Integration of reactive, torque-based self-collision avoidance into a task hierarchy

Reactively dealing with self-collisions is an important requirement on multidegree-of-freedom robots in unstructured and dynamic environments. Classical methods to integrate respective algorithms into task hierarchies cause substantial problems: Either these unilateral safety constraints are permanently active, unnecessarily locking DOF for other tasks, or they get activated online and result in a discontinuous control law. We propose a new, reactive self-collision avoidance algorithm for highly complex robotic systems with a large number of DOF. In particular, configuration-dependent damping is imposed to dissipate undesired kinetic energy in a well-directed manner. Moreover, we merge the algorithm with a novel method to incorporate these unilateral constraints into a dynamic task hierarchy. Our approach both allows us to specifically limit the force/torque derivative to comply with physical constraints of the real robot and to prevent discontinuities in the control law while activating/deactivating the constraints. No redundancy is wasted. No comparable algorithms have been developed and implemented on a torque-controlled robot with such a level of complexity so far. The implementation of our generic solution on the multi-DOF humanoid Justin clearly validates the performance and demonstrates the real-time applicability of our synthetic approach. The proposed method can be used to contribute to whole-body controllers.