Quadruped Obstacle Avoidance and Footstep Planning with Distributed Low-cost Time-of-Flight Sensors
Authors: Giammarco Caroleo, Timothée Mahamoodally, Matteo Manzardo, Jin Jin, Marco Pontin, Matias Mattamala, Renato Vidoni, Perla Maiolino, +1 more
Organizations: Oxford Robotics Institute, University of Oxford, UK. · Free University of Bozen-Bolzano, Italy. · University of Edinburgh, UK. · University of Udine, Italy.
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.
Figures & tables
Fig. 1 : This paper studies time-of-flight (ToF) sensors for quadrupedal locomotion and navigation. Mounted on the front shell of ANYmal D, they provide near-field perception for elevation mapping, obstacle avoidance, and footstep planning.
Fig. 2 : The 3D-printed front shell embedding the ToF sensors. The shell integrating 8 VL53L5CX is highlighted. The inset view shows the custom PCB designed for the sensing unit.
Fig. 3 : Field of View of the ANYmal’s ToF sensors. (a) Shaded pyramids representing the FoV of each ToF . (b) Azimuth–elevation coverage of the eight ToF s compared against the depth cameras. (c) The FoV of the depth cameras and the ToF s when projected on the ground plane.
Fig. 4 : System-level integration of ToFs. We show the ANYmal D and handheld reconstruction setup.
Fig. 5 : Accumulated point cloud reconstruction results of an office using LiDAR (left) and ToF (right). The point clouds are colorized by surface normal in the z-direction.
Fig. 6 : Comparison between the ToF surface reconstruction and the LiDAR mesh using three meshing methods. All views are colorized by surface normal in the z-direction.
Fig. 7 : Exp 2 – Footstep Planning on Railway Sleepers. Top: ANYmal D traversing an obstacle course consisting of three 13 cm railway sleepers arranged in line. Bottom: representative elevation maps generated using depth sensing (left) and distributed ToF sensing (right).
Fig. 8 : Exp 2 – Footstep Planning on an Irregular Indoor Staircase. Top: indoor staircase experiment with ANYmal D. Bottom: representative elevation maps generated using depth sensing (left) and distributed ToF sensing (right).
Fig. 9 : Exp 2 – Footstep Planning on an Outdoor Staircase. Top: ANYmal D during the outdoor staircase experiment. Bottom: representative elevation maps generated using depth sensing (left) and distributed ToF sensing (right).
Fig. 10 : Simulation environment with navigation paths of increasing complexity used for obstacle avoidance.
Fig. 11 : Real world obstacle-avoidance experiments under three difficulty levels. Columns correspond to easy, medium, and hard obstacle-course layouts. The top row shows the obstacle layouts together with the robot trajectories, the middle row shows the corresponding depth-camera-based perception, and the bottom row shows the corresponding ToF-based perception.
Course
Completion Time [s]
Path Length [m]
Depth
ToF
Depth
ToF
Low difficulty
63.8
63.2
35.0
34.2
Medium difficulty
47.5
55.4
27.2
28.8
Hard difficulty
71.8
84.6
28.5
30.5
TABLE III : Performance of obstacle avoidance task for both depth camera and ToF configurations.
Course
Number of Sensors Remaining
8
7
6
5
Low difficulty
100
100
100
92.5
Medium difficulty
100
97.5
97.5
85.0
Hard difficulty
100
95.0
95.0
67.5
TABLE IV : Success rate (%) of simulated obstacle avoidance when progressively removing ToF sensors (from a starting point of 8).
Course
Depth
ToF
Time [s]
Collisions
Fail.
Time [s]
Collisions
Fail.
Easy
29.7
0.0
0%
27.5
0.2
0%
Medium
32.9
0.1
0%
37.0
0.56
10%
Hard
66.1
1.5
20%
71.4
1.63
20%
TABLE V : Obstacle-avoidance performance across three difficulty levels, repeated over 10 trials. Completion time is averaged over successful trials only. We report the average number of collisions of the ANYmal D’s legs with an obstacle for runs that still reached the goal. Failures are reported out of 10 trials.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · Yunshenchu Technology Co., Ltd., Hangzhou, China · Hangzhou Public Library, Hangzhou, China +1
School of Instrument Science and Engineering, Southeast University, Nanjing, China. · School of Electrical Engineering, Southeast University, Nanjing, China. · School of Mechanical Engineering, Southeast University, Nanjing, China.