CN119742904A - Collaborative optimization method and system for obstacle avoidance navigation and energy scheduling of hybrid energy supply unmanned system - Google Patents
Collaborative optimization method and system for obstacle avoidance navigation and energy scheduling of hybrid energy supply unmanned system Download PDFInfo
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Abstract
本发明公开了一种混合供能无人系统的多尺度避障导航与能量调度荷源协同优化方法及系统，属于混合供能无人系统调度优化领域。首先根据大尺度探测数据重构环境搜索最优路径，并在运动过程中根据动态障碍轨迹实时进行动态避障导航。无人系统根据高精度环境数据进行障碍物检测以实时实现动态避障，当检测到碰撞风险时，采用改进动态窗口法对最优路径进行局部调整从而避开障碍物。随后根据能量存储状况反馈综合最优运动策略，最终实现荷源双向在线能量调度。系统通过预测可行负荷需求区间并以等效氢耗总量最小为目标建立目标函数，求解各供能单元在各预测时刻的最优输出功率变化率。本发明能有效提升混合供能无人系统的续航能力，实现安全、高效运动。
The present invention discloses a method and system for collaborative optimization of multi-scale obstacle avoidance navigation and energy scheduling of load sources for a hybrid energy supply unmanned system, and belongs to the field of scheduling optimization of hybrid energy supply unmanned systems. First, the environment is reconstructed according to large-scale detection data to search for the optimal path, and dynamic obstacle avoidance navigation is performed in real time according to the dynamic obstacle trajectory during the movement. The unmanned system performs obstacle detection based on high-precision environmental data to achieve dynamic obstacle avoidance in real time. When the risk of collision is detected, the improved dynamic window method is used to make local adjustments to the optimal path to avoid obstacles. Subsequently, the optimal motion strategy is fed back based on the energy storage status, and finally bidirectional online energy scheduling of load sources is achieved. The system predicts the feasible load demand range and establishes an objective function with the goal of minimizing the total equivalent hydrogen consumption, and solves the optimal output power change rate of each energy supply unit at each predicted moment. The present invention can effectively improve the endurance of the hybrid energy supply unmanned system and achieve safe and efficient movement.
Description
Technical Field
The invention belongs to the field of scheduling optimization of hybrid energy supply unmanned systems, and particularly relates to a collaborative optimization method and a collaborative optimization system for obstacle avoidance navigation and energy scheduling of a hybrid energy supply unmanned system.
Background
With the global importance of all countries on high-altitude patrol, emergency rescue, ocean monitoring and ocean resource exploration and the continuous increase of military task reconnaissance and execution demands, the technology for replacing human beings to execute various dangerous tasks in complex environments such as underwater, air and the like is designed and researched, and the technology becomes the core focus of modern unmanned intelligent system research. Modern exploration and task execution equipment needs to have the capability of stably working for a long time under extreme environments so as to support the demands of fields such as scientific research, resource management, environmental protection, safety guarantee and the like. Therefore, the unmanned system with universal autonomous long-term endurance can be developed, so that the exposure of human beings in dangerous environments can be reduced, and the efficiency, accuracy and response capability of tasks can be greatly improved. The solid oxide fuel cell (Solid Oxide Fuel Cell, SOFC) is a quiet and efficient power generation technology, is provided with auxiliary energy sources such as lithium batteries and the like, can form a hybrid power system with strong endurance capacity and can be rapidly adapted to load requirements, is very suitable for the application characteristics of multiple scenes under complex working conditions, and can be widely applied to task scenes such as underwater unmanned underwater vehicles, high-altitude unmanned vehicles and the like. Unlike urban road environment, unstructured complex dynamic environment is changeable and map precision is low, and autonomous unmanned system is difficult to perform high-precision autonomous navigation and operation like vehicles using high-precision map and structured road scene.
In order to solve the problems, the energy management strategy becomes a powerful tool for intensive research, so that the energy consumption can be effectively reduced, and the operation efficiency of the system is improved. However, considering only the energy management strategy is not yet sufficient to fully enhance the overall performance of the fuel cell hybrid autonomous unmanned system. Multiple layers of collaborative optimization are performed by comprehensively considering factors such as environment perception, track planning, energy management and the like, so that higher task success rate and longer endurance time can be realized in a complex natural dynamic environment. The multi-dimensional comprehensive optimization method not only can improve the energy utilization efficiency, but also can enhance the autonomous decision making capability and the environment adaptability of the system, thereby comprehensively improving the overall performance of the unmanned system.
