Robot learning-enhanced tree-based algorithms for kinodynamic motion planning: A comparative analysis

Abstract

Kinodynamic motion planning is pivotal in advancing robotics, en- abling autonomous systems to navigate dynamic environments effectively while adhering to both kinematic and dynamic constraints. This study delves into the efficacy of tree sampling-based planners, namely the Rapidly- exploring Random Tree (RRT), Rapidly-exploring Random Tree Star (RRT*), and Dominance Informed Region Trees (DIRT), in kinodynamic motion planning. Through a comparative analysis focusing on both fully informed and uninformed versions of these algorithms, I explore their performance in environments with dynamic constraints. Special emphasis is placed on the integration of learned controls, aiming to enhance maneuver planning. My research reveals significant differences in success rates, iterations, and path costs among the algorithms, underscoring DIRTs superiority under certain conditions and the beneficial impact of learned controls. These findings contribute valuable insights into the selection and optimization of motion planning algorithms, paving the way for more efficient and adapt- able autonomous systems.