Path planning for Lunar rovers

Abstract

The unique six-legged swarming rover Lunar Zebro is designed and produced by students from the Delft University of Technology. The objective of the rover is to accomplish an autonomous mission on the Lunar surface by 2024. This thesis evaluates a path planning algorithm that is designed for autonomous navigation in the Lunar environment. The thesis studies existing path planning algorithms and determines the essential functionalities of the algorithm and the unique requirements of Lunar Zebro. It is found that an Artificial Potential Field based path planning algorithm accommodates the determined needs and requirements.
With the help of the Artificial Potential Field path planning algorithm and the unique requirements, a vector field based algorithm is developed. The algorithm uses an attractive vector field to attract the rover to the determined target. Meanwhile, obstacles or other obstructions are denoted by a repulsive rotational vector field around the edge of the obstacles. This rotational repulsive force ensures obstacle avoidance and prevention of the local minimum trap, which often occurs in Artificial Potential Field path planning. Improvements are suggested to increase reachability and decrease path length and planning time of the rotational vector field algorithm. In the Python developed simulation, the improved algorithm accomplishes a 62% reduction in planning time compared with the original Artificial Potential Field algorithm and achieves similar path length results. Moreover, the proposed algorithm has a reachability of 90% where the Artificial Potential Field algorithm just reaches a success rate of 55%.
The thesis concludes with the future work recommendations for a low level implementation in C or either C++ to facilitate the deployment in a microcontroller.