Perceptive whole-body planning for multilegged robots in confined spaces

Legged robots are exceedingly versatile and have the potential to navigate complex, confined spaces due to their many degrees of freedom. As a result of the computational complexity, there exist no online planners for perceptive whole-body locomotion of robots in tight spaces. In this paper, we present a new method for perceptive planning for multilegged robots, which generates body poses, footholds, and swing trajectories for collision avoidance. Measurements from an onboard depth camera are used to create a three-dimensional map of the terrain around the robot. We randomly sample body poses then smooth the resulting trajectory while satisfying several constraints, such as robot kinematics and collision avoidance. Footholds and swing trajectories are computed based on the terrain, and the robot body pose is optimized to ensure stable locomotion while not colliding with the environment. Our method is designed to run online on a real robot and generate trajectories several meters long. We first tested our algorithm in several simulations with varied confined spaces using the quadrupedal robot ANYmal. We also simulated experiments with the hexapod robot Weaver to demonstrate applicability to different legged robot configurations. Then, we demonstrated our whole-body planner in several online experiments both indoors and in realistic scenarios at an emergency rescue training facility. ANYmal, which has a nominal standing height of 80 cm and a width of 59 cm, navigated through several representative disaster areas with openings as small as 60 cm. Three-meter trajectories were replanned with 500 ms update times.

Automated summary

This paper presents a new method for perceptive planning for multilegged robots, which involves generating body poses, footholds, and swing trajectories for collision avoidance. The method computes footholds and swing trajectories based on the terrain to optimize the robot's body pose for stable locomotion while avoiding collisions.