Evaluating Performance of Multiple RRTs

This paper presents experimental results evaluating the performance of a new multiple rapidly-exploring random Tree (RRT) algorithm. RRTs are randomised planners especially adept at solving difficult, high-dimensional path planning problems. However, environments with low-connectivity due to the presence of obstacles can severely affect convergence. Multiple RRTs have been proposed as a means of addressing this issue, however, this approach can adversely affect computational efficiency. This paper introduces a new and simple method which takes advantage of the benefits of multiple trees, whilst ensuring the computational burden of maintaining them is minimised. Results indicate that multiple RRTs are able to reduce the logarithmic complexity of the search, most notably in environments with high obstacle densities.