embodiment;

FIG. 14 is a schematic diagram of a scenario 1 optimal control algorithm path planning in one embodiment;

FIG. 15 is a schematic diagram of scenario 1 in combination with heuristic and optimal control algorithm path planning in one embodiment;

FIG. 16 is a schematic diagram of a scene 1 optimal control algorithm control amount and an aircraft speed variation curve in one embodiment, (a) a tractor steering angle variation curve, (b) a tractor acceleration variation curve, and (c) a tractor speed variation curve;

FIG. 17 is a schematic diagram of a scene 1 combined with heuristic and optimal control algorithm control amounts and an aircraft speed variation curve according to an embodiment, (a) a tractor steering angle variation curve, (b) a tractor acceleration variation curve, and (c) a tractor speed variation curve;

FIG. 18 is a schematic diagram of a scenario 2 optimal control algorithm path planning in one embodiment;

FIG. 19 is a schematic diagram of path planning for scenario 2 in combination with heuristic and optimal control algorithms in one embodiment;

FIG. 20 is a schematic diagram of a scene 2 optimal control algorithm control amount and an aircraft speed variation curve in one embodiment, (a) a tractor steering angle variation curve, (b) a tractor acceleration variation curve, and (c) a tractor speed variation curve;

FIG. 21 is a schematic diagram of a scene 2 combined with heuristic and optimal control algorithm control and aircraft speed variation curves in one embodiment, (a) tractor steering angle variation curve, (b) tractor acceleration variation curve, and (c) tractor speed variation curve;

FIG. 22 is a schematic diagram of a scenario 3 in combination with heuristic and optimal control algorithm path planning in one embodiment;

FIG. 23 is a schematic diagram of a scene 3 combined with heuristic and optimal control algorithm control and aircraft speed variation curves in one embodiment, (a) tractor steering angle variation curve, (b) tractor acceleration variation curve, and (c) tractor degree variation curve;

FIG. 24 is a block diagram of an apparatus in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

As shown in fig. 1, the method for planning an aircraft traction path according to the present application, which combines heuristic and optimal control, in one embodiment includes the following steps:

Step 102, obtaining a path planning task of the aircraft on the platform, wherein the path planning task comprises an initial position of the aircraft, a target position of the aircraft and an obstacle.

And 104, planning a path for avoiding the obstacle from the initial position to the target position by adopting an A algorithm to obtain an initial path, wherein the initial path comprises a plurality of nodes.

Specifically, an A-algorithm is adopted, the total cost of the current node is obtained according to the cost from the initial position to the current node, the estimated cost from the current node to the target position and the dynamic weighing operator, and the initial path is obtained according to the minimum value of the total cost of all the nodes. The initial path includes a plurality of nodes.

The dynamic weighing operator is greater than or equal to one and gradually decreases from the initial stage to the final stage of the algorithm.

When the obstacle is positioned in the vertical axial direction of the current node, the path of the current node does not comprise the vertical adjacent node of the obstacle, and when the obstacle is positioned in the horizontal axial direction of the current node, the path of the current node does not comprise the horizontal adjacent node of the obstacle.

The method comprises the steps of connecting each maximum salient point of an airplane contour into a convex polygon to obtain a convex hull model, expanding the convex hull model outwards by a certain safe buffer distance by adopting a polygon expansion algorithm to obtain a convex hull transition model, taking the vertex of the convex hull model as the center of a circle and the safe buffer distance as the radius to obtain a plurality of round models tangent to the convex hull transition model, and obtaining the obstacle convex hull expansion constraint according to the convex hull transition model and the round models.

And 106, searching each node in the initial path again from the first node, taking the second node as a key point when an obstacle exists in a preset range of the path between the first node and the second node, otherwise judging whether the obstacle exists in the preset range of the path between the first node and the third node, and obtaining a key point set until judging whether the obstacle exists in the preset range of the path between the last node and the adjacent node.

And step 108, establishing a kinematic model for the process of the plane on the platform, meeting barrier constraint, kinematic constraint and terminal pose constraint to obtain an optimal control model, and converting the optimal control model to obtain a nonlinear programming model.

The kinematic constraint and the terminal pose constraint comprise a speed relation constraint between an airplane and a tractor, a steering angle and speed constraint of the airplane and a control variable constraint of the tractor.

The optimal control model is converted by adopting a Radau pseudo-spectrum algorithm, and other algorithms in the prior art can be adopted.

And 110, selecting more than one key point from the key point set to perform geometric calculation to obtain a corresponding key point gesture, and obtaining an optimal path according to the key point gesture and the nonlinear programming model.

Specifically, more than one key point is selected to obtain angular bisectors of two tracks taking the key points as intersection points, the normal line of the angular bisectors is taken as the speed direction of the airplane at the key points, the maximum threshold speed is taken as the speed of the airplane at the key points, and more than one key point gesture is obtained according to the speed direction and the speed, wherein the key point gestures are in one-to-one correspondence with the key points.

Inputting the key point gestures into the nonlinear programming model, solving the nonlinear programming model by adopting a solver to obtain connection points, wherein the connection points are in one-to-one correspondence with the key points, obtaining corresponding segmented paths according to one key point and the connection points corresponding to the key points, and obtaining an optimal path according to all the segmented paths.

It should be noted that, the maximum threshold speed may be preset according to the safety requirement, and may be specifically set by experience or the prior art.

The method comprises the steps of firstly establishing a convex hull obstacle expansion model aiming at a complex platform arrangement environment, secondly introducing a dynamic weighing factor into an A-type algorithm, designing a track re-search algorithm, carrying out preliminary path planning on a starting position and a target position of an airplane by using the improved A-type algorithm to obtain a shortest path for avoiding obstacles, solving a shortest path key point in the path, selecting a proper amount of key point to solve a key point motion state, taking the key point as a middle point of pseudo-spectrum path planning, carrying out sectional planning and integration on the starting position and the target position of the airplane by combining an optimal control algorithm, simplifying road conditions by sectional planning, effectively solving the problem of initial value sensitivity of the optimal control algorithm aiming at the complex obstacle environment, and solving the problem that the optimal control algorithm cannot solve due to complex obstacle conditions, and obtaining the shortest path which meets the constraints of kinematics and terminal pose and is short in distance.

It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.

The aircraft traction and transportation is a key link of the whole-period running and recycling operation of the fleet, in order to improve the aircraft transportation efficiency and aim at obtaining an optimal path meeting the constraints of kinematics and terminal pose, in a specific embodiment, the aircraft traction path planning research is performed by combining a heuristic method with an optimal control method.

The airplane take-off and landing guarantee operation is usually performed according to a mode of the cyclic operation of the wave division times, and can be particularly divided into a wave division operation mode and a continuous operation mode. In the wave division operation mode, each wave is formed into a fleet according to the maximum available capacity to execute the cyclic operation recovery operation, and in the continuous operation mode, different fleets are divided, the cyclic operation recovery operation is respectively executed, and the respective flight periods are staggered and overlapped from head to tail, but only one fleet can be accommodated by a platform in the same period.

The platform guarantee operation mainly comprises two types of processes, namely a direct running process of the first-wave aircraft running and a secondary running process when the recovered aircraft runs again to execute tasks, but no matter which guarantee process is needed, the operation processes of filling, hanging and the like of the aircraft, warming self-checking and the like are all necessary.

As shown in fig. 2, since the narrow space platform is divided into different functional areas to perform corresponding safeguard tasks, different areas are required to perform corresponding work in the safeguard process of the aircraft, and thus the aircraft needs to be transported. In the direct running process, the mission aircraft parked in the hangar is transported to the platform support stand by the tractor and the aircraft lifter to execute the filling and hanging and other service support. After the clusters are recovered to the platform, part of fault or fixed check airplanes are transported to a hangar for maintenance, and the airplanes needing to execute the task of restarting are subjected to restarting guarantee, so that the airplanes are transported for multiple times between different working areas of the platform in the whole process.

Therefore, in a narrow platform space, a path which has safety and high efficiency from an initial station to a target station and accords with working reality is found, and the method has important significance in ensuring the dispatching of the aircraft, executing various outgoing recovery guarantee operations and improving the dispatching efficiency of the fleet.

Step 102, obtaining a path planning task of the aircraft on the platform, wherein the path planning task comprises an initial position of the aircraft, a target position of the aircraft and an obstacle.

The algorithm A is a classical heuristic algorithm which is most effective in solving the shortest path in a static road network, and the shortest path is found by setting a specific heuristic function as a heuristic factor to induce the calculation direction of the algorithm. The algorithm is applied to the plane traction path planning, so that a shortest path is obtained for simplifying a problem model, and the following assumption is made that (1) the algorithm is regarded as particles in the plane dispatching process, (2) the motion and dynamics constraints such as steering angles in the plane dispatching process are not considered, and (3) the pose constraints of the plane dispatching process and the terminal are not considered.

Step 104 adopts an A-algorithm, obtains the total cost of the current node according to the cost from the initial position to the current node, the estimated cost from the current node to the target position and the dynamic weighing operator, and obtains the initial path according to the minimum total cost of all the nodes. The initial path includes a plurality of nodes.

The algorithm compares the current node information:

F(x_i,y_i)＝G(x_i,y_i)+H(x_i,y_i) (1)

Wherein F is the total cost of the current node, G is the cost from the starting point to the current node, H is the estimated cost from the current node to the target point, namely the heuristic function value, and x _i,y_i is the current node coordinate.

The nodes with the smallest total cost value are continuously selected to be connected into the shortest path, and the airplane can run along any direction instead of running along the longitudinal and transverse directions or the diagonal directions of the grid purely, so that the heuristic function selects Euclidean distance, which is also called straight line distance. The heuristic function is:

The method comprises the steps of estimating cost from a current node to a target point, namely, a heuristic function value, wherein H is the estimated cost from the current node to the target point, (x _i,y_i) is the current node coordinate, D is the unit node distance cost value, and (x _goal,y_goal) is the target node coordinate.

