Smooth RRT-connect: An extension of RRT-connect for practical use in robots

Abstract

We propose a new extend function for Rapidly-Exploring Randomized Tree (RRT) algorithms that expands along a curve, obeying velocity and acceleration limits, rather than using straight-line trajectories. This results in smooth, feasible trajectories that can readily be applied in robotics applications. Our main focus is the implementation of such methods on RoboSimian, a quadruped robot competing in the DARPA Robotics Challenge (DRC). Planning in a high-dimensional space is also a large consideration in the evaluation of the techniques discussed in this paper as motion planning for RoboSimian requires a search over a 16-dimensional space. In our experiments, we show that our approach produces results that are comparable to the standard RRT solutions in a two-dimensional space and significantly outperforms the latter in a higher-dimensional setting both in computation time and in algorithm reliability.