Collision detection, isolation and identification for humanoids

Abstract

High-performance collision handling, which is divided into the five phases detection, isolation, estimation, classification and reaction, is a fundamental robot capability for safe and sensitive operation/interaction in unknown environments. For complex humanoid robots collision handling is obviously significantly more complex than for classical static manipulators. In particular, the robot stability during the collision reaction phase has to be carefully designed and relies on high fidelity contact information that is generated during the first three phases. In this paper, a unified realtime algorithm is presented for determining unknown contact forces and contact locations for humanoid robots based on proprioceptive sensing only, i.e. joint position, velocity and torque, as well as force/torque sensing along the structure. The proposed scheme is based on nonlinear model-based momentum observers that are able to recover the unknown contact forces and the respective locations. The dynamic loads acting on internal force/torque sensors are also corrected based on a novel nonlinear compensator. The theoretical capabilities of the presented methods are evaluated in simulation with the Atlas robot. In summary, we propose a full solution to the problem of collision detection, collision isolation and collision identification for the general class of humanoid robots.