Authors: Muhammad Aria Rajasa Pohan, Jana Utama
Abstract
This study aims to present an effective local path-planning strategy for Autonomous Vehicles (AVs) operating in a dynamic urban environment. The method combines bi-directional rule templates, configuration time-space, and RRT-ACS algorithm (hybridization of rapidly exploring random tree star with ant colony system). The use of RRT-ACS accelerates the convergence of feasible pathway planning. Path quality is improved using bi-directional rule templates derived from dynamic urban environment traffic scenarios and combining the RRT-ACS algorithm with configuration time-space. The resulting technique, RRT+BRT+CTS (RRT-ACS with two-way rule template and configuration space-time), emerged as a major research breakthrough. Several simulations of dynamic urban environmental conditions are used to validate the effectiveness of the RRT+BRT+CTS. Comparisons were made with two well-known path-planning algorithms used in dynamic urban environments: Fast RRT and Closed Loop RRT (CL-RRT). The simulation results demonstrate the superior performance of the proposed strategy for local path planning in dynamic environments regarding speed and efficiency for AVs. As a result, the proposed strategy is a suitable choice for addressing the complex challenges of Avs’s urban navigation. © School of Engineering, Taylor’s University.
Author keywords
Autonomous road vehicles; Bi-directional rule templates; Configuration time-space; Path planning; RRT-ACS algorithm
This article can be accessed at https://www.scopus.com/pages/publications/85176544101