Optimal Coverage Control of Multi-agent Systems in Constrained Environments with Line-of-sight Connectivity Preservation

This paper addresses the optimal coverage control problem of multi-agent systems in constrained environments with static or moving obstacles. The multi-agent systems take a leader-follower framework while the leader’s trajectory is given and the goal of the team is to maximize the coverage metric while maintaining the line-of-sight communication connectivity and obstacle avoidance along with the leader’s movement. We propose a Mixed Integer Nonlinear Programming (MINLP) approach by modeling the connectivity constraints and the obstacle avoidance constraints as some nonlinear constraints. This approach will provide an off-line solution for dealing with constrained coverage problems with obstacles. Simulation results are provided to show the effectiveness of the method.

[1]  Youmin Zhang,et al.  Distributed coordination of multi-agent systems for coverage problem in presence of obstacles , 2012, 2012 American Control Conference (ACC).

[2]  Jie Huang,et al.  Adaptive Leader-Following Consensus for a Class of Nonlinear Multi-Agent Systems with Jointly Connected Switching Networks , 2016 .

[3]  Christos G. Cassandras,et al.  Optimal dynamic formation control of multi-agent systems in constrained environments , 2016, Autom..

[4]  Kai Liu,et al.  Leader-following consensus of multi-agent systems with jointly connected topology using distributed adaptive protocols , 2014, J. Frankl. Inst..

[5]  Yuan Fan,et al.  Coverage control for mobile sensor networks with limited communication ranges on a circle , 2018, Autom..

[6]  Christos G. Cassandras,et al.  Exploiting submodularity to quantify near-optimality in multi-agent coverage problems , 2019, Autom..

[7]  Mac Schwager,et al.  Robust Adaptive Coverage Control for Robotic Sensor Networks , 2017, IEEE Transactions on Control of Network Systems.

[8]  Ömür Arslan,et al.  Statistical Coverage Control of Mobile Sensor Networks , 2019, IEEE Transactions on Robotics.

[9]  Petros G. Voulgaris,et al.  Multi-objective control for multi-agent systems using Lyapunov-like barrier functions , 2013, 52nd IEEE Conference on Decision and Control.

[10]  Christos G. Cassandras,et al.  Distributed Coverage Control in Sensor Network Environments with Polygonal Obstacles , 2008 .

[11]  Guo Qi TRAJECTORY CONTROL WITH OBSTACLE AVOIDANCE OF MOBILE ROBOTS BASED ON NEURAL NETWORK , 2002 .

[12]  John A. Richards,et al.  Optimal Coverage Control and Stochastic Multi-Target Tracking * , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

[13]  Zezhong Xu,et al.  Dynamic obstacle avoidance and path planning based on modified genetic algorithm , 2005 .

[14]  Jinde Cao,et al.  Leader-following consensus of non-linear multi-agent systems with jointly connected topology , 2014 .

[15]  Christos G. Cassandras,et al.  A submodularity-based approach for multi-agent optimal coverage problems , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[16]  Katia Sycara,et al.  Voronoi-based Coverage Control with Connectivity Maintenance for Robotic Sensor Networks , 2019, 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS).

[17]  Christos G. Cassandras,et al.  Escaping local optima in a class of multi-agent distributed optimization problems: A boosting function approach , 2014, 53rd IEEE Conference on Decision and Control.

[18]  Petros G. Voulgaris,et al.  A swarm-based approach to dynamic coverage control of multi-agent systems , 2020, Autom..

[19]  Ahmed Benzerrouk,et al.  Obstacle avoidance controller generating attainable set-points for the navigation of Multi-Robot System , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).