Damage Reduction Strategies for a Falling Humanoid Robot

Instability of humanoid robots is a common problem, especially given external disturbances or difficult terrain. Even with the robustness of most whole body controllers, instability is inevitable given the right conditions. When these unstable events occur they can result in costly damage to the robot potentially causing a cease of normal functionality. Therefore, it is important to study and develop methods to control a humanoid robot during a fall to reduce the chance of critical damage.

This thesis proposes joint angular velocity strategies to reduce the impact velocity resulting from a lateral, backward, or forward fall. These strategies were used on two and three link reduced order models to simulate a fall from standing height of a humanoid robot. The results of these simulations were then used on a full degree of freedom robot, Viginia Tech's humanoid robot ESCHER, to validate the efficacy of these strategies.

By using angular velocity strategies for the knee and waist joint, the reduced order models resulted in a decrease in impact velocity of the center of mass by 58%, 87%, and 74% for a lateral, backward, and forward fall respectively in comparison to a rigid fall using the same initial conditions. Best case angular velocity strategies were then developed for various initial conditions for each falling direction. Finally, these parameters were implemented on the full degree of freedom robot which showed results similar to those of the reduced order models.