On the performance of random linear projections for sampling-based motion planning

Ioan Alexandru Sucan, Lydia E. Kavraki
2009 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Sampling-based motion planners are often used to solve very high-dimensional planning problems. Many recent algorithms use projections of the state space to estimate properties such as coverage, as it is impractical to compute and store this information in the original space. Such estimates help motion planners determine the regions of space that merit further exploration. In general, the employed projections are user-defined, and to the authors' knowledge, automatically computing them has not
more » ... uting them has not yet been investigated. In this work, the feasibility of offline-computed random linear projections is evaluated within the context of a state-of-the art samplingbased motion planning algorithm. For systems with moderate dimension, random linear projections seem to outperform human intuition. For more complex systems it is likely that non-linear projections would be better suited.
doi:10.1109/iros.2009.5354403 dblp:conf/iros/SucanK09 fatcat:6vsvplkahnbqvnxc2nxxubtmw4