Because of limitations of the algorithm a, when searching for path planning, it is necessary to start searching from the starting point to spread all around under the guidance of the heuristic function, but this still results in searching for a large number of useless nodes (such as nodes in the opposite direction to the target node). In order to optimize the search efficiency of the a-algorithm and reduce redundant search, the a-algorithm is improved by adopting a dynamic measurement method, and the improved node information algorithm is as follows:

Wherein F is the total cost of the current node, G is the cost from the starting point to the current node, omega is the dynamic weighing operator of the current node, H is the estimated cost from the current node to the target point, namely the heuristic function value, and x _i,y_i is the coordinates of the current node.

According to the characteristics of different search stages of the algorithm, the dynamic weighing operator is adjusted, when the algorithm is in the initial stage, the main purpose of the algorithm is to quickly get close to the target, namely the heuristic function value is relatively important, and when the algorithm is in the final stage, the main purpose of the algorithm is to accurately find the shortest path, namely the real cost of the path is relatively important. With the search of the algorithm, dynamic weighing operators are continuously adjusted, so that the search efficiency of the algorithm is improved, the search depth of the algorithm is increased, the blind search of the algorithm can be effectively prevented from increasing the search quantity, and the quality of the optimal solution is ensured.

The dynamic weighing operator is greater than or equal to one and gradually decreases from the initial stage to the final stage of the algorithm.

The conventional a-algorithm generally does not consider oblique angle avoidance when performing path planning, that is, when an obstacle is located in the horizontal or vertical axial direction of the current node, the path can directly reach the adjacent node of the obstacle across the oblique angle of the obstacle, which results in that when the obstacle is a continuous oblique obstacle node, the algorithm cannot correctly identify the obstacle node, and the path planning is wrong, as shown in fig. 3.

The algorithm is improved according to the above problem, the improved algorithm flow is shown in table 1, when the obstacle node is located in the horizontal axial direction of the current node, the path of the current node does not include the vertical adjacent node of the obstacle, and when the obstacle node is located in the vertical axial direction of the current node, the path of the current node does not include the horizontal adjacent node of the obstacle (i.e. 18 rows in table 1, wherein 1-8 are eight adjacent nodes of the current node, and are numbered from left to right and from top to bottom in sequence).

When the obstacle is positioned in the vertical axial direction of the current node, the path of the current node does not comprise the vertical adjacent node of the obstacle, and when the obstacle is positioned in the horizontal axial direction of the current node, the path of the current node does not comprise the horizontal adjacent node of the obstacle.

Table 1 improved a algorithm flow

The a algorithm satisfies the barrier convex hull expansion constraint.

The obstacle of the aircraft in the platform transportation process is mainly other aircraft parked on the platform, the traditional aircraft solid model is a circumcircle model, however, the model greatly expands the space occupied by the aircraft, and no path can be caused in environments with high concentration such as a hangar platform and the like. In this regard, the approximation degree and the safety of the model are weighed, the convex hull model is built by combining the characteristics of the airplane body, and the maximum convex points of the airplane contour are connected into a convex polygon. The aircraft is in a wing collecting state when being parked as an obstacle, and the convex shell model is shown in fig. 4.

The shunting aircraft is regarded as particles, the influence of the shape and the gesture of the aircraft is ignored, so that when the obstacle avoidance is performed in path planning, a safe buffer distance is required to be added to the obstacle, the safe shunting of the actual aircraft on the path is ensured, and a polygonal expansion algorithm is adopted to expand the convex hull model outwards by a certain safe buffer distance according to the problem, as shown in fig. 5.

The calculation formula of the post-expansion vertex coordinates is as follows:

Wherein (x _Q,y_Q) is a Q point coordinate, (x _P,y_P) is a P point coordinate, d is an expansion distance, v ₁ is a direction vector of a straight line l ₁, v ₂ is a direction vector of a straight line l ₂, and Normalize (v ₁) and Normalize (v ₂) are unit vectors of v ₁ and v ₂.

The expanded convex hull model, namely the convex hull transition model, is shown in fig. 6 (a), a more serious stretching occurs at the sharp angle of the convex hull model after polygonal expansion, the stretching distance is far beyond the required safe buffering distance, the obstacle at the sharp angle is excessively expanded, the expansion distance is taken as the center of a circle, the circle shown in fig. 6 (b) is taken as the radius, a plurality of circular models tangential to the convex hull transition model are obtained, a proper amount of key points are selected on the circle to improve the original obstacle expansion model (convex hull transition model), and the improved obstacle expansion model, namely the obstacle convex hull expansion model, is shown in fig. 6 (c).

And the obstacle convex hull expansion constraint is satisfied when the obstacle convex hull expansion model is satisfied.

And when an obstacle exists in the preset range of the path between the first node and the second node, taking the second node as a key point, otherwise judging whether the obstacle exists in the preset range of the path between the first node and the third node, and judging whether the obstacle exists in the preset range of the path between the last node and the adjacent node until the key point set is obtained.

