2023/10/10 by Viet-Anh Le, Vaishnav Tadiparthi, Le, Viet-Anh +11 · 1 citation
Computer Science · Engineering · #Reinforcement Learning in Robotics #Distributed Control Multi-Agent Systems #Evacuation and Crowd Dynamics
paper · pdf · doi:10.48550/arxiv.2310.06964
In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory model that forecasts pedestrians' trajectories. We formulate the local MPC formulation for each individual robot that includes both individual and shared objectives, in which the latter encourages the emergence of coordination among robots. Next, we consider the multi-robot navigation and human-robot interaction, respectively, as a potential game and a two-player game, then employ an iterative best response approach to solve the resulting optimization problems in a centralized and distributed fashion. Finally, we demonstrate the effectiveness of coordination among robots in simulated crowd navigation.