35,912 research outputs found
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
Fast multipole networks
Two prerequisites for robotic multiagent systems are mobility and
communication. Fast multipole networks (FMNs) enable both ends within a unified
framework. FMNs can be organized very efficiently in a distributed way from
local information and are ideally suited for motion planning using artificial
potentials. We compare FMNs to conventional communication topologies, and find
that FMNs offer competitive communication performance (including higher network
efficiency per edge at marginal energy cost) in addition to advantages for
mobility
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