4,258 research outputs found
Position and Orientation Based Formation Control of Multiple Rigid Bodies with Collision Avoidance and Connectivity Maintenance
This paper addresses the problem of position- and orientation-based formation
control of a class of second-order nonlinear multi-agent systems in a D
workspace with obstacles. More specifically, we design a decentralized control
protocol such that each agent achieves a predefined geometric formation with
its initial neighbors, while using local information based on a limited sensing
radius. The latter implies that the proposed scheme guarantees that the
initially connected agents remain always connected. In addition, by introducing
certain distance constraints, we guarantee inter-agent collision avoidance as
well as collision avoidance with the obstacles and the boundary of the
workspace. The proposed controllers employ a novel class of potential functions
and do not require a priori knowledge of the dynamical model, except for
gravity-related terms. Finally, simulation results verify the validity of the
proposed framework
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
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