For a fuel cell hybrid unmanned system, the existing endurance optimization method is mainly divided into two types of internal energy management and external track planning. The internal energy management method focuses on optimizing energy distribution among power sources of the fuel cell hybrid power system so as to improve energy efficiency of the system and prolong endurance time. Although the methods can optimize energy utilization to a certain extent, the dynamic navigation requirements of the autonomous unmanned system in a complex environment are often ignored, and the real-time response capability to external environment changes is lacking, so that the problems of insufficient energy utilization and limited endurance time exist in practical application. On the other hand, the external track planning method mainly focuses on how to plan an optimal path in a complex environment to avoid collision with obstacles and improve navigation efficiency. The method can provide an ideal autonomous navigation scheme in a static or quasi-static environment through a path searching and optimizing algorithm. However, they generally disregard the actual energy status of the unmanned system and the characteristics of the power system, neglecting the importance of internal energy management, which may lead to situations in which energy distribution is not reasonable during long-term tasks, and the task execution is interrupted due to energy exhaustion.
Generally, how to improve the navigation accuracy, operation safety and cruising ability of an unmanned system in a complex environment at the same time is a problem to be solved urgently.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides a collaborative optimization method and a collaborative optimization system for obstacle avoidance navigation and energy scheduling of a hybrid energy supply unmanned system, and aims to improve the cruising ability of the hybrid energy supply unmanned system and realize safe and efficient navigation through multi-level system optimization of environment awareness, track planning and energy management.
In order to achieve the above object, according to one aspect of the present invention, there is provided a load source collaborative three-layer sub-optimization method of a hybrid energy unmanned system, including:
The unmanned system sails along the optimal path, the sailing process is divided into a plurality of prediction time domains, and the dynamic obstacle avoidance and the charge source bidirectional cooperative energy scheduling are sequentially executed in each prediction time domain;
the dynamic obstacle avoidance comprises the steps of carrying out obstacle detection according to environmental data acquired by a high-precision sensor in real time, and carrying out local adjustment on an optimal path if a collision area of a certain obstacle is detected to be intersected with the optimal path so as to avoid the collision area of each obstacle;
The energy scheduling comprises the following steps:
s1, predicting a speed interval of the unmanned system at each prediction time in a current prediction time domain according to a current optimal path, mapping the speed interval into a feasible load demand interval at the corresponding prediction time, and determining a feasible energy scheduling interval corresponding to each feasible load demand interval;
S2, establishing an objective function by taking the minimum equivalent hydrogen consumption total amount of the hybrid energy supply system in the current prediction time domain as a target, solving and obtaining the output power change rate of each energy supply unit in the hybrid energy supply system at each prediction time based on a feasible load demand interval and a corresponding feasible energy scheduling interval, and obtaining an optimal control variable sequence;
(S3) controlling the output power of each energy supply unit in the hybrid energy supply system at each prediction moment according to the optimal control variable sequence;
The large-scale sensor and the high-precision sensor are both mounted on the unmanned system, the large-scale sensor has a wider searching range, the high-precision sensor has higher detection precision, and the collision area is an area formed by a position which is smaller than a preset safety distance from an obstacle.
Further, the local adjustment of the optimal path to avoid the collision area of each obstacle includes:
(T1) when there is a possibility of collision while continuing to navigate along the optimal path, taking the current time as the current sampling time;
and (T2) sampling in a forward speed sampling space of the unmanned system at the current sampling moment to obtain a plurality of groups of candidate speeds, wherein the forward speed sampling space is as follows:
(T3) calculating the position of the unmanned system at the next sampling moment according to the position of the unmanned system at the current sampling moment and the candidate speed sampling result, and eliminating the position in the collision area;
(T4) forming the current sampling time and the positions of the previous sampling time into a current local path, and selecting an optimal local path from the current local path;
(T5) if the real-time track adjustment of the dynamic obstacle is completed, turning to a step (T6), otherwise, turning to a step (T2);
(T5) adjusting the optimal path using the current optimal local path;
where (v, ω) represents a set of candidate speeds, v represents the speed of the unmanned system, ω represents the angular speed of the unmanned system, Δt represents the sampling time interval, a max represents the maximum acceleration, Indicating the maximum angular acceleration.
