1 research outputs found
Optimized Mission Planning for Planetary Exploration Rovers
The exploration of planetary surfaces is predominately unmanned, calling for
a landing vehicle and an autonomous and/or teleoperated rover. Artificial
intelligence and machine learning techniques can be leveraged for better
mission planning. This paper describes the coordinated use of both global
navigation and metaheuristic optimization algorithms to plan the safe,
efficient missions. The aim is to determine the least-cost combination of a
safe landing zone (LZ) and global path plan, where avoiding terrain hazards for
the lander and rover minimizes cost. Computer vision methods were used to
identify surface craters, mounds, and rocks as obstacles. Multiple search
methods were investigated for the rover global path plan. Several combinatorial
optimization algorithms were implemented to select the shortest distance path
as the preferred mission plan. Simulations were run for a sample Google Lunar X
Prize mission. The result of this study is an optimization scheme that path
plans with the A* search method, and uses simulated annealing to select ideal
LZ-path- goal combination for the mission. Simulation results show the methods
are effective in minimizing the risk of hazards and increasing efficiency. This
paper is specific to a lunar mission, but the resulting architecture may be
applied to a large variety of planetary missions and rovers