Multi-cost robotic motion planning under uncertainty

This paper describes an algorithm for robotic motion planning that is capable of optimising several cost functions simultaneously to provide optimised, feasible and collision-free paths. The algorithm is based on the best-first graph search algorithm using a Pareto frontier to evaluate costs at each node. Additionally, we include a calculation of the distribution of robot trajectories when the path is realised using a LQR based controller. This ensures that the possibility of collisions is greatly reduced. Results are provided that show multi-cost robotic path planning under position uncertainty and control constraints whilst simultaneously optimising distance travelled and fuel spent.