Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation
This addresses path planning challenges for robots in cluttered environments, but it is incremental as it builds on existing MOD approaches.
The paper tackles the computationally expensive minimum obstacle displacement planning problem for mobile robots by developing approximate solutions that are less intensive and differ from the optimal cost by a factor.
In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no path exists due to collision with obstacles. However this problem is computationally expensive and grows exponentially in the size of number of movable obstacles. This work looks into approximate solutions that are computationally less intensive and differ from the optimal solution by a factor of the optimal cost.