The improved algorithm A can quickly and accurately search the shortest path from the initial position to the target position of the aircraft under the condition of meeting the assumption of the algorithm A belongs to a unit decomposition method, the path is formed by continuously connecting adjacent nodes, so that the path has a large number of turns and is limited by the length of the unit, and when the distance between the target nodes is far, the change of the slope of a connecting line between the two separated nodes and the target node is small, and the change of an angle perpendicular to the connecting line direction is insufficient to identify the suboptimal path.

And (3) designing a re-search track optimization algorithm, optimizing the two problems by re-searching track nodes, sequentially selecting second nodes along the path by taking the initial node as a first node, judging whether barrier nodes exist within a certain distance of a connecting line of the two nodes, continuously selecting the next node along the path to serve as the second node for re-judgment if the barrier nodes do not exist, marking the current second node as a key point if the barrier nodes exist, sequentially selecting the second nodes along the path to continue searching by taking the current second node as the first node, and finally finishing searching.

Table 2 re-search track optimization algorithm flow

Wherein Walkable functions to connect two nodes and determine if there is an obstacle node within a certain distance of the connection. And (5) obtaining the key points of the original track through re-searching the track, and generating an optimal path.

Because the path generated by the algorithm A does not consider the kinematics and dynamics constraints such as steering angle in the dispatching process and the terminal pose constraints in the dispatching process of the aircraft, when actual problem study, such as the study of the problems of the dispatching of the aircraft, and the like, the factors have important influence on the operations such as obstacle avoidance, warehouse-out, warehouse-in and the like in the multi-aircraft dispatching process. In order to obtain an aircraft dispatching path meeting the constraints of kinematics, dynamics and aircraft terminal pose, the method comprises the steps of solving the motion state of key points on the basis of obtaining the key points of the path by using the A-type algorithm, carrying out sectional planning on the path by taking the key points as nodes and combining an optimal control model, and connecting all sections of paths to obtain a complete path.

Modeling analysis was performed using a rodless, towed aircraft dispatch system as an example.

And establishing a kinematic model for the process of transferring the aircraft on the platform. The aircraft deployment rodless towing system is similar to the trailer system and can therefore also be considered as one of the trailer systems. Because the movement speed is slow in the process of the airplane transportation, the surface of the platform in a narrow space is flat, the tires are not sliding in the process of movement, and because of the slow movement, the inertia force and the lateral force are negligible, and a simplified system model is shown in fig. 7.

In fig. 7, θ ₁ and θ ₂ respectively represent angles between axial directions and x-axes of the aircraft and the tractor, L ₁ and L ₂ are front-rear wheel tracks of the aircraft and front-rear wheel tracks of the tractor, (x ₁,y₁)、(x₂,y₂)、(x₃,y₃) respectively represent hinge points of the aircraft and the tractor and coordinate positions of the tractor, β ₁ and β ₂ respectively represent steering angles of the aircraft and the tractor, and M ₀ is a connecting line distance between the hinge point of the tractor and the rear wheel of the tractor.

According to the system structure and the motion relation, the kinematic equation is as follows:

Wherein ,X＝[x₁,y₁,θ₁,θ₂,v₂]^T,U＝[u₁,u₂]^T,u₁＝tanβ₂,u₂＝a₂ is the tractor acceleration and v ₂ is the tractor translation speed.

The kinematic model satisfies the barrier constraint.

Different from the A-algorithm obstacle model, the path calculation can be carried out only by realizing the analysis expression form of the obstacle avoidance in the optimal control algorithm, and for the convenience of calculation, the airplane model adopts a traditional characteristic circle model, and collision judgment is carried out by utilizing the Euclidean distance between the circle center and the obstacle and the magnitude concern of the safety distance, wherein the judgment formula is as shown in the formula (6):

Wherein n is the total number of the obstacles, x _oi and y _oi are the central position coordinates of the ith obstacle, a _i and b _i are the lengths of the obstacles in the transverse axis direction and half of the lengths of the obstacles in the longitudinal axis direction, d is the safe buffer distance, and p is the shape parameter. When 2p=1, the graph described by the formula is diamond, when 2p=2, the graph described is circle or ellipse, and when p→infinity, the graph described is rectangle.

The kinematic model also meets kinematic constraints and terminal pose constraints. The kinematic constraints and the terminal pose constraints comprise speed relation constraints between the airplane and the tractor, steering angle and speed constraints of the airplane and control variable constraints of the tractor.

Depending on the structural features of the towing system used on the platform, the speed relationship between the translational speed of the aircraft and the towing vehicle can be expressed as:

Among them, the steering angle and speed of the aircraft should satisfy:

furthermore, the control variables of the tractor should also satisfy the corresponding constraint relationships, namely:

The upper and lower limits of the translational speed and the translational acceleration are set according to the specific model of the aircraft and the rodless traction equipment and the related safety standard requirements, and the steering angle is generally calculated according to the turning radius of the aircraft and the tractor, and the specific calculation mode is as follows:

since the constraints of the obstacle constraint and the translational speed, the control variable and the like are all inequality constraints, the two parts can be considered together to form a unified inequality constraint relation, and then all inequality constraints can be uniformly expressed as:

h≤0 (11)

Wherein, the formula (11) can be composed of the formula (6), the formula (8) and the formula (9).