Further, step (T4) selects an optimal local path from the current local paths based on the evaluation function;
The evaluation function is:
The method comprises the steps of setting a speed evaluation function, wherein, the Heading (v, omega) is used for evaluating an angle difference between the direction of the tail end of a track and a target point of an unmanned system at a current set speed, the Dist (v, omega) is used for evaluating a collision distance between the unmanned system and an obstacle, the speed evaluation function is used for evaluating the navigation speed of the unmanned system in a constraint range, and alpha, beta and gamma are coefficients of the Heading evaluation function Heading (v, omega), the distance evaluation function Dist (v, omega) and the speed evaluation function Velocity (v, omega) respectively.
Further, in the step (S2), the expression of the objective function is:
Wherein J represents an objective function value, P i represents a prediction time domain, and i represents a prediction time in the prediction time domain; Indicating the equivalent hydrogen consumption of the hybrid energy system at the i-th predicted time in the predicted time domain P i.
Further, searching an optimal path from a starting point to a target point according to environmental data acquired by a large-scale sensor, including:
performing environment modeling according to environment data acquired by a large-scale sensor, and determining all paths from a starting point to a target point;
And searching the optimal paths from all paths by using the particle swarm algorithm by taking each path as a particle in the particle swarm algorithm, wherein in the searching process, the calculation formula of the fitness value of the particle is as follows:
f(p)=ρ·PathLength(p)+σ·ObstacleAvoidance(p)+τ
·EnergyConsumption(p)
Wherein p represents a particle, f (p) represents an fitness value of the particle p, PATHLENGTH (P), obstacleAvoidance (p) and EnergyConsumption (p) represent a path length, obstacle avoidance capability and energy consumption of a path corresponding to the particle p, and ρ, σ and τ represent coefficients of the path length, the obstacle avoidance capability and the energy consumption, respectively.
According to another aspect of the invention, a load source collaborative three-level optimization system of a hybrid energy unmanned system is provided, which comprises a flight path planning module, a collaborative optimization control module, a dynamic obstacle avoidance module and an energy scheduling module;
The track planning module is used for searching an optimal path from the starting point to the target point according to the environmental data acquired by the large-scale sensor;
the dynamic obstacle avoidance module is used for executing dynamic obstacle avoidance;
the energy scheduling module is used for executing energy scheduling of the charge source bidirectional cooperation;
The collaborative optimization control module is used for enabling the unmanned system to navigate along the optimal path, dividing the navigation process into a plurality of prediction time domains, and executing dynamic obstacle avoidance and energy scheduling by using the dynamic obstacle avoidance module and the energy scheduling module in each prediction time domain;
the dynamic obstacle avoidance comprises the steps of carrying out obstacle detection according to environmental data acquired by a high-precision sensor in real time, and carrying out local adjustment on an optimal path if a collision area of a certain obstacle is detected to be intersected with the optimal path so as to avoid the collision area of each obstacle;
The energy scheduling comprises the following steps:
s1, predicting a speed interval of the unmanned system at each prediction time in a current prediction time domain according to a current optimal path, mapping the speed interval into a feasible load demand interval at the corresponding prediction time, and determining a feasible energy scheduling interval corresponding to each feasible load demand interval;
S2, establishing an objective function by taking the minimum equivalent hydrogen consumption total amount of the hybrid energy supply system in the current prediction time domain as a target, solving and obtaining the output power change rate of each energy supply unit in the hybrid energy supply system at each prediction time based on a feasible load demand interval and a corresponding feasible energy scheduling interval, and obtaining an optimal control variable sequence;
(S3) controlling the output power of each energy supply unit in the hybrid energy supply system at each prediction moment according to the optimal control variable sequence;
The large-scale sensor and the high-precision sensor are both mounted on the unmanned system, the large-scale sensor has a wider searching range, the high-precision sensor has higher detection precision, and the collision area is an area formed by a position which is smaller than a preset safety distance from an obstacle.
According to still another aspect of the invention, an autonomous unmanned system is provided, which comprises a hybrid energy unmanned system and a load source collaborative three-layer sub-optimization system of the hybrid energy unmanned system provided by the invention;
The unmanned system is provided with a large-scale sensor and a high-precision sensor, wherein the large-scale sensor has a wider searching range, and the high-precision sensor has higher detection precision.
Further, the large-scale sensor is sonar, and the high-precision sensor is a camera.
In general, through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained:
(1) The invention utilizes the large-scale sensor to collect environment data and plan the optimal path, utilizes the high-precision sensor to collect finer environment data in real time on the basis of the environment data to detect more accurate obstacles, dynamically adjusts the optimal path locally based on the detection result to avoid the obstacles, thereby realizing multi-scale environment sensing, effectively improving the running safety and high efficiency of the unmanned system, further optimizing the power distribution among energy supply units in the hybrid energy supply system in real time with the minimum energy consumption as a target according to the real-time speed of the unmanned system, thereby ensuring the high-efficiency utilization of energy and the minimum consumption and obviously prolonging the endurance capacity of the unmanned system. In general, the invention determines the total range of energy required by the system load from a load end such as an integral route track, then performs scheduling re-optimization of an energy system in a real-time updating process of an unmanned system based on the optimized total load, obtains the systematic optimization performance of the unmanned system to a greater extent through a load-source bidirectional collaborative optimization mechanism, realizes three-layer optimization strategies of external environment perception, track planning and internal energy management, and can promote the cruising ability of the hybrid energy unmanned system and realize safe and efficient navigation.
(2) In the preferred scheme of the invention, when an obstacle is detected, the optimal path is subjected to local stripe adjustment to avoid the obstacle, the feasible speed and track of the submarine in a given time window are calculated in real time based on an improved dynamic window method (Dynamic Window Approach, DWA) and the local obstacle avoidance and path dynamic adjustment are carried out, specifically, the motion state of the unmanned system is described by the speed, the angular speed and the position of the unmanned system, when the collision possibility exists during navigation along the optimal path, the speed is sampled in a forward speed sampling space, the position of the unmanned system is updated based on the speed sampling result, and meanwhile, the updated position is ensured not to collide with the obstacle, so that the local path capable of avoiding the obstacle can be planned efficiently and accurately. In a further preferred embodiment, the current optimum path is selected from the plurality of sets of trajectories based on an evaluation function comprising an azimuth evaluation function, a distance evaluation function and a speed evaluation function at each sampling instant, ensuring that the highest sailing efficiency is obtained while avoiding obstacles.
Drawings
Fig. 1 is a flow chart of a load source collaborative three-level optimization method of a hybrid energy unmanned system provided by an embodiment of the invention.
Detailed Description
The present invention 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 invention 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 invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In the present invention, the terms "first," "second," and the like in the description and in the drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In order to solve the technical problem that the autonomous navigation scheme of the existing hybrid energy supply unmanned system cannot improve the navigation precision, operation safety and cruising ability of the unmanned system at the same time, the invention provides the obstacle avoidance navigation and energy scheduling collaborative optimization method and system of the hybrid energy supply unmanned system, which are integrally designed in such a way that the organic combination of environment perception, path planning and energy management is realized, all subsystems are coordinated to work on different levels through a multi-level collaborative optimization strategy, the multi-scale environment perception technology is utilized, the data of various sensors are integrated, the perception precision and instantaneity of the environment are improved, and thus reliable environment information is provided for path planning; through path planning, combining with real-time environment data, an energy-efficient and safe navigation path is planned, and the unmanned system is ensured to be capable of efficiently navigating in a complex environment; through a refined energy management strategy, the output distribution relation of the fuel cell hybrid power system is dynamically adjusted, so that the energy consumption is reduced to the maximum extent, and the endurance time of the autonomous unmanned system is prolonged.
The load source collaborative three-layer sub-optimization method provided by the invention can be suitable for hybrid energy supply unmanned systems under various complex environments such as underwater, air and the like, such as an underwater unmanned underwater vehicle, a high-altitude unmanned vehicle and the like, and in the following embodiments, the underwater unmanned vehicle with hybrid energy supply by a fuel cell and a lithium cell is taken as an example for illustration.
For the underwater unmanned underwater vehicle, a system model can be constructed, wherein the system model comprises a fuel cell hybrid power system model which is used for describing the characteristics of a power system and consists of a fuel cell and a lithium battery, a dynamics model which is used for describing the dynamics characteristics of an autonomous unmanned system, and a sensor model which is used for describing the perception mode of the autonomous unmanned system. Wherein:
the sensor model describes the environment sensing mode of the autonomous unmanned system, and comprises parameters such as detection range, resolution, data updating frequency, noise characteristics and the like of various sensors. Through the sensor model, how the underwater unmanned underwater vehicle perceives and models the surrounding environment in actual operation can be simulated, real-time environment data support is provided, and a necessary information basis is provided for dynamic path planning and obstacle avoidance strategies.
The dynamics model is used for describing dynamics characteristics of the underwater unmanned submarine, including two aspects of kinematics and dynamics. The kinematic model relates to the motion state parameters such as the speed, acceleration, angular velocity, course angle and the like of the underwater vehicle, and the dynamic model covers the stress condition, the propulsion system characteristic and the motion equation of the underwater vehicle in the underwater environment. The motion behavior of the underwater unmanned underwater vehicle under various operation conditions can be predicted through the dynamic model, and basic data support is provided for path planning and track optimization.
The hybrid system model details the operating principle and performance characteristics of a hybrid system consisting of a fuel cell and a lithium cell. The fuel cell portion mainly includes voltage, current, power output characteristics, and energy conversion efficiency of the fuel cell under different operating conditions. The lithium battery part covers parameters such as charge and discharge characteristics, energy density, battery life and the like. By comprehensively modeling the fuel cell and the lithium battery, the energy distribution and the dynamic performance of the hybrid power system in different task stages can be accurately simulated, and the high-efficiency utilization and the long endurance of the submersible vehicle are ensured.
In order to realize multi-scale environment sensing, optionally, in the following embodiments, a large-scale sensor uses sonar, and a high-precision sensor uses an underwater camera. The sonar has the characteristics of wide exploration range and low precision, and the camera has the characteristics of high detection precision and relatively small range. By combining the two, the environment sensing with large range and low precision and the detail recognition with small range and high precision can be realized. It should be noted that the selection of the sensor is only an alternative embodiment, and should not be construed as a limitation of the present invention, and in other embodiments of the present invention, other sensors may be selected according to the exploration scope and accuracy requirements.
The following are examples.
Example 1:
A load source cooperation three-layer sub-optimization method of a hybrid energy unmanned system is shown in fig. 1, and comprises the following steps:
Firstly, acquiring environmental data by using sonar to obtain large-scale environmental perception data. Specifically, the sonar system can quickly acquire an outline of the surrounding environment, including information such as a topographic structure, a position and a shape of a large obstacle, and the like, in a large range by transmitting and receiving sound waves. Although the resolution of sonar is relatively low, the wide detection range and the strong penetrating power of sonar enable the sonar to provide comprehensive environmental data in a complex underwater environment, and provide basic information for primary path planning of the submarine.
After the large-scale environment sensing data is obtained, searching an optimal path from a starting point to a target point according to the large-scale environment sensing data, optionally, in the embodiment, performing optimal path searching from the starting point to the target point by adopting a Particle Swarm Optimization (PSO) algorithm, randomly generating N particles, wherein each particle represents a path from the starting point P start to the end point P goal, calculating a fitness value f (P) of each particle, and recording a local optimal P best and a global optimal g best of each particle;
in the embodiment, the calculation formula of the fitness value of the particle is as follows:
f(p)=ρ·PathLength(p)+σ·ObstacleAvoidance(p)+
τ·EnergyConsumption(p)
Wherein p represents a particle, f (p) represents an fitness value of the particle p, PATHLENGTH (P), obstacleAvoidance (p) and EnergyConsumption (p) represent a path length, obstacle avoidance capability and energy consumption of a path corresponding to the particle p, and ρ, σ and τ represent coefficients of the path length, the obstacle avoidance capability and the energy consumption, respectively.
The adaptability value designed by the embodiment comprehensively considers factors such as path length, obstacle avoidance capability, energy consumption and the like, can plan an energy-efficient and safe navigation path, and ensures that an unmanned system can efficiently navigate in a complex environment.
After an optimal path is planned, the unmanned system can navigate along the optimal path, the navigation process is divided into a plurality of prediction time domains, and dynamic obstacle avoidance and charge source bidirectional cooperative energy scheduling are sequentially executed in each prediction time domain.
The dynamic obstacle avoidance includes:
The underwater high-precision camera can acquire high-resolution image data in a small range and capture detailed characteristics in the environment, and the underwater camera system can identify and position small obstacles, fine structures and environmental changes through real-time transmission and processing of high-definition images, so that accurate obstacle avoidance information and path adjustment are provided when the underwater vehicle approaches the obstacles;
Detecting the obstacle according to the high-precision environment sensing data;
If the collision area of a certain obstacle is detected to be intersected with the optimal path, locally adjusting the optimal path to avoid the collision area of each obstacle;
Optionally, the embodiment adopts an improved dynamic window method (Dynamic Window Approach, DWA) to calculate the feasible speed and track of the submarine in a given time window in real time and to dynamically adjust the local obstacle avoidance and path;
It is easy to understand that when the underwater unmanned submersible is sailing along the optimal path planned in the large scale range, the speed v, the angular speed ω and the position Pos thereof are basic quantities for describing the movement state of the underwater unmanned submersible, and the speed, the acceleration and the angular speed of the underwater unmanned submersible during the working process are constrained due to the self hardware condition and the environmental limitation, and can be described by the following formula:
Wherein v min and v max represent minimum and maximum speeds, respectively, a min and a max represent minimum and maximum accelerations, respectively, α min and α max represent minimum and maximum angular accelerations, respectively, ω min and ω max represent minimum and maximum angular speeds, respectively; And Representing the minimum angular acceleration and the maximum angular acceleration, respectively.
In order to perform dynamic adjustment of local obstacle avoidance and paths, a set of candidate speeds are dynamically generated by using a DWA (dynamic component analysis) by utilizing the current state and constraint conditions, and the optimal speed is selected from the candidate speeds, so that real-time obstacle avoidance and path optimization are realized. The forward speed sampling space V m of the autonomous unmanned system is:
For each group of speeds (V, omega) sampled from the forward speed sampling space V m, the position Pos (t+Δt) of the next sampling time can be calculated forward at each sampling time, and the position possibly colliding with the obstacle is removed to ensure that the underwater unmanned submarine cannot collide with the obstacle at the next sampling time;
predicting the position of the next sampling instant and ensuring that no collision with an obstacle occurs can be done by the following formula:
Wherein k and k-1 represent two consecutive sampling moments, (pos_x k,Pos_yk,Pos_θk) and (pos_x k-1,Pos_yk-1,Pos_θk-1) represent states of the underwater unmanned aerial vehicle at sampling moments k and k-1, respectively, wherein pos_x and pos_y represent coordinates, pos_θ represents a heading angle, obs (x,y) represents a position coordinate of an obstacle, dis (Obs (x,y),(Pos_xk,Pos_yk)) represents a distance between the obstacle and the underwater unmanned aerial vehicle, and r represents a preset safety distance.
According to a dynamics model of the underwater unmanned underwater vehicle and a state estimation equation thereof, the underwater unmanned underwater vehicle performs multi-group forward track planning within a constraint range of dynamic performance, and a current optimal path is selected according to a path evaluation function, wherein in the embodiment, the path evaluation function can be given by the following description:
The method comprises the steps of setting a speed evaluation function, wherein, the Heading (v, omega) is used for evaluating an angle difference between the direction of the tail end of a track and a target point of an unmanned system at a current set speed, the Dist (v, omega) is used for evaluating a collision distance between the unmanned system and an obstacle, the speed evaluation function is used for evaluating the navigation speed of the unmanned system in a constraint range, and alpha, beta and gamma are coefficients of the Heading evaluation function Heading (v, omega), the distance evaluation function Dist (v, omega) and the speed evaluation function Velocity (v, omega) respectively.
Based on the dynamic obstacle avoidance mode, the running speed of the underwater unmanned underwater vehicle in a period of time in the future can be predicted, a running speed sequence is obtained, and the speed sequence is converted into a corresponding power demand sequence by combining a dynamics model of the underwater unmanned underwater vehicle, so that the energy distribution of a power system is optimized, and the optimal sailing fuel consumption is realized. Accordingly, in the present embodiment, the energy scheduling includes the steps of:
S1, predicting a speed interval of the unmanned system at each prediction time in a current prediction time domain according to a current optimal path, mapping the speed interval into a feasible load demand interval at the corresponding prediction time, and determining a feasible energy scheduling interval corresponding to each feasible load demand interval;
A feasible speed interval exists in the dynamic adjustment process of the external path planning of the unmanned system, a corresponding load demand interval can be obtained based on the feasible speed interval, a feasible energy distribution scheme exists for each possible load demand in the load demand interval, and thus each feasible energy distribution scheme jointly forms a feasible energy scheduling interval, the minimum external load demand is not necessarily an optimal result, and an optimal scheduling result is searched from the feasible load demand interval to the corresponding feasible scheduling interval;
S2, establishing an objective function by taking the minimum equivalent hydrogen consumption total amount of the hybrid energy supply system in the current prediction time domain as a target, and solving to obtain the output power change rate of each energy supply unit in the hybrid energy supply system at each prediction time based on a feasible load demand interval and a corresponding feasible energy scheduling interval to obtain an optimal control variable sequence;
Optionally, in this embodiment, the objective function established is:
Wherein J represents an objective function value, P i represents a prediction time domain, and i represents a prediction time in the prediction time domain; representing the equivalent hydrogen consumption of the hybrid energy supply system at the ith predicted time in the predicted time domain P i;
It is easy to understand that there are corresponding constraints on the output power, the power change rate, and the like of various batteries in the hybrid power supply system, and in this embodiment, the constraints specifically include the output power constraint and the output power change rate constraint of the fuel battery, and the state of charge constraint and the output power constraint of the lithium battery, and can be specifically expressed by the following formulas:
Wherein P fc and ΔP fc represent the output power and the rate of change of the output power of the fuel cell, respectively, AndRepresenting the minimum output power and the maximum output power of the fuel cell respectively,AndRespectively, the minimum output power change rate and the maximum output power change rate of the fuel cell, SOC and P BAT respectively, the state of charge and the output power of the lithium battery, SOC min and SOC max respectively, the maximum state of charge and the minimum state of charge of the lithium battery,AndRespectively representing the maximum charging power and the maximum discharging power of the lithium battery;
In order to meet the requirements of implementing control, the present embodiment establishes a state space model describing the hybrid energy supply system as follows:
Wherein S is a state variable composed of a lithium battery state of charge SOC and a fuel battery output power P fc, u is a control variable composed of a change rate delta P fc of the fuel battery output power, P is the output power of a hybrid energy supply system composed of the fuel battery and the lithium battery, A, B u and C are a system matrix, a control matrix and an output matrix respectively;
According to the hybrid power system model, a system response prediction equation at the future k+N moment is established as follows:
Wherein N represents the length of the prediction time domain;
Optionally, in this embodiment, a dynamic programming (Dynamic Programming, DP) solver is specifically used to solve the objective function to obtain an optimal control variable sequence in the prediction time domain;
And (S3) controlling the output power of each energy supply unit in the hybrid energy supply system at each prediction moment according to the optimal control variable sequence.
In this embodiment, the underwater unmanned underwater vehicle implements the speed sequence according to the optimal path after the dynamic obstacle avoidance by using the energy scheduling manner, predicts the power of N step sizes in the future, sets constraint conditions on the premise that the equivalent hydrogen consumption of the underwater unmanned underwater vehicle is an objective function, uses the dynamic programming solver to obtain the optimal control variable sequence in the prediction time domain, applies the first value of the optimal control variable sequence to the hybrid energy supply system, and simultaneously circulates the above processes to perform rolling optimization, thereby implementing the obstacle avoidance and the limit endurance of the underwater unmanned underwater vehicle. The control method adopted by the embodiment for energy scheduling is a model predictive control (Model Predictive Control, MPC) method.
