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RAPF: Efficient path planning for lunar microrovers

Thomas Manteaux, David Rodríguez-Martínez, Raj Thilak Rajan

Abstract

Efficient path planning is key for safe autonomous navigation over complex and unknown terrains. Lunar Zebro (LZ), a project of the Delft University of Technology, aims to deploy a compact rover, no larger than an A4 sheet of paper and weighing not more than 3 kilograms. In this work, we introduce a Robust Artificial Potential Field (RAPF) algorithm, a new path-planning algorithm for reliable local navigation solution for lunar microrovers. RAPF leverages and improves state of the art Artificial Potential Field (APF)-based methods by incorporating the position of the robot in the generation of bacteria points and considering local minima as regions to avoid. We perform both simulations and on field experiments to validate the performance of RAPF, which outperforms state-of-the-art APF-based algorithms by over 15% in reachability within a similar or shorter planning time. The improvements resulted in a 200% higher success rate and 50% lower computing time compared to the conventional APF algorithm. Near-optimal paths are computed in real-time with limited available processing power. The bacterial approach of the RAPF algorithm proves faster to execute and smaller to store than path planning algorithms used in existing planetary rovers, showcasing its potential for reliable lunar exploration with computationally constrained and energy constrained robotic systems.

RAPF: Efficient path planning for lunar microrovers

Abstract

Efficient path planning is key for safe autonomous navigation over complex and unknown terrains. Lunar Zebro (LZ), a project of the Delft University of Technology, aims to deploy a compact rover, no larger than an A4 sheet of paper and weighing not more than 3 kilograms. In this work, we introduce a Robust Artificial Potential Field (RAPF) algorithm, a new path-planning algorithm for reliable local navigation solution for lunar microrovers. RAPF leverages and improves state of the art Artificial Potential Field (APF)-based methods by incorporating the position of the robot in the generation of bacteria points and considering local minima as regions to avoid. We perform both simulations and on field experiments to validate the performance of RAPF, which outperforms state-of-the-art APF-based algorithms by over 15% in reachability within a similar or shorter planning time. The improvements resulted in a 200% higher success rate and 50% lower computing time compared to the conventional APF algorithm. Near-optimal paths are computed in real-time with limited available processing power. The bacterial approach of the RAPF algorithm proves faster to execute and smaller to store than path planning algorithms used in existing planetary rovers, showcasing its potential for reliable lunar exploration with computationally constrained and energy constrained robotic systems.
Paper Structure (15 sections, 21 equations, 3 figures, 3 tables, 1 algorithm)

This paper contains 15 sections, 21 equations, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related work
  4. Artificial Potential Field (APF) algortihms
  5. RVF
  6. CRBAPF*
  7. Robust and Lightweight APF
  8. Simulations
  9. Virtual environment
  10. Performance metrics
  11. Simulation results
  12. Field validation
  13. Testbed description and testing approach
  14. Testing results and discussion
  15. Summary

Figures (3)

  • Figure 1: a) Field test layout. b) Potential map computed by the RAPF algorithm. Dark blue represents low-potential regions while red represents high-potential regions. The orange line shows the computed path, black line defines the ground truth, and the yellow cross shows where a local minimum was found. c) Lunar Zebro prototype used for testing
  • Figure 2: Mean computed path (red) , mean walked path (black) and standard deviation of the mean walked path (gray) for 10 repetitions for two different scenarios (Path1 & Path 2)
  • Figure 3: Potential map, computed path (orange), and path followed by the rover (black). Left: CRBAPF. Right: RAPF