We present a hybrid system model describing the behavior of multiple agents cooperating to solve an optimal coverage problem under energy depletion and repletion constraints. The model captures the controlled switchin...
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We present a hybrid system model describing the behavior of multiple agents cooperating to solve an optimal coverage problem under energy depletion and repletion constraints. The model captures the controlled switching of agents between coverage (when energy is depleted) and battery charging (when energy is replenished) modes. Our analysis contains three parts. The first part shows how the model guarantees the feasibility of the coverage problem by defining a guard function on each agent's battery level to prevent it from dying on its way to a charging station. The second part provides two scheduling algorithms to solve the contention problem of agents competing for the only charging station in the mission space. The third part shows the optimality of the motion plan adopted in the proposed model. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The coverage control problem for robotic networks focuses on distributively coordinating the positioning of multiple dynamic agents to provide sensor coverage across a bounded region in two dimensional space. The asso...
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ISBN:
(纸本)9783907144008
The coverage control problem for robotic networks focuses on distributively coordinating the positioning of multiple dynamic agents to provide sensor coverage across a bounded region in two dimensional space. The associated optimal coverage problem seeks to position these agents so as to minimize an associated coverage cost. This coverage cost is typically defined with respect to a density function that is used to bias the network towards desired configurations. Previous approaches to this optimal coverage problem have addressed both static and dynamic environments through the choice of density function;however, stability guarantees for time-varying densities are restricted by significant technical assumptions that simplify the underlying proofs at the expense of limited applicability. In this paper, a generalized algorithm is presented that guarantees practical stability under relaxed technical assumptions. The algorithm, and its convergence, is illustrated via simulation examples.
This paper analyzes the limited range, spatial load balancing problem for agents deployed in non-convex environments and subject to differential constraints which restricts how the agents can move. First, the (unlimit...
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This paper analyzes the limited range, spatial load balancing problem for agents deployed in non-convex environments and subject to differential constraints which restricts how the agents can move. First, the (unlimited range) spatial load balancing problem is introduced and the minimization problem with area constraints is defined. Then, to extend the problem for limited ranges, two cost functions and a sub-partition are defined. The problems are then analyzed and the results prove the existence of a partition that satisfies the area constraints. The non-convex environment makes the problem difficult to solve in continuous-space. Therefore, a probabilistic roadmap is used to approximate agents' cells via a graph. A distributed algorithm is proven to converge to an approximate solution. Finally, the convergence of the algorithm is shown in simulation.
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