Physics-based Motion Planning: Evaluation Criteria and Benchmarking

TL;DR

This paper introduces evaluation criteria for physics-based motion planning and benchmarks several kinodynamic planners, revealing their strengths in time and power optimization across different scenarios.

Contribution

It proposes new evaluation criteria for physics-based motion planners and benchmarks key algorithms, providing insights into their performance in various physical scenarios.

Findings

KPIECE finds the fastest solutions with high success rates.

SyCLoP achieves the most power-efficient solutions.

Different planners excel in different optimization criteria.

Abstract

Motion planning has evolved from coping with simply geometric problems to physics-based ones that incorporate the kinodynamic and the physical constraints imposed by the robot and the physical world. Therefore, the criteria for evaluating physics-based motion planners goes beyond the computational complexity (e.g. in terms of planning time) usually used as a measure for evaluating geometrical planners, in order to consider also the quality of the solution in terms of dynamical parameters. This study proposes an evaluation criteria and analyzes the performance of several kinodynamic planners, which are at the core of physics-based motion planning, using different scenarios with fixed and manipulatable objects. RRT, EST, KPIECE and SyCLoP are used for the benchmarking. The results show that KPIECE computes the time-optimal solution with heighest success rate, whereas, SyCLoP compute the most power-optimal solution among the planners used.