Rapid replanning of energy-efficient paths for navigation on uneven terrains

Mobile robots are often utilized in remote and hostile outdoor environments with uncertainties and unknown dangerous. The energy-efficient paths generated based on prior information can be impracticable due to the changes in the environment. Recently proposed Z* search algorithm is capable of finding physically feasible energy-efficient paths on uneven terrains. It can achieve the same accuracy as any brute force algorithm, but with a low computational complexity. However, neither Z* nor any other energy-efficient path planners can effectively handle path replanning triggered by environment changes such as emergence of obstacles. In order to fill this void, we propose a novel algorithm which can recompute optimal paths efficiently. Simulation results show that the proposed algorithm can find equally energy-efficient paths as Z* does, but at a considerably lower computational cost. Therefore, the proposed algorithm can be very useful in mobile robot navigation on uneven terrains with unknown obstacles.