The optimal control model for rodless traction system path planning can be described as equation (12).

Wherein: For the system described by equation (5), t _f is the time to end, t ₀ is the time to start, wk is the weight adjustment factor, J is the objective function, and R is the weight matrix.

After the kinematic model meets the constraint, an optimal control model is obtained; and converting the optimal control model to obtain a nonlinear programming model.

The transformation of the optimal control model may be performed by using various algorithms in the prior art, for example, using a Radau pseudo-spectrum algorithm, and the solving steps are as follows.

(1) Parameter initialization

And firstly, carrying out assignment initialization on each parameter in the optimal control model.

(2) Time interval discretization

The method comprises the steps of solving by utilizing a Radau pseudo-spectrum method, firstly dividing a time interval [ t ₀,t_f ] to be solved into K subintervals [ t _k-1,t_k],t₀＜t₁＜…＜t_k＜…＜t_K＝t_f, k=1, 2, ], wherein K is conveniently represented, and the subsequent (^(k)) represents the relevant parameters in the kth interval, so that the following (K) is to be solvedThe following transformation is used to transform the interval [ -1,1 ]:

for discretization solution, interpolation transformation is needed to be carried out on state variables, control variables, constraint functions and optimization objective functions, the matching point of the Radau pseudo-spectrum method is Legendre Gauss Radau (LGR) point, and the N-order LGR point is a solution of a polynomial equation P _N(T)+P_N-1 (T) =0, wherein P _N (T) represents a Legendre polynomial of the N-order:

(3) Subinterval variable approximation

① T _i ^(k),i＝1,2,…,N^(k) for the first N ^(k) LGR ligand points for the state variables, respectivelyUsing Lagrange's interpolation polynomial of N ^(k) th orderAs a basis function, interpolating approximations of state variables can be obtained:

where k=1, 2,..k, X ^(k)(T_i ^(k)) is the value of X ^(k) at node T _i ^(k), Interpolation basis functions for Lagrange, which satisfy:

② For the control variables, similar to the state variables, for the first K-1 intervals:

Where k=1, 2, K-1;

For the Kth interval, an N ^(k) -1 Lagrange interpolation polynomial is utilized As a basis function, interpolation approximation of the control variable can be obtained:

Wherein:

③ For constraint functions. First deriving the state variables:

in the opposite type (20) Discretizing the positions to obtain:

In the formula, Wherein, the matrix is a Radau pseudo-spectrum differential matrix, and the order is N ^(k)×(N^(k) +1).

Substituting equation (21) into equation (12) may convert the differential constraint equation to:

Path constraint discretization:

Where i=1, 2,.. ^(k)

Performance index:

The state variables of adjacent subintervals connecting endpoints are equal, and the following conditions are satisfied:

And selecting more than one key point from the key point set to perform geometric calculation to obtain a corresponding key point gesture, and obtaining an optimal path according to the key point gesture and the nonlinear programming model.

The selection of the key points is preferable to the smooth track section, and the key points with larger change of the course angle of the airplane are not preferable to be selected, otherwise, larger turning track is generated, and the planned path deviates from the shortest path.

The geometrical calculation is that more than one key point is selected to obtain angular bisectors of two tracks taking the key points as intersection points, a normal line of the angular bisectors is taken as a speed direction of the airplane at the key points, the maximum threshold speed is taken as the speed of the airplane at the key points, and more than one key point gesture is obtained according to the speed direction and the speed, and the key point gestures are in one-to-one correspondence with the key points.

The method comprises the steps of establishing an A-trajectory key point kinematic solution model, and assuming that the speed is moderate and kept unchanged in the motion process of the airplane, namely tangential acceleration is 0, and enabling the motion direction of the tractor to be consistent with the motion direction of the airplane, namely theta ₁＝θ₂. The normal line of the angular bisectors of the two intersecting tracks is taken as the aircraft speed direction, as shown in fig. 8:

In fig. 8, a broken line ABC is an a-algorithm planned path, where B is a key point, n is an angular bisector of ++abc, and l is a normal line of n, and if the aircraft moves along ABC in sequence, its speed direction is shown as v in fig. 8.

Inputting the key points serving as intermediate points into the nonlinear programming model, carrying out sectional programming on the paths of the aircraft by utilizing an optimal control algorithm, solving the nonlinear programming model by adopting a solver (such as SNOPT software packages and the like) to obtain connection points, wherein the connection points are in one-to-one correspondence with the key points, obtaining corresponding sectional paths according to one key point and the connection points corresponding to the key points, and merging all the sectional paths to obtain the optimal paths. The optimal path is the shortest path that satisfies the constraint condition.

