Enabling nonholonomic smoothness generically allowing for unpredictable drift

This paper addresses the problem of how to enable smoothness generically in nonholonomic motion planning for mobile robots travelling at speed both without and with unpredictable drift. The problem raises the challenge of maintaining goal connection while also optimising the path curvature. The approach is a gradient based methodology set in dynamic potential fields and has the capacity to enable integration with existing potential field based methods for obstacle avoidance and coordinated motion. The methodology interleaves multiple planning and execution cycles to deal with drift. We present our methodology and demonstrate experimental results for the 4-D unicycle and a nonholonomic integrator. The results show that the curvature metric used is able to improve smoothness to a given degree of optimality while reliably maintaining goal connection. They also show that interleaving and increased smoothness allows the path to keep relatively close to optimised plans.