In many real-world applications (eg, human-robot collaboration), the environment changes
rapidly, and the intended path may become invalid due to moving obstacles. In these
situations, the robot should quickly find a new path to reach the goal, possibly without
stopping. Planning from scratch or repairing the current graph can be too expensive and
time-consuming. This paper proposes MARS, a sampling-based Multi-pAth Replanning
Strategy that enables a robot to move in dynamic environments with unpredictable …

In many real-world applications (eg, human-robot collaboration), the environment changes
rapidly, and the intended path may become invalid due to moving obstacles. In these
situations, the robot should quickly find a new path to reach the goal, possibly without
stopping. Planning from scratch or repairing the current graph can be too expensive and
time-consuming. This paper proposes MARS, a sampling-based Multi-pAth Replanning
Strategy that enables a robot to move in dynamic environments with unpredictable …