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In this paper, we propose decentralized position controllers for a team of point-mass robots that must cooperatively transport a payload to a target location. The robots have double-integrator dynamics and are rigidly attached to the... more
In this paper, we propose decentralized position controllers for a team of point-mass robots that must cooperatively transport a payload to a target location. The robots have double-integrator dynamics and are rigidly attached to the payload. The controllers only require robots' measurements of their own positions and velocities, and the only information provided to the robots is the desired position of the payload's center of mass. We consider scenarios in which the robots do not know the position of the payload's center of mass and try to selfishly stabilize their own positions to the desired location, similar to the behaviors exhibited by certain species of ants when retrieving food items in groups. We propose a proportional-derivative (PD) controller that does not rely on inter-robot communication, prior information about the load dynamics and geometry, or knowledge of the number of robots and their distribution around the payload. Using a Lyapunov argument, we prove...
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Research Interests: Engineering, Collective Behavior, Computer Science, Motion Planning, Optimization, and 15 morePopulation, Swarm Robotics, Robot kinematics, Case Study, ROBOT, Global Optimization, Population Size, Stochastic Optimization, Robot Control, Mathematical Model, Control Strategy, Equations, Environmental Flow, Robot sensing systems, and Swarm Behaviour
We do a Probabilistic Analysis of the Network generated by robots involved in Stochastic Boundary Coverage
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This technical brief summarizes and extends our recently introduced control framework for stochastically allocating a swarm of robots among boundaries of circular regions. As in the previous work, a macroscopic model of the swarm... more
This technical brief summarizes and extends our recently introduced control framework for stochastically allocating a swarm of robots among boundaries of circular regions. As in the previous work, a macroscopic model of the swarm population dynamics is used to synthesize robot control policies that establish and maintain stable predictable team sizes around region boundaries. However, this extension shows that the control strategy can be implemented with no robot-to-robot communication. Moreover, target team sizes can vary across different types of regions, where a region's type is a subjective characteristic that only needs to be detectable by each individual robot. Thus, regions of one type may have a higher equilibrium team size than regions of another type. In other work that predicts and controls stochastic swarm behaviors using macroscopic models, the equilibrium allocations of the swarm are sensitive to changes in the mean robot encounter rates with objects in the environ...
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Research Interests: Engineering, Robotics, Collective Behavior, Computer Science, Biomedical Engineering, and 12 moreSwarm Intelligence, Social insects, Swarm Robotics, Distributed Control, Behavior Modeling, Multirobot systems, Experimental Study, Biological System Modeling, Electrical And Electronic Engineering, Hybrid System, Swarm Behaviour, and A Priori Information
Research Interests: Mechanical Engineering, Robotics, Collective Behavior, Computer Science, Modeling, and 15 moreMarkov Processes, Stochastic Systems, Optimization, Optimization Problem, Swarm Robotics, Multi Robot Systems, Distributed Control, Stochastic Control, Optimisation, ROBOT, Scalability, Task Allocation, Stochastic system, Markov Process, and Electrical And Electronic Engineering
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Research Interests: Collective Behavior, Partial Differential Equations, Computational Modeling, Motion control, Probability, and 12 moreStochastic processes, Mobile Robots, Smoothed Particle Hydrodynamics, Robot kinematics, Population Size, Hydrodynamics, Robot Control, PARTIAL DIFFERENTIAL EQUATION, Random Processes, Mathematical Model, Decentralized Control, and Top Down
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Group food retrieval in some ant species serves as a useful paradigm for multirobot collective transport strategies that are decentralized, scalable, and do not require a priori information about the payload. We present a comprehensive... more
Group food retrieval in some ant species serves as a useful paradigm for multirobot collective transport strategies that are decentralized, scalable, and do not require a priori information about the payload. We present a comprehensive overview of group retrieval in ants and investigate this phenomenon in Aphaenogaster cockerelli in order to extract the ants' roles during transport, the rules that govern their actions, and the individual forces that they apply to guide a food item to their nest. To measure these forces, we fabricated elastic structures with calibrated stiffness properties, induced ants to retrieve the structures, and tracked the resulting deformations with a camera. We then developed a hybrid system model of the ant behaviors that were observed in the experiments. We conducted simulations of the behavioral model that incorporate a quasi-static model of planar manipulation with compliant attachment points. Our simulations qualitatively replicate individual ant activity as well as certain macroscopic features of the transport.
Research Interests: Robotics, Collective Behavior, Biomedical Engineering, Swarm Intelligence, Social insects, and 10 moreElasticity, Swarm Robotics, Distributed Control, Compliant Mechanisms, Behavior Modeling, Multirobot systems, Experimental Study, Biological System Modeling, Electrical And Electronic Engineering, and Hybrid System
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We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task... more
We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al., in Proceedings of the International Conference on Intelligent Robots and Systems (IROS’07), pp. 2320–2325, 2007). We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.
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We present a methodology for characterizing, analyzing, and synthesizing swarm behaviors using both a macroscopic continuous model that represents a swarm as a continuum and a macroscopic discrete model that enumerates individual agents.... more
We present a methodology for characterizing, analyzing, and synthesizing swarm behaviors using both a macroscopic continuous model that represents a swarm as a continuum and a macroscopic discrete model that enumerates individual agents. Our methodology is applied to a dynamical model of ant house hunting, a decentralized process in which a colony attempts to emigrate to the best site among several alternatives. The model is hybrid because the colony switches between different sets of behaviors, or modes, during this process. Using the model in [1], we investigate the relation of site population growth to initial system state with an algorithm called Multi-Affine Reachability analysis using Conical Overapproximations (Marco) [2]. We then derive a microscopic hybrid dynamical model of an agent that respects the specifications of the global behavior at the continuous level. Our multi-level simulations demonstrate that we have produced a rigorously correct microscopic model from the macroscopic descriptions.
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We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no... more
We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.
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We present a new algorithm for the reachability analysis of multi-affine hybrid systems. In our previous work on reachability analysis and that of our collaborators [1,2,3], we exploited the convexity of multi-affine functions and the... more
We present a new algorithm for the reachability analysis of multi-affine hybrid systems. In our previous work on reachability analysis and that of our collaborators [1,2,3], we exploited the convexity of multi-affine functions and the fact that the vector field in modes with rectangular invariants is uniquely determined by its values at the rectangle vertices. In this paper, we explicitly calculate conical overapproximations of the reachable set in the invariant of each mode. We describe our Multi-Affine Reachability analysis using Conical Overapproximations, Marco, and show that it yields results that are superior to those obtained by existing methods for multi-affine hybrid systems. Finally, we demonstrate the application of Marco to the analysis of an ant house hunting model that incorporates quorum sensing [4] and the analysis of bi-stability of the lactose induction system regulated by glucose and lactose [5].