Contact Model Fusion for Event-Based Locomotion in Unstructured Terrains

As legged robots are sent into unstructured environments, the ability to robustly manage contact transitions will be a critical skill. This paper introduces an approach to probabilistically fuse contact models, managing uncertainty in terrain geometry, dynamic modeling, and kinematics to improve the robustness of contact initiation at touchdown. A discrete-time extension of the generalized-momentum disturbance observer is presented to increase the accuracy of proprioceptive force control estimates. This information is fused with other contact priors under a framework of Kalman Filtering to increase robustness of the method. This approach results in accurate contact detection with 99.3 % accuracy and a small 4-5ms delay. Using this new detector, an Event-Based Finite State Machine is implemented to deal with unexpected early and late contacts. This allows the robot to traverse cluttered environments by modifying the control actions for each individual leg based on the estimated contact state rather than adhering to a rigid time schedule regardless of actual contact state. Experiments with the MIT Cheetah 3 robot show the success of both the detection algorithm, as well as the Event-Based FSM while making unexpected contacts during trotting.

I. Introduction

Legged robots offer the promise to negotiate difficult terrain environments beyond the reach of wheeled or tracked vehicles. As robots begin to navigate such terrains, an increasing burden is placed on control systems that are designed to manage contact transitions and achieve solid footholds amidst incomplete and uncertain terrain knowledge. Contact force sensing and its associated feedback mechanisms will playa crucial role in this process. However, conventional force estimation is noisy, with force sensing technology notoriously fragile. Thus, an ability to reliably manage contact transitions through proprioceptive sensing alone is a desirable skill for rough terrain locomotion.