Recent progress on sampling based dynamic motion planning algorithms

Authors

Andrew Short, University of WollongongFollow
Zengxi Stephen Pan, University of WollongongFollow
Nathan P. Larkin, University of WollongongFollow
Stephen van Duin, University of WollongongFollow

RIS ID

110383

Publication Details

Short, A., Pan, Z., Larkin, N. & van Duin, S. (2016). Recent progress on sampling based dynamic motion planning algorithms. 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 1305-1311). USA: IEEE.

Abstract

This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored.