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Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data

Shreya Santra, Kentaro Uno, Gen Kudo, Kazuya Yoshida

TL;DR

This work tackles autonomous coverage planning for lunar micro-rovers operating under limited sensing and computation in unknown 3D terrain. It introduces a risk-aware, myopic CPP that fuses global DEM data with local sensor information, augmented by SLAM-based mapping and a Bug-algorithm for obstacle avoidance. Key contributions include a DEM-informed cost function, an energy-aware traversal model, and comprehensive sim-to-real validation using a CLOVER rover in both simulated and outdoor environments. The results demonstrate efficient coverage with low energy and computational cost, supporting scalable, autonomous lunar exploration with small rovers and informing future multi-robot missions.

Abstract

This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data with local topographic features into motion cost calculations. The algorithm also integrates localization and mapping to update the rover's pose and map the environment. The resulting environment map's accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were conducted to validate the algorithm's efficacy in sim-to-real scenarios. The results showed that the algorithm could achieve high coverage with low energy consumption and computational cost, while incrementally exploring the terrain and avoiding obstacles. This study contributes to the advancement of path planning methodologies for space exploration, paving the way for efficient, scalable and autonomous exploration of lunar environments by small rovers.

Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data

TL;DR

This work tackles autonomous coverage planning for lunar micro-rovers operating under limited sensing and computation in unknown 3D terrain. It introduces a risk-aware, myopic CPP that fuses global DEM data with local sensor information, augmented by SLAM-based mapping and a Bug-algorithm for obstacle avoidance. Key contributions include a DEM-informed cost function, an energy-aware traversal model, and comprehensive sim-to-real validation using a CLOVER rover in both simulated and outdoor environments. The results demonstrate efficient coverage with low energy and computational cost, supporting scalable, autonomous lunar exploration with small rovers and informing future multi-robot missions.

Abstract

This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data with local topographic features into motion cost calculations. The algorithm also integrates localization and mapping to update the rover's pose and map the environment. The resulting environment map's accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were conducted to validate the algorithm's efficacy in sim-to-real scenarios. The results showed that the algorithm could achieve high coverage with low energy consumption and computational cost, while incrementally exploring the terrain and avoiding obstacles. This study contributes to the advancement of path planning methodologies for space exploration, paving the way for efficient, scalable and autonomous exploration of lunar environments by small rovers.
Paper Structure (17 sections, 6 equations, 8 figures, 1 table)

This paper contains 17 sections, 6 equations, 8 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. PROBLEM FORMULATION
  4. Coverage Cost Function
  5. Digital Elevation Model
  6. Energy Consumption
  7. SIMULATION EVALUATION
  8. Modelling of Rover Testbed
  9. Modelling of Lunar Terrain
  10. Integration of Bug Algorithm
  11. SLAM Implementation
  12. SIM-TO-REAL EXPERIMENTS
  13. Outdoor Tests Results
  14. Terrain Reconstruction and Off-line Path Planning
  15. Real-Time Path Planning
  16. ...and 2 more sections

Figures (8)

  • Figure 1: Concept of myopic coverage path planning by means of global and local level information for lunar rovers. A rover assigned per grid explores entirely to visit all the cell.
  • Figure 2: Global and Local Path Planning of Chang'e 4 Yutu-2 rover ©CNSA
  • Figure 3: Grid representation of exploration area.
  • Figure 4: CPP results by changing the weight of cost function: with ($\beta = 0.7$) and without ($\beta = 0$) DEM based cost term.
  • Figure 5: Myopic path planning using bug algorithm and SLAM in the simulator.
  • ...and 3 more figures