Cooperative Collision Avoidance for Nonholonomic Robots

Javier Alonso-Mora, Paul Beardsley, Roland Siegwart
2018 IEEE Transactions on robotics  
In this paper we present a method, namely εCCA, for collision avoidance in dynamic environments among interacting agents, such as other robots or humans. Given a preferred motion by a global planner or driver, the method computes a collision-free local motion for a short time horizon, which respects the actuator constraints and allows for smooth and safe control. The method builds on the concept of reciprocal velocity obstacles and extends it to respect the kinodynamic constraints of the robot
more » ... aints of the robot and account for a grid-based map representation of the environment. The method is best suited for large multirobot settings, including heterogeneous teams of robots, where computational complexity is of paramount importance and the robots interact with one another. In particular, we consider a set of motion primitives for the robot and solve an optimization in the space of control velocities with additional constraints. Additionally, we propose a cooperative approach to compute safe velocity partitions in the distributed case. We describe several instances of the method for distributed and centralized operation and formulated both as convex and non-convex optimizations. We compare the different variants and describe the benefits and trade-offs both theoretically and in extensive experiments with various robotic platforms: robotic wheelchairs, robotic boats, humanoid robots, small unicycle robots and simulated cars. J. Alonso-Mora is with the
doi:10.1109/tro.2018.2793890 fatcat:k72qxkf2ergfzdvkd7kng4di2i