Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces
This addresses the problem of legged robot navigation in tight environments for robotics applications, representing an incremental advance by applying existing algorithms to a new context.
The paper tackled enabling legged robots to adapt their walking posture for navigating confined spaces, resulting in a method that allowed a hexapod robot to navigate under 25cm overhangs, through 70cm gaps, and over 22cm obstacles in simulations and real-world tests like mining tunnels.
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 paper 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 optimisation algorithm CHOMP which creates smooth trajectories while avoiding obstacles. The proposed method has been tested in simulation and implemented on the hexapod robot Weaver, which is 33cm tall and 82cm wide when walking normally. We demonstrate navigating under 25cm overhanging obstacles, through 70cm wide gaps and over 22cm high obstacles in both artificial testing spaces and realistic environments, including a subterranean mining tunnel.