Finding energy-efficient paths on uneven terrains

Abstract

Abstract-Mobile robots are increasingly getting popular in outdoor applications. Long period of continuous operations are common in such applications. Therefore, robot motions need to be optimized to minimize their energy consumption. Shortest paths do not always guarantee minimum energy consumptions of mobile robots. This paper proposes a novel algorithm to generate energyefficient paths on uneven terrains using an established energycost model for mobile robots. Terrains are represented using grid based elevation maps. Similar to A* algorithm, the energy-cost of traversing through a particular gird depends on a heuristic energy-cost estimation from the current location to the goal. The proposed heuristic energy-cost function makes it possible to generate zigzag-like path patterns on steep hills under the power limitations of the robot. Therefore, the proposed method can find physically feasible energy-efficient paths on any given terrain, provided that such paths exist. Simulation results presented in this paper demonstrate the performance of the proposed algorithm on uneven terrains maps.

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

“…In [31], the authors showed that A* with such a heuristic function is unable to find physically feasible paths on steep terrains because heuristics can sometime be infinitely large depending on the gradient of the straight line connecting current location and goal location. Recently, we proposed a heuristic search algorithm (basic Z*) for energy-efficient path planning on simulated grid-based elevation maps [33]. Its heuristics can overcome the impermissible traversal headings due to power limitations of a mobile robot.…”

“…Its heuristics can overcome the impermissible traversal headings due to power limitations of a mobile robot. Results given in [33] show that the basic Z* can find energy-efficient paths on uneven terrains where A*-E opt may fail due to infinitely large heuristic energycost. However, the computational efficiency of the basic Z* algorithm may vary due to regular node revisits while searching for an optimal solution.…”

“…Based on such zigzag movements, we proposed a heuristic energy-cost function [33], which can be summarized as…”

“…Here, the gravitational field strength (g) is assumed to be 9.81 ms −2 . In contrast to previous work on energy-efficient path planning [31], [33], which were conducted on simulated hilly landscapes, tests were carried out on several DEMs of real terrains with an area of 1 km × 1 km. All these DEMs were fed to the algorithms under test as square-shaped graphs with 100 nodes on each side (i.e., 10 000 nodes in each graph).…”

Methodology of Using Terrain Passability Maps for Planning the Movement of Troops and Navigation of Unmanned Ground Vehicles

“…Current research focuses on the autonomous movement of vehicles in a dynamic environment, full of terrain obstacles [ 44 , 45 , 46 ]. Understandably, scientists pay special attention to considering obstacles caused by terrain formation, hence a large number of publications include Digital Terrain Models in the presented methodologies [ 47 , 48 , 49 , 50 ].…”