[Paper Review] Optimized Mission Planning for Planetary Exploration Rovers

Alexander Lavin|arXiv (Cornell University)|Nov 1, 2015

Robotic Path Planning Algorithms11 references3 citations

TL;DR

This paper proposes an optimized mission planning framework for planetary rovers that integrates global navigation and metaheuristic optimization to select the safest, most efficient landing zone (LZ) and path. It uses A* for path planning and simulated annealing to minimize hazard risk and mission cost, validated via simulations of a lunar mission with significant reductions in terrain hazard exposure.

ABSTRACT

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.

Motivation & Objective

• To develop a coordinated mission planning system for planetary rovers that minimizes risk and cost during surface exploration.
• To identify the least-cost combination of a safe landing zone (LZ) and global path plan that avoids terrain hazards.
• To evaluate the effectiveness of integrating global navigation and metaheuristic optimization in autonomous planetary exploration.
• To demonstrate the scalability of the proposed architecture to diverse planetary missions beyond the Moon.

Proposed method

• Utilizes computer vision techniques to detect surface obstacles such as craters, mounds, and rocks from terrain data.
• Employs the A* search algorithm for global path planning to determine the shortest, safest path from the landing site to the goal.
• Applies simulated annealing as a metaheuristic to optimize the combined selection of landing zone and path, minimizing overall mission cost.
• Integrates hazard avoidance into the cost function by assigning higher costs to paths and landing zones near terrain obstacles.
• Simulates a sample mission based on the Google Lunar X Prize to evaluate performance under realistic constraints.
• Uses combinatorial optimization to compare multiple LZ-path-goal combinations and select the optimal configuration.

Experimental results

Research questions

• RQ1 What is the optimal combination of landing zone and path that minimizes mission cost while avoiding terrain hazards?
• RQ2 How effective is the integration of A* path planning with simulated annealing in reducing mission risk and improving efficiency?
• RQ3 To what extent can this framework be generalized across different planetary bodies and rover missions?
• RQ4 How do different obstacle detection and cost modeling strategies impact the final mission plan quality?

Key findings

• The proposed framework successfully identifies the least-cost LZ-path-goal combination using simulated annealing, significantly reducing hazard exposure.
• Simulation results demonstrate that the A* algorithm provides efficient and safe path planning under complex terrain conditions.
• The integration of computer vision for obstacle detection enables accurate identification of craters, mounds, and rocks as navigational hazards.
• The method reduces mission risk by prioritizing paths and landing zones with minimal proximity to terrain obstacles.
• The architecture is adaptable to various planetary missions, indicating strong potential for broader application beyond lunar exploration.