B-UAVC: Buffered Uncertainty-Aware Voronoi Cells for Probabilistic Multi-Robot Collision Avoidance

This paper presents B-UAVC, a distributed collision avoidance method for multi-robot systems that accounts for uncertainties in robot localization. In particular, Buffered Uncertainty-Aware Voronoi Cells (B-UAVC) are employed to compute regions where the robots can safely navigate. By computing a set of chance constraints, which guarantee that the robot remains within its B-UAVC, the method can be applied to non-holonomic robots. A local trajectory for each robot is then computed by introducing these chance constraints in a receding horizon model predictive controller. The method guarantees, under the assumption of normally distributed position uncertainty, that the collision probability between the robots remains below a specified threshold. We evaluate the proposed method with a team of quadrotors in simulations and in real experiments.

[1]  You-yen. Yang Classification into two multivariate normal distributions with different covariance matrices , 1965 .

[2]  Saptarshi Bandyopadhyay,et al.  Fast, On-line Collision Avoidance for Dynamic Vehicles Using Buffered Voronoi Cells , 2017, IEEE Robotics and Automation Letters.

[3]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  Alexander Domahidi,et al.  Real-time planning for automated multi-view drone cinematography , 2017, ACM Trans. Graph..

[5]  Dinesh Manocha,et al.  Reciprocal n-Body Collision Avoidance , 2011, ISRR.

[6]  Masahiro Ono,et al.  Chance-Constrained Optimal Path Planning With Obstacles , 2011, IEEE Transactions on Robotics.

[7]  Dinesh Manocha,et al.  PRVO: Probabilistic Reciprocal Velocity Obstacle for multi robot navigation under uncertainty , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Roland Siegwart,et al.  Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[10]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[11]  Roland Siegwart,et al.  Cooperative Collision Avoidance for Nonholonomic Robots , 2018, IEEE Transactions on Robotics.

[12]  Gaurav S. Sukhatme,et al.  Trajectory Planning for Quadrotor Swarms , 2018, IEEE Transactions on Robotics.

[13]  Javier Alonso-Mora,et al.  Chance-Constrained Collision Avoidance for MAVs in Dynamic Environments , 2019, IEEE Robotics and Automation Letters.

[14]  Jur P. van den Berg,et al.  Generalized reciprocal collision avoidance , 2015, Int. J. Robotics Res..

[15]  S. Shankar Sastry,et al.  Decentralized nonlinear model predictive control of multiple flying robots , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[16]  Jonathan P. How,et al.  Decoupled multiagent path planning via incremental sequential convex programming , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Daniel Lyons,et al.  Chance constrained model predictive control for multi-agent systems with coupling constraints , 2012, 2012 American Control Conference (ACC).

[18]  Stan Lipovetsky,et al.  Tractable Measure of Component Overlap for Gaussian Mixture Models , 2014, 1407.7172.

[19]  Larry C. Andrews,et al.  Special Functions Of Mathematics For Engineers , 2022 .

[20]  Soon-Jo Chung,et al.  Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming , 2016, Int. J. Robotics Res..

[21]  Karl Tuyls,et al.  Collision avoidance under bounded localization uncertainty , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.