In general, the embodiment provides a multi-level autonomous unmanned system load source collaborative three-level optimization method covering environment perception, track planning and energy management, through a multi-scale dynamic environment perception technology, an autonomous unmanned system can acquire and update detailed information of surrounding environments in real time, and perform real-time obstacle avoidance and path re-planning by combining an improved dynamic window method, and according to the current position, speed, acceleration and other dynamic parameters of the autonomous unmanned system, a plurality of feasible paths are generated and evaluated, and an optimal track is selected in real time, so that the autonomous unmanned system can ensure that unnecessary path detour and dead time are reduced due to path optimization in a large scale range, can rapidly respond to environment changes, avoid obstacles and avoid collision risks. In a complex environment, the navigation precision and the safety are obviously improved, and meanwhile, the speed sequence in the path planning process is organically combined with the on-line real-time energy management and optimization. Further, according to the power requirements of different task stages and environmental conditions, the power output of each energy supply unit in the hybrid energy supply system is intelligently adjusted, the efficient utilization and the minimum consumption of energy are ensured, the cruising ability of the autonomous unmanned system is obviously prolonged, the energy requirements of the autonomous unmanned system in different running states and the performance of the fuel cell can be predicted through comprehensive power system modeling, potential faults are prevented, and the reliability of the system is improved.
Example 2:
A load source collaborative three-layer sub-optimization system of a hybrid energy supply unmanned system comprises a track planning module, a collaborative optimization control module, a dynamic obstacle avoidance module and an energy scheduling module;
The track planning module is used for searching an optimal path from the starting point to the target point according to the environmental data acquired by the large-scale sensor;
the dynamic obstacle avoidance module is used for executing dynamic obstacle avoidance;
the energy scheduling module is used for executing energy scheduling of the charge source bidirectional cooperation;
The collaborative optimization control module is used for enabling the unmanned system to navigate along the optimal path, dividing the navigation process into a plurality of prediction time domains, and executing dynamic obstacle avoidance and energy scheduling by using the dynamic obstacle avoidance module and the energy scheduling module in each prediction time domain;
the dynamic obstacle avoidance comprises the steps of carrying out obstacle detection according to environmental data acquired by a high-precision sensor in real time, and carrying out local adjustment on an optimal path if a collision area of a certain obstacle is detected to be intersected with the optimal path so as to avoid the collision area of each obstacle;
The energy scheduling comprises the following steps:
s1, predicting a speed interval of the unmanned system at each prediction time in a current prediction time domain according to a current optimal path, mapping the speed interval into a feasible load demand interval at the corresponding prediction time, and determining a feasible energy scheduling interval corresponding to each feasible load demand interval;
S2, establishing an objective function by taking the minimum equivalent hydrogen consumption total amount of the hybrid energy supply system in the current prediction time domain as a target, solving and obtaining the output power change rate of each energy supply unit in the hybrid energy supply system at each prediction time based on a feasible load demand interval and a corresponding feasible energy scheduling interval, and obtaining an optimal control variable sequence;
(S3) controlling the output power of each energy supply unit in the hybrid energy supply system at each prediction moment according to the optimal control variable sequence;
The large-scale sensor and the high-precision sensor are both mounted on the unmanned system, the large-scale sensor has a wider searching range, the high-precision sensor has higher detection precision, and the collision area is an area formed by a position which is smaller than a preset safety distance from an obstacle.
In this embodiment, the specific implementation of each module may refer to the description of embodiment 1 above, and will not be repeated here.
Example 3:
an autonomous unmanned system, comprising a hybrid energy unmanned system and a load source cooperative three-level optimization system of the hybrid energy unmanned system provided in the above embodiment 1;
The unmanned system is provided with a large-scale sensor and a high-precision sensor, wherein the large-scale sensor has a wider searching range, and the high-precision sensor has higher detection precision.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
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