Dynamic Informed Bias RRT*-Connect: Improving Heuristic Guidance by Dynamic Informed Bias Using Hybrid Dual Trees Search

Abstract

AbstractThe RRT*-Connect algorithm enhances efficiency through dual tree bias growth, yet this bias can be inherently blind, potentially affecting the algorithm’s heuristic performance. In contrast, the Informed RRT* algorithm narrows the planning problem’s scope by leveraging an informed region, thereby improving convergence efficiency towards optimal solutions. However, this approach relies on the prior establishment of feasible paths. Combining these two algorithms can address the challenges posed by Informed RRT while also accelerating convergence towards optimality, albeit without resolving the issue of blind bias in dual trees.In this paper, we proposed a novel algorithm: Dynamic Informed Bias RRT*-Connect. This algorithm, grounded in potential and explicit informed bias sampling, introduces a dynamical bias points set that guides dual tree growth with precision objectives. Additionally, we enhance the evaluation framework for algorithmic heuristics by introducing two innovative metrics that effectively capture the algorithm’s characteristics. The improvements observed in traditional indicators demonstrate that the proposed algorithm exhibits greater heuristic compared to RRT*-Connect and Informed RRT*-Connect. These findings also suggest the viability of the new metrics introduced in our evaluation framework.