RRT-Connect: Faster, asymptotically optimal motion planning

• Published in: 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) pp. 1670 - 1677
• Main Authors: Klemm, Sebastian, Oberlander, Jan, Hermann, Andreas, Roennau, Arne, Schamm, Thomas, Zollner, J. Marius, Dillmann, Rudiger
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2015
• Subjects: Algorithm design and analysis; Joining processes; Legged locomotion; Planning; Probabilistic logic; Search problems
• DOI: 10.1109/ROBIO.2015.7419012

We present an efficient asymptotically-optimal randomized motion planning algorithm solving single-query path planning problems using a bidirectional search. The algorithm combines the benefits from the widely known algorithms RRT-Connect and RRT∗ and scores better than both by finding a solution faster than RRT∗, and -unlike RRT-Connect - converging towards a theoretical optimum. We outline the proposed algorithm and proof its optimality. The efficiency and robustness is demonstrated in a number of real world applications which benefit from the bidirectional approach: planning car trajectories in a parking garage for the autonomous vehicle CoCar, generating cost-efficient trajectories for the multi-legged walking robot LAURON V in a planetary exploration scenario and performing mobile manipulation tasks for our highly actuated service robot HoLLiE. Moreover, we compare and show the improvements over "vanilla" RRT in a set of challenging benchmarks. RRT∗-Connect will contribute to increase the performance of autonomous robots and vehicles due to the reduced motion planning time in complex environments.