In order to intuitively show the path planning effect of the improved A algorithm, a narrow space platform layout is selected as an example, a certain aircraft position in a parking area with higher aircraft density and a certain temporary aircraft position at a far distance are selected as examples, the algorithm before and after the dynamic measurement and the track re-search optimization are adopted to plan the aircraft dispatching path between the two aircraft positions in the full-scale state, and the superiority of the improved A algorithm is shown through transverse comparison.

Fig. 9 and fig. 10 are schematic diagrams showing the search range of the algorithm before and after the dynamic measurement optimization, in which the searched node area is CLOSELIST sets in the algorithm, and the smaller the sets are, the higher the search efficiency of the representative algorithm is. Before dynamic measurement optimization, the algorithm needs to search a large number of areas to carry out path planning, the efficiency is low, and after the dynamic measurement optimization is added, redundant search nodes are not generated except the necessary paths and the obstacle detection needs, so that the optimization effect is obvious, and the algorithm efficiency is greatly improved.

Fig. 11 and fig. 12 are schematic diagrams showing path planning before and after the heavy search optimization, the area a in fig. 11 is the problem of excessive turning of the track mentioned above, the area B in fig. 11 is the problem of limited unit length mentioned above, and when the distance between the target nodes is far, the change of the slope of the connecting line between the two nodes and the target node is small, and the change of the angle perpendicular to the connecting line direction is insufficient to identify the suboptimal path. After the optimization of the re-search algorithm, the track is shown as a black path in fig. 12, the area A broken line part and the area B suboptimal path are both optimized, and the algorithm successfully generates the shortest path.

The final full path planning effect is shown in fig. 13.

Taking a certain airplane as an example, the airplane is used for establishing a kinematic model, carrying out an optimal control method and a multi-scene path planning simulation experiment combining heuristic and optimal control algorithms, and analyzing an experiment result.

The simulation experiment selects a single key point, a narrow space platform is in a full-plaited state, the aircraft is a fixed-wing aircraft with larger volume, each scene simulation experiment only changes the initial aircraft stand, the target aircraft stand and the arrangement condition of the obstacle aircraft, various parameters and constraint conditions of the aircraft and the tractor are kept unchanged, and the values of the parameters are p＝3、L₁＝5.88m、L₂＝2.4m、β_1max＝π/4、-1m/s≤v₂≤1m/s、-1m/s²≤u₂≤1m/s²、-π≤θ₁≤π、-π≤θ₂≤π, in (6), because the distance between the tractor hinging point of the actual tractor and the center of the rear wheel of the tractor is extremely small, M ₀＝0、-1≤u₁ is less than or equal to 1, namely-pi/4 is less than or equal to beta ₂ is less than or equal to pi/4.

TABLE 3 comparison of simulation experiment results for scenario 1

Fig. 14 is a path planning track diagram of an aircraft scheduling path by an optimal control algorithm, fig. 15 is a path planning track diagram combining a heuristic algorithm and an optimal control algorithm, and comparing the two path planning track diagrams, it can be found that both algorithms can effectively realize obstacle avoidance, but the track diagram obtained by the algorithm is smoother than the track obtained by the pure optimal control algorithm, and for further analysis, the control quantity u= [ U ₁,u₂]^T and the change curve of the aircraft movement speed with time are analyzed, as shown in fig. 16 and 17.

In the two figures, (a) is a control quantity u ₁, namely a change curve of a steering angle of the tractor, and can be obtained from the figures, the algorithm obviously reduces the fluctuation range and the frequency of the control quantity u ₁, wherein the reduction value on the fluctuation range can reach 45%, which indicates that the improved algorithm enables the process of searching targets to be more stable, the searching efficiency is higher, the change trend of the control quantity is consistent with the turning condition of the aircraft, namely u ₁ >0 aircraft turns left, and u ₁ <0 aircraft turns right, and the control effect is good.

In the two diagrams, (b) is a control quantity u2, namely a change curve of the acceleration of the tractor, the stability of the result obtained by the algorithm is obviously superior to that obtained by simply using the optimal control algorithm, the speed of the aircraft is kept unchanged in the middle process of the movement only in the acceleration of the initial stage and in the deceleration of the final stage, the aircraft runs stably, and the reversing phenomenon of the aircraft occurs in the movement process due to the fluctuation of the acceleration better than that obtained by the optimal control algorithm in the two diagrams (c), which is caused by the fact that the algorithm is difficult to converge due to the fact that the planned path is too long or the obstacle condition is complex, the effect obtained by the algorithm is extremely remarkable in the aspect, and the problem of error planning caused by the fact that the target distance or the obstacle condition is complex is effectively avoided.

