Anytime informed path re-planning and optimization for robots in changing environments
This work addresses path planning for robots in changing environments, though it appears incremental as it builds on existing re-planning and optimization techniques.
The paper tackles the problem of robot path planning in dynamic environments with moving obstacles by developing an algorithm that switches between pre-computed paths for collision avoidance and improves paths continuously. Numerical simulations demonstrate the algorithm's effectiveness in various scenarios.
In this paper, we propose a path re-planning algorithm that makes robots able to work in scenarios with moving obstacles. The algorithm switches between a set of pre-computed paths to avoid collisions with moving obstacles. It also improves the current path in an anytime fashion. The use of informed sampling enhances the search speed. Numerical results show the effectiveness of the strategy in different simulation scenarios.