A Real-Time Safe Navigation Proposal for Mobile Robots in Unknown Environments Using Meta-Heuristics

Abstract

Autonomous navigation in mobile robots represents a significant challenge in unknown and dynamic environments, where the need to avoid obstacles and find safe trajectories in real-time requires efficient solutions. This work presents the MetaHeuristic Real-Time Safe Navigation (MHRTSN) strategy, which combines potential fields with population-based meta-heuristics to optimize trajectory planning and navigation in such environments. The MHRTSN strategy was tested through a series of simulations using different static and dynamic scenarios, comparing the performance of MHRTSN-GA and MHRTSN-PSO versions in terms of displacement, distance traveled, and CPU and clock times. The results demonstrate that both versions provide sub-optimal solutions, with MHRTSN-PSO showing superior performance in terms of required processing time and better convergence compared to MHRTSN-GA when using a small number of individuals. Comparisons with other approaches in the literature showed that MHRTSN generated paths of equivalent length to those of the literature, but in a safer manner. Thus, the proposed approach offers an efficient and safe solution for autonomous navigation in mobile robots, contributing to the advancement of this field in real-world applications. Future work may explore the application of the strategy in physical robotic systems and more complex environments.