Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces

Legged robots have the ability to adapt their walking posture to navigate confined spaces due to their high degrees of freedom. However, this has not been exploited in most common multilegged platforms. This letter presents a deformable bounding box abstraction of the robot model, with accompanying mapping and planning strategies, that enable a legged robot to autonomously change its body shape to navigate confined spaces. The mapping is achieved using robot-centric multi-elevation maps generated with distance sensors carried by the robot. The path planning is based on the trajectory optimization algorithm CHOMP that creates smooth trajectories while avoiding obstacles. The proposed method has been tested in simulation and implemented on the hexapod robot Weaver, which is 33 cm tall and 82 cm wide when walking normally. We demonstrate navigating under 25 cm overhanging obstacles, through 70 cm wide gaps and over 22 cm high obstacles in both artificial testing spaces and realistic environments, including a subterranean mining tunnel.