Fast marching subjected to a vector field-path planning method for mars rovers

摘要

Path planning is an essential tool for the robots that explore the surface of Mars or other celestial bodies such as dwarf planets, asteroids, or moons. These vehicles require expert and intelligent systems to adopt the best decisions in order to survive in a hostile environment. The planning module has to take into account multiple factors such as the obstacles, the slope of the terrain, the surface roughness, the type of ground (presence of sand), or the information uncertainty. This paper presents a path planning system for rovers based on an improved version of the Fast Marching (FM) method. Scalar and vectorial properties are considered when computing the potential field which is the basis of the proposed technique. Each position in the map of the environment has a cost value (potential) that is used to include different types of variables. The scalar properties can be introduced in a component of the cost function that can represent characteristics such as difficulty, slowness, viscosity, refraction index, or incertitude. The cost value can be computed in different ways depending on the information extracted from the surface and the sensor data of the rover. In this paper, the surface roughness, the slope of the terrain, and the changes in height have been chosen according to the available information. When the robot is navigating sandy terrain with a certain slope, there is a landslide that has to be considered and corrected in the path calculation. This landslide is similar to a lateral current or vector field in the direction of the negative gradient of the surface. Our technique is able to compensate this vector field by introducing the influence of this variable in the cost function. Because of this modification, the new method has been called Fast Marching (subjected to a) vector field (FMVF). Different experiments have been carried out in simulated and real maps to test the method performance. The proposed approach has been validated for multiple combinations of the cost function parameters. (C) 2017 Elsevier Ltd. All rights reserved.