Distributed Multi-Robot Navigation in Formation among Obstacles : A Geometric and Optimization Approach with Consensus

This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots, allowing for reconfigurations, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations.

[1]  Vijay Kumar,et al.  Automated composition of motion primitives for multi-robot systems from safe LTL specifications , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Angela P. Schoellig,et al.  Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2002, SIAM J. Optim..

[4]  Paul A. Beardsley,et al.  Collision avoidance for aerial vehicles in multi-agent scenarios , 2015, Auton. Robots.

[5]  Vijay Kumar,et al.  Distributed multi-robot task assignment and formation control , 2008, 2008 IEEE International Conference on Robotics and Automation.

[6]  Robin Deits,et al.  Computing Large Convex Regions of Obstacle-Free Space Through Semidefinite Programming , 2014, WAFR.

[7]  Jorge Cortés,et al.  Global and robust formation-shape stabilization of relative sensing networks , 2009, Autom..

[8]  Tucker R. Balch,et al.  Social potentials for scalable multi-robot formations , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Nicholas Roy,et al.  Towards A Swarm of Agile Micro Quadrotors , 2013 .

[10]  Hyo-Sung Ahn,et al.  A survey of multi-agent formation control , 2015, Autom..

[11]  Javier Alonso-Mora,et al.  Multi-robot navigation in formation via sequential convex programming , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[12]  Eduardo Montijano,et al.  Efficient multi-robot formations using distributed optimization , 2014, 53rd IEEE Conference on Decision and Control.

[13]  John R. Spletzer,et al.  Convex Optimization Strategies for Coordinating Large-Scale Robot Formations , 2007, IEEE Transactions on Robotics.

[14]  Vijay Kumar,et al.  Modeling and control of formations of nonholonomic mobile robots , 2001, IEEE Trans. Robotics Autom..

[15]  Giuseppe Notarstefano,et al.  A distributed simplex algorithm for degenerate linear programs and multi-agent assignments , 2012, Autom..

[16]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[17]  Ross A. Knepper,et al.  Local motion planning for collaborative multi-robot manipulation of deformable objects , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Paul A. Beardsley,et al.  Image and animation display with multiple mobile robots , 2012, Int. J. Robotics Res..

[19]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..