Pareto be Multi-Objective Termite Colony Optimization for Motion Planning in concern with Four-Wheeled Mobile Robots

This paper presents a novel application of Pareto-based Multi-Objective Termite Colony Optimization (MOTCO) in motion planning for a four-wheeled mobile robot navigating dynamic environments. The MOTCO algorithm is inspired by termite foraging behavior, leveraging pheromone-based interactions and exploration to solve multi-objective optimization problems, including minimizing path length, ensuring obstacle avoidance, and reducing energy consumption. By integrating the Pareto dominance approach, the algorithm effectively handles trade-offs among conflicting objectives, ensuring diverse and optimal solutions suitable for real-time robotic applications. The methodology involves formulating the motion planning problem as a multi-objective optimization task, incorporating constraints such as dynamic obstacle positions and kinematic limitations of the robot. The proposed MOTCO algorithm iteratively refines potential paths, guided by pheromone trails and Pareto-optimal fronts, to achieve a robust, collision-free, and energy-efficient trajectory. Experimental evaluations compare MOTCO with other state-of-the-art algorithms, including Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization. Metrics such as path length, execution time, convergence rate, and computational efficiency are analyzed across varying environmental complexities. The results demonstrate that MOTCO consistently outperforms existing methods, achieving shorter paths, faster convergence, and improved adaptability in dynamic scenarios. The Pareto dominance mechanism ensures a well-distributed set of solutions, providing flexibility for decision-makers in selecting trade-offs suited to specific operational needs. This research contributes to advancing autonomous navigation in robotics, with potential applications in warehouse automation, search-and-rescue missions, and other complex environments requiring efficient multi-objective decision-making.