Model predictive collaborative motion planning and control of mobile robots including safety aspects

One main problem in multi-robot systems is the coordinated navigation of the autonomous robots. Hereby, the robots have to fulfill their respective tasks while avoiding collisions with other moving robots. In addition, problem-specific differential constraints like limitations of the velocities and accelerations have to be considered. Coordinated navigation of such a multi-robot system therefore has to combine contradicting aspects like efficient task accomplishment and the fulfillment of safety and problem-specific constraints in parallel. This work focuses on a two-level model predictive optimizing approach. On a global long-term level, simple dynamic models of the robots are used to compute optimal paths under differential constraints where a safety distance between all robots is achieved. Since many uncertainties and unforeseen events could occur, all robots are additionally using a nonlinear model predictive control approach on a local real-time level. This control approach solves the path following and the collision avoidance problem in parallel while also considering differential constraints of the single robots.