Whole-body collision avoidance control design using quadratic programming with strict and soft task priorities. (April 2020) - Wang, Yuquan; Wang, Lihui
Highlights: We presented a whole-body collision avoidance control design using quadratic optimization where both the strict and soft task priorities are specified. We use strict constraints to guarantee the collision avoidance task while minimizing other task space objectives using soft task priorities. We validated the results using experiments performed on a dual-arm robot. Published C++ and Python simulation packages of the proposed multi-objective QP controller. We presented the details of the QP controller formulation and the detailed tasks and constraints specification, such as orientation task, velocity task and the admittance task Abstract: Despite safe mechanical design is necessary for the collaborative robots, we can not underestimate the importance of active safety due to a multi-objective control design. Active safety not only complements the mechanical compliance but also enables classical industrial robots the ability to fulfill additional task-space objectives. Using the gradient of the collision avoidance task as hard constraints of a quadratic programming (QP) controller, we assign strict priority to avoid collisions and specify other QP controller objectives with soft task priorities. Through experiments performed on a dual-arm robot, we show that the proposed solution is able to generate safe robot motion that fulfills the task specifications while keeping the feasibility of the underlying quadratic optimization problem.