Table 4 comparison of simulation experiment results for scenario 2

As shown in fig. 18 to 21, the simulation results of the two algorithms of the scene 2 are compared, and the differences of the four types of data in the table 4 are not large, but comparing fig. 18 and 19 can find that the space occupation of the path track planned by the algorithm is far smaller than that of the optimal control algorithm, the shortest path is more attached, the dispatching process is concentrated at the lower half part of the platform, and the upper half part is basically unoccupied. In such narrow space, the improvement of the space utilization rate has important significance for the dispatching of the aircraft clusters, the algorithm ensures the space utilization rate of the platform to the greatest extent, and a sufficient safety channel is reserved for the dispatching of other aircraft, so that the algorithm has more practical significance.

For complex obstacle situations, as shown in the 3 obstacle situations of the scene of fig. 22, the optimal control algorithm cannot converge to obtain an optimal path due to the fact that the target distance is too far and the obstacle situation is complex, and an optimal solution cannot be found, but the algorithm can still avoid complex obstacles to reach the target airplane position, and plan the optimal path, and the change curves of (a) and (b) and (c) in fig. 23 can show that the movement state of the airplane is gentle and stable, the change trend of the control quantity is consistent with the turning situation of the airplane, and the control effect is good.

Compared with the traditional optimal control algorithm, the three-scene simulation experiment proves that the algorithm has the advantages that the initial value sensitivity problem of the optimal control algorithm in a complex obstacle environment is effectively solved by ①, the paths are enabled to be more attached to the shortest paths by ②, the time required by the airplane transportation is effectively shortened, the space limitation of the platform is considered, enough safety channels are reserved for the platform, fluctuation of the airplane transportation track is reduced by ③, the track is smoother, and the stability of the airplane transportation is enhanced.

The method is characterized in that a convex hull obstacle expansion model is established aiming at a complex arrangement environment, dynamic weighing factors are introduced into an A-scale algorithm, a track re-search algorithm is designed, shortest path key points are solved, then the movement state of the key points is solved, and the optimal control algorithm is combined to plan and integrate segmented paths among the key points. And a path planning simulation experiment under a typical platform environment is also developed, and the superiority of the method is verified by comparing with the result of the optimal control algorithm simulation experiment. The method improves the A-algorithm, combines the optimal control algorithm, establishes the shortest path through improving the A-algorithm, selects key points, carries out key point segmentation processing on the long path, carries out path planning on each segment by utilizing the optimal control algorithm, ensures that the path is the shortest path, fully considers the kinematics and terminal pose constraint in the aircraft dispatching process, avoids the problem of initial value sensitivity in complex obstacle environments, and effectively improves the optimization performance of the aircraft dispatching.

In one embodiment, as shown in FIG. 24, an aircraft towing path planning apparatus combining heuristic and optimal control is provided, which includes an acquisition module 2402, an initial path establishment module 2404, a set of key points establishment module 2406, a model establishment module 2408, and an optimal path establishment module 2410, wherein:

the acquisition module 2402 is used for acquiring a path planning task of the aircraft on the platform, wherein the path planning task comprises an initial position of the aircraft, a target position of the aircraft and an obstacle;

an initial path establishing module 2404, configured to perform path planning for avoiding an obstacle from an initial position to a target position by adopting an a-x algorithm, to obtain an initial path, where the initial path includes a plurality of nodes;

When an obstacle exists in a preset range of a path between the first node and the second node, taking the second node as a key point, otherwise judging whether the obstacle exists in the preset range of the path between the first node and the third node, and until judging whether the obstacle exists in the preset range of the path between the last node and the adjacent node, obtaining a key point set;

the model building module 2408 is used for building a kinematic model for the process of the plane on the platform, meeting barrier constraint, kinematic constraint and terminal pose constraint to obtain an optimal control model;

The optimal path establishing module 2410 is configured to select more than one key point from the set of key points to perform geometric solution to obtain a corresponding key point gesture, and obtain an optimal path according to the key point gesture and the nonlinear programming model.

For specific limitations on the aircraft towing path planning apparatus combining heuristic and optimal control, reference may be made to the above limitation on the aircraft towing path planning method combining heuristic and optimal control, and no further description is given here. Each of the modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (10)

