Online Bipedal Locomotion Adaptation for Stepping on Obstacles Using a Novel Foot Sensor

TL;DR

This paper introduces a novel, cost-effective foot sensor for bipedal robots that enables online adaptation and obstacle negotiation, significantly reducing impact forces during locomotion on unseen inclined obstacles.

Contribution

The paper presents a new predictive foot sensor and control architecture that improves obstacle detection and impact mitigation in bipedal locomotion.

Findings

Successful walking on unseen 12-degree inclined obstacles.

Significant reduction in foot-ground impact forces.

Effective integration of the sensor with existing control systems.

Abstract

In this paper, we present a novel control architecture for the online adaptation of bipedal locomotion on inclined obstacles. In particular, we introduce a novel, cost-effective, and versatile foot sensor to detect the proximity of the robot's feet to the ground (bump sensor). By employing this sensor, feedback controllers are implemented to reduce the impact forces during the transition of the swing to stance phase or steeping on inclined unseen obstacles. Compared to conventional sensors based on contact reaction force, this sensor detects the distance to the ground or obstacles before the foot touches the obstacle and therefore provides predictive information to anticipate the obstacles. The controller of the proposed bump sensor interacts with another admittance controller to adjust leg length. The walking experiments show successful locomotion on the unseen inclined obstacle without reducing the locomotion speed with a slope angle of 12. Foot position error causes a hard impact with the ground as a consequence of accumulative error caused by links and connections' deflection (which is manufactured by university tools). The proposed framework drastically reduces the feet' impact with the ground.