Browsing by Subject "Multi-Agent Navigation"

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    Learning To Communicate for Coordinated Multi-Agent Navigation
    (2019-06) Hildreth, Dalton James
    This work presents a decentralized multi-agent navigation approach that allows agents to coordinate their motion through local communication. Our approach allows agents to develop their own emergent language of communication through an optimization process that simultaneously determines what agents say in response to their spatial observations and how agents interpret communication from others to update their motion. We apply our communication approach together with the TTC-Forces crowd simulation algorithm and show a significant decrease in congestion and bottle-necking of agents, especially in scenarios where agents benefit from close coordination. In addition to reaching their goals faster, agents using our approach show coordinated behaviors including greeting, flocking, following, and grouping.Furthermore, we observe that communication strategies optimized for one scenario often continue to provide time-efficient, coordinated motion between agents when applied to different scenarios.This suggests that the agents are learning to generalize strategies for coordination through their communication “language".
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    Online Action Selection Methods for Multi-Agent Navigation
    (2016-06) Godoy Del Campo, Julio
    In multi-agent navigation, agents have to move from their start positions to their goal locations while avoiding collisions with other agents and any static element in the environment. Existing methods either compute the motion of each agent centrally or allow each agent to compute its own motion. Using a central controller limits the number of agents that can be controlled in real time, while using a local method produces motions that are optimal locally but do not account for the motions of the other agents, producing inefficient global motions when many agents move in a crowded space. This dissertation proposes a set of online action selection methods that each agent uses to dynamically adapt its behavior to the local conditions. Specifically, we propose four approaches based on learning, planning, coordination and model inference to improve the global motions of a set of agents. These approaches are highly scalable because each agent makes its own decisions on how to move. We validate the approaches experimentally, with multiple simulations in a variety of environments and with different numbers of agents. When compared to other techniques, the proposed approaches produce motions that are more efficient and make better use of the space, allowing agents to reach their destinations faster.