Joint Communication and Motion Planning for Cobots
This addresses safety and efficiency challenges for collaborative robots working with humans, representing an incremental improvement by integrating communication into motion planning.
The paper tackles the problem of robots moving among humans in co-working scenarios by developing a joint communication and motion planning framework that selects communication signals and computes motion plans, resulting in reduced conflicts and resolved deadlocks as shown in theoretical and simulator-based evaluations.
The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation robot behavior. Movement among humans is one of the most fundamental -- and yet critical -- problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks.