Mobile Robot Navigation in Complex and Dynamic Environments Using Multi-Objective Ant Colony Optimization

In many real-world scenarios, automated mobile robots are used to navigate through environments with rough terrain containing obstacles. These obstacles can be static as well as dynamic in nature. The robot should maintain a considerable safe distance from these obstacles. Moreover, the path taken by the robot must be smooth as well as energy efficient. This article proposes a Multi-objective Ant Colony Optimization (MOACO) based path planning algorithm called Efficient Multi-objective Ant Colony Optimization (EMOACO) for navigating mobile robots in environments with static and dynamic obstacles. The environments are modeled as grid-based. We consider four objectives to be optimized. These are– path length, safety, path smoothness, and overall elevation change. An ageing and ranking-based novel pheromone update technique is introduced in this paper that increases the diversity of solutions present in the Pareto front. For static environments (SE), a comparative study is performed among the proposed EMOACO-SE, NSGA-II, and A* algorithm. Another comparative study is carried out for the dynamic environment (DE) among the proposed EMOACO-DE and ABACO (Ageing Based Ant Colony Optimization) algorithm. These comparative studies establish the efficacy of the proposed method.