CMP-IRRT*: A Perception-Assisted Height-Adaptive Planner for Quadruped Robots

Key points

• Quadruped robots can traverse low obstacles, but many 2D planning pipelines still model obstacles as binary occupied regions and rely on sampling-based search that can be inefficient under a limited budget.1
• Given a calibrated top-view RGB observation, the perception module estimates obstacle regions and converts depth predictions into a ground-relative height map.1
• Experiments on 2D planning benchmarks show that CMP-IRRT reduces explored nodes and iterations compared with classical and neural-guided baselines, and a controlled ablation supports the contribution of the Mamba-based guide.1
• In constructed traversability-aware scenarios, the proposed planner reduces path length by up to 16.3% when low obstacles are traversable, and a Unitree Go2 demonstration further shows executable bypassing and traversal behaviors.1

Sources (1)

• [1]CMP-IRRT*: A Perception-Assisted Height-Adaptive Planner for Quadruped Robots
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 05:33 PM

  We propose a perception-assisted height-adaptive planning framework based on CMP-IRRT*, a Channel Mamba PointNet-guided Informed RRT* planner.

  Quadruped robots can traverse low obstacles, but many 2D planning pipelines still model obstacles as binary occupied regions and rely on sampling-based search that can be inefficient under a limited budget.

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