Iterative parallel sampling RRT for racing car simulation

Abstract

Graphics Processing Units have evolved at a large pace, maintaining a processing power orders of magnitude higher than Central Processing Units. As a result, the interest of using the General-Purpose computing on Graphics Processing Units paradigm has grown. Nowadays, big effort is put to study probabilistic search algorithms like the Randomized Search Algorithms family, which have good time complexity, and thus can be adapted to massive search spaces. One of those algorithms is Rapidly Exploring Random Tree (RRT) which reveals good results when applied to high dimensional dynamical search spaces. This paper proposes a new variant of the RRT algorithm called Iterative Parallel Sampling RRT which explores the use of parallel computation in GPU to generate faster solutions. The algorithm was used to construct a CUDA accelerated bot for the TORCS open source racing game and tested against the plain RRT. Preliminary tests show lap time reductions of around 17% and the potential for reducing search times.