Quadruped Obstacle Avoidance and Footstep Planning with Distributed Low-cost Time-of-Flight Sensors

2026-09-25

AI Summary

This study addresses the high cost, substantial power consumption, and near-field blind spots associated with reliance on depth cameras and LiDAR for terrain perception in quadruped robots. We propose a novel perception architecture based on distributed, low-cost time-of-flight (ToF) sensors. By optimizing the spatial layout of these sensors to eliminate near-field blind spots and integrating local terrain mapping with foothold planning algorithms, our approach effectively substitutes conventional expensive sensing modalities. Experimental evaluations conducted on the ANYmal platform demonstrate that the proposed system achieves centimeter-level mapping accuracy while significantly reducing hardware costs and energy consumption. Furthermore, it reliably supports near-field obstacle avoidance and stable locomotion control for quadruped robots.

Abstract

Quadruped robots typically rely on depth cameras and LiDAR sensors to map their local environment. However, these sensors have limited close-range coverage, are relatively expensive, and consume significant power. This study investigates whether distributed Time-of-Flight (ToF) sensors can serve as a low-cost alternative to depth cameras for near-field terrain mapping for locomotion and local navigation. We designed a distributed ToF sensing architecture for the ANYbotics ANYmal quadruped, assessed its environment reconstruction accuracy, and benchmarked it against depth cameras for terrain mapping and obstacle avoidance. Distributing these sensors around the robot can also avoid the blind spots of traditional sensors. Our results show that, despite their low resolution and higher measurement noise, distributed ToF sensors can support reliable perceptual locomotion with centimeter-level local mapping accuracy. The proposed sensing strategy provides sufficient geometric information for near-field obstacle avoidance and footstep planning, at substantially lower cost, energy consumption, and system complexity than depth cameras.

Problem

• Quadruped robots
• Obstacle avoidance
• Footstep planning
• Time-of-Flight sensors
• Near-field terrain mapping

Innovation

• Distributed ToF Sensors
• Quadruped Robot
• Obstacle Avoidance
• Footstep Planning
• Near-field Terrain Mapping