Game-Based Approximate Optimal Motion Planning for Safe Human-Swarm Interaction

Safety as a fundamental requirement for human-swarm interaction has attracted a lot of attention in recent years. Most existing approaches solve a constrained optimization problem at each time step, which has a high real-time requirement. To deal with this challenge, this article formulates the safe human-swarm interaction problem as a Stackerberg-Nash game, in which the optimization is performed over the entire time domain. The leader robot is supposed to be in a dominant position, interacting directly with the human operator to realize trajectory tracking and responsible for guiding the swarm to avoid obstacles. The follower robots always take their best responses to leader's behavior with the purpose of achieving the desired formation. Following the bottom-up principle, we first design the best-response controllers, that is, Nash equilibrium strategies, for the followers. Then, a Lyapunov-like control barrier function-based safety controller and a learning-based formation tracking controller for the leader are designed to realize safe and robust cooperation. We show that the designed controllers can make the robotic swarms move in a desired geometric formation following the human command and modify their motion trajectories autonomously when the human command is unsafe. The effectiveness of the proposed approach is verified through simulation and experiments. The experiment results further show that safety can still be guaranteed even when there exists a dynamic obstacle.