A Constraint-Aware Heuristic Path Planner for Finding Energy-Efficient Paths on Uneven Terrains

Motions of mobile robots need to be optimized to minimize their energy consumption to ensure long periods of continuous operations. Shortest paths do not always guarantee the minimum energy consumption of mobile robots. Moreover, they are not always feasible due to climbing constraints of mobile robots, especially on steep terrains. We utilize a heuristic search algorithm to find energy-optimal paths on hilly terrains using an established energy-cost model for mobile robots. The terrains are represented using grid-based elevation maps. Similar to A*-like heuristic search algorithms, the energy-cost of traversing through a given location of the map depends on a heuristic energy-cost estimation from that particular location to the goal. Using zigzag-like path patterns, the proposed heuristic function can estimate heuristic energy-costs on steep terrains that cannot be estimated using traditional methods. We proved that the proposed heuristic energy-cost function is both admissible and consistent. Therefore, the proposed path planner can always find feasible energy-optimal paths on any given terrain without node revisits, provided that such paths exist. Results of tests on real-world terrain models presented in this paper demonstrate the promising computational performance of the proposed path planner in finding energy-efficient paths.