Safety-aware Path Planning Using Metaheuristics

The proliferation of robotic applications has been notable in recent years, spanning a diverse range of use cases. Ensuring robot safety is crucial for safeguarding both robots and humans, as well as the infrastructure they operate within. Robots navigating indoor environments are particularly susceptible to hazards ranging from minor incidents to severe damage. Therefore, it is essential to consider potential risks and ensure safe navigation while maintaining efficiency. This paper proposes a path-planning method that prioritizes robot safety and path efficiency. Ant Colony Optimization (ACO) is employed in this method to find optimal solutions that balance path distance and the risks faced by robots. Experimental results validate that the proposed approach significantly reduces potential hazards robots may face in various environments during navigation compared to existing methods. Importantly, this risk reduction is achieved without compromising path distance or accessibility.