464 research outputs found
Stackelberg Game-Theoretic Trajectory Guidance for Multi-Robot Systems with Koopman Operator
Guided trajectory planning involves a leader robotic agent strategically
directing a follower robotic agent to collaboratively reach a designated
destination. However, this task becomes notably challenging when the leader
lacks complete knowledge of the follower's decision-making model. There is a
need for learning-based methods to effectively design the cooperative plan. To
this end, we develop a Stackelberg game-theoretic approach based on Koopman
operator to address the challenge. We first formulate the guided trajectory
planning problem through the lens of a dynamic Stackelberg game. We then
leverage Koopman operator theory to acquire a learning-based linear system
model that approximates the follower's feedback dynamics. Based on this learned
model, the leader devises a collision-free trajectory to guide the follower,
employing receding horizon planning. We use simulations to elaborate the
effectiveness of our approach in generating learning models that accurately
predict the follower's multi-step behavior when compared to alternative
learning techniques. Moreover, our approach successfully accomplishes the
guidance task and notably reduces the leader's planning time to nearly half
when contrasted with the model-based baseline method
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