Autonomous Navigation of a Robotic Wheelchair in an Indoor Environment

Abstract

Autonomous Mobile Robots (AMR) are robotic systems capable of navigating in environments without human intervention. Their growing popularity and practical applications have led to a rapid expansion, driven by increasing interest and research. However, a major challenge faced by these systems is the generation and execution of movements required for efficient trajectory planning, which remains a persistent problem in autonomous systems. In this study, our objective is to contribute to the field of motion planning by introducing two new variants of the Rapidly-exploring Random Tree Star (RRT*) algorithm that integrate the Whale Optimization Algorithm (WOA) to generate near-optimal trajectories. To validate the proposed variants, we implemented them in a simulation environment. Then, we explored the parameter space of WOA for both variants in order to identify optimal parameters and deepen our understanding of behavior with different configurations. The results obtained from the two variants demonstrate significant improvements in trajectory quality, surpassing the performance of the original RRT* algorithm. These promising results highlight the untapped potential of using this optimization technique and also pave the way for further research to explore and exploit the benefits of parallelization aiming to enhance the efficiency and effectiveness of these variants.