Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance

International audienceIn this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach

Adaptive-based Distributed Cooperative Multi-Robot Coverage

International audienceIn this paper we present a solution to the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with unknown obstacles. The problem is addressed taking into account several physical and environmental constraints like limited sensor capabilities, obstacle-avoidance, etc. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task. Furthermore, we propose a different formulation of the problem in order to obtain a distributed solution which allows us to consider also limited communication capabilities. Extensive simulations are presented to evaluate the efficiency of the proposed algorithm and to compare centralized and distributed approach

Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance

International audienceIn this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach