Evaluating Guiding Spaces for Motion Planning
This work provides a theoretical foundation for comparing and improving sampling-based motion planning algorithms, which is incremental as it builds on existing heuristics.
The paper tackles the problem of evaluating biased sampling in robot motion planning by introducing a unifying framework called the motion planning guiding space and proposing an information-theoretic method to assess the quality of guided planning, demonstrating its applicability through analysis of several algorithms.
Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more information, or are more likely to participate in the final solution. In this work, we define the \emph{motion planning guiding space}, which encapsulates many seemingly distinct prior works under the same framework. In addition, we suggest an information theoretic method to evaluate guided planning which places the focus on the quality of the resulting biased sampling. Finally, we analyze several motion planning algorithms in order to demonstrate the applicability of our definition and its evaluation.