Robot Motion Planning: can GPUs be a Game Changer?

This paper presents a parallel computing implementation of the Iterative Dynamic Programming (IDP) solution to the multipoint Markov-Dubins problem using GPUs. The multipoint Markov-Dubins problem requires the computation of the shortest path with bounded curvature that connects a sequence of planar points (waypoints). As well as being interesting in its own right, an efficient solution to this problem is key to finding optimal or suboptimal solutions of other problems such as the Dubins Travelling Salesman and the Dubins Orienteering problem. The constraint on the curvature makes the problem highly non-linear and complicates its solution. Classic methods are optimisation-based and cast the problem into the Nonlinear Programming (NLP) or Mixed Integer Nonlinear Programming (MINLP) frameworks, for which existing solutions cannot be significantly parallelised. On the contrary, the IDP solution proposed here is well suited for parallel execution. In the paper, we show that the parallel implementation of the IDP outperforms both the NLP/MINLP methods and the iterative version of the IDP methods in terms of accuracy, computation time and power consumption. Computation time and power consumption will be the main focus of the paper, because they are closely related to the implementation on an embedded platform.

Automated summary

This paper introduces a parallel computing implementation of the Iterative Dynamic Programming (IDP) solution to the multipoint Markov-Dubins problem using GPUs. By comparing different methods, the paper demonstrates that the parallel implementation of IDP outperforms other methods in terms of accuracy, computation time, and power consumption.