1. An aircraft towing path planning method combining heuristics and optimal control, characterized by comprising: Obtaining a path planning task for transporting an aircraft on a platform; the path planning task includes: an initial position of the aircraft, a target position of the aircraft, and obstacles; Using the A* algorithm, a path is planned for the aircraft from an initial position to a target position to avoid obstacles, and an initial path is obtained, wherein the initial path includes a plurality of nodes; Starting from the first node, each node in the initial path is re-searched; when there is an obstacle within the preset range of the path between the first node and the second node, the second node is used as the key point, otherwise, whether there is an obstacle within the preset range of the path between the first node and the third node is determined, until whether there is an obstacle within the preset range of the path between the last node and the adjacent node is determined, and a key point set is obtained; A kinematic model is established for the process of aircraft transportation on the platform, and obstacle constraints, kinematic constraints and terminal posture constraints are satisfied to obtain an optimal control model; the optimal control model is transformed to obtain a nonlinear programming model; Select one or more key points in the key point set to perform geometric calculations to obtain corresponding key point postures; obtain an optimal path based on the key point postures and the nonlinear programming model; The optimal control model for the path planning of the towbarless traction system can be described as: ; Where: To reach the end moment, It's time to depart. is the weight adjustment factor, is the objective function, is the weight matrix, , , , represents the steering angle of the tractor, is the tractor acceleration; Convert the differential constraint equation to: ; Path constraint discretization: ; In the formula, Indicates obstacles, ; Performance indicators: ; The state variables of the endpoints of adjacent subintervals are equal, satisfying: . 2. The method according to claim 1, characterized in that the A* algorithm is used to plan a path for the aircraft from an initial position to a target position to avoid obstacles, and obtaining the initial path includes: The A* algorithm is used to obtain the total cost of the current node based on the cost from the initial position to the current node, the estimated cost from the current node to the target position, and the dynamic measurement operator; According to the minimum total cost of all nodes, obstacles are avoided and the initial path is obtained. 3. The method according to claim 2, characterized in that the A* algorithm is used to plan a path for the aircraft from the initial position to the target position to avoid obstacles, and obtaining the initial path further comprises: When the obstacle is located on the horizontal axis of the current node, the path of the current node does not include the vertical adjacent nodes of the obstacle; When the obstacle is located on the vertical axis of the current node, the path of the current node does not include the horizontally adjacent nodes of the obstacle. 4. The method according to claim 3 is characterized in that the dynamic weight operator is greater than or equal to one and gradually decreases from the initial stage to the final stage of the A* algorithm. 5. The method according to any one of claims 1 to 4, characterized in that the A* algorithm satisfies the obstacle convex hull expansion constraint; The obstacle convex hull expansion constraint includes: connecting the maximum convex points of the aircraft contour into a convex polygon to obtain a convex hull model; A polygon expansion algorithm is used to expand the convex hull model outward by a certain safety buffer distance to obtain a convex hull transition model; Taking the vertex of the convex shell model as the center of the circle and the safety buffer distance as the radius, a plurality of circular models tangent to the convex shell transition model are obtained; According to the convex hull transition model and the circle model, an obstacle convex hull expansion constraint is obtained. 6. The method according to any one of claims 1 to 4, characterized in that selecting one or more key points for geometric solution to obtain the corresponding key point postures comprises: Select one or more key points, obtain the angle bisector of two trajectories with the key point as the intersection point, take the normal line of the angle bisector as the speed direction of the aircraft at the key point, and take the maximum threshold speed as the speed magnitude of the aircraft at the key point; According to the velocity direction and the velocity magnitude, one or more key point postures are obtained, and the key point postures correspond to the key points one by one. 7. The method according to any one of claims 1 to 4, characterized in that obtaining the optimal path according to the key point posture and the nonlinear programming model comprises: Inputting the key point posture into the nonlinear programming model, using a solver to solve the nonlinear programming model to obtain connection points; the connection points correspond to the key points one by one; According to a key point and the connection points corresponding to the key point, a corresponding segmented path is obtained; according to all the segmented paths, an optimal path is obtained. 8. The method according to any one of claims 1 to 4, characterized in that the kinematic constraints and terminal posture constraints include: The speed relationship constraints between the aircraft and the tractor, the steering angle and speed constraints of the aircraft, and the control variable constraints of the tractor. 9. The method according to any one of claims 1 to 4, characterized in that the Radau pseudospectral algorithm is used to transform the optimal control model. 10. An aircraft towing path planning device combining heuristic and optimal control, characterized in that it includes: An acquisition module is used to acquire a path planning task for the aircraft to be transported on the platform; the path planning task includes: an initial position of the aircraft, a target position of the aircraft, and obstacles; An initial path establishment module is used to use the A* algorithm to plan a path from an initial position to a target position of the aircraft to avoid obstacles, and obtain an initial path, wherein the initial path includes a plurality of nodes; A key point set establishment module is used to re-search each node in the initial path starting from the first node; when there is an obstacle within a preset range of the path between the first node and the second node, the second node is used as a key point; otherwise, it is determined whether there is an obstacle within a preset range of the path between the first node and the third node, until it is determined whether there is an obstacle within the preset range of the path between the last node and the adjacent node, thereby obtaining a key point set; A model building module is used to establish a kinematic model for the process of aircraft transportation on the platform, and to satisfy obstacle constraints, kinematic constraints and terminal posture constraints to obtain an optimal control model; the optimal control model is transformed to obtain a nonlinear programming model; An optimal path establishment module is used to select one or more key points in the key point set to perform geometric calculations to obtain corresponding key point postures; and obtain an optimal path based on the key point postures and the nonlinear programming model; The optimal control model for the path planning of the towbarless traction system can be described as: ; Where: To reach the end moment, It's time to depart. is the weight adjustment factor, is the objective function, is the weight matrix, , , , represents the steering angle of the tractor, is the tractor acceleration; Convert the differential constraint equation to: ; Path constraint discretization: ; In the formula, Indicates obstacles, ; Performance indicators: ; The state variables of the endpoints of adjacent subintervals are equal, satisfying: .