Rule-Based Cooperative Collision Avoidance Using Decentralized Model Predictive Control

摘要

A rule-based decentralized model predictive control (DMPC) approach is employed to address the collision avoidance problem of multiple moving vehicles. Every vehicle uses model predictive control (MPC) to plan its trajectory towards its assigned target. The neighboring vehicles exchange their predicted trajectories at each sample time to predict the conflicts. Then, decentralized coordination and cooperation is performed to resolve the predicted conflicts. The Coordination part consists of online recalculation of the directed interaction graph topology to label conflicting vehicles as leader or follower. Between two conflicting vehicles, the vehicle with higher speed is labeled as leader and the other as follower. The Cooperation part consists of two simple rules, referred to as Heading-rule and Velocity-rule, which are often employed by human pedestrians to avoid potential collisions. The Heading-rule is first employed by both leader and follower to resolve the conflict. If it is not feasible to resolve the conflict by Heading-rule then the Velocity-rule is employed to decelerate the follower and accelerate the leader until the conflict is resolved. Numerous simulations of a team of unicycles are used to illustrate the proposed approach.