# Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces

## TL;DR

This paper introduces a novel deformable bounding box model and planning strategy enabling legged robots to autonomously adapt their posture for navigating confined spaces, demonstrated through simulation and real-world tests.

## Contribution

The paper presents a deformable bounding box abstraction and integrated mapping and planning methods for autonomous posture adaptation in legged robots.

## Key findings

- Successfully navigated through tight spaces in simulation and real environments.
- Demonstrated obstacle avoidance and space adaptation on the Weaver hexapod robot.
- Achieved navigation under 25cm overhangs and through 70cm wide gaps.

## Abstract

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.