When robots learn from nature: GLWOA-RRT*, a nature-inspired motion planning approach

Optimal motion planning for autonomous mobile robots remains still an open issue, with the classical approaches often struggle to address real-world conditions imposed on the system. In recent years, there has been a surge of interest in nature-inspired approaches in solving different technological problems by imitating natural processes. In this context, the present paper concerns the development of a motion planner based on a nature-inspired approach, which is dubbed GLWOA-RRT*. It is based on the Rapidly Exploring Random Tree Star (RRT*), offering an efficient exploration of the search space for a good initialisation of agents, and the Whale Optimisation Algorithm (WOA), one of the most efficient nature-based approaches thanks to its fast convergence, high calculation accuracy, efficiency in balancing exploration and exploitation to avoid falling into a local optimum, and demonstrated remarkable performance in tackling real-world challenges in different fields. GLWOA-RRT* is conceptually built as a global approach, solving the motion planning problem by encoding each agent in the swarm by the robot’s motion. The problem is solved by determining the optimal and safe motion to be executed by the robot. GLWOA-RRT* has been applied first in simulation in both static and dynamic environments, where a detailed experimental analysis of WOA parameters in a motion planning context has been performed. Furthermore, it has been tested in a range of challenging scenarios, where the results demonstrate the efficiency of the algorithm in finding a valid and optimal motion in a reasonable time. The obtained results also exhibit the performance of GLWOA-RRT* in improving RRT* by offering better results, especially in challenging scenarios. Finally, GLWOA-RRT* has been implemented and validated in real-world scenarios using the Robot Operating System (ROS), both in 3D Gazebo-based simulation environments and in physical environments. The outcomes demonstrate its efficacy and applicability in real-world settings.