162,840 research outputs found
Distributed control of multi-robot systems using bifurcating potential fields
The distributed control of multi-robot systems has been shown to have advantages over conventional single robot systems. These include scalability, flexibility and robustness to failures. This paper considers pattern formation and reconfigurability in a multi-robot system using bifurcating potential fields. It is shown how various patterns can be achieved through a simple free parameter change. In addition the stability of the system of robots is proven to ensure that desired behaviours always occur
Safety Barrier Certificates for Heterogeneous Multi-Robot Systems
This paper presents a formal framework for collision avoidance in multi-robot
systems, wherein an existing controller is modified in a minimally invasive
fashion to ensure safety. We build this framework through the use of control
barrier functions (CBFs) which guarantee forward invariance of a safe set;
these yield safety barrier certificates in the context of heterogeneous robot
dynamics subject to acceleration bounds. Moreover, safety barrier certificates
are extended to a distributed control framework, wherein neighboring agent
dynamics are unknown, through local parameter identification. The end result is
an optimization-based controller that formally guarantees collision free
behavior in heterogeneous multi-agent systems by minimally modifying the
desired controller via safety barrier constraints. This formal result is
verified in simulation on a multi-robot system consisting of both cumbersome
and agile robots, is demonstrated experimentally on a system with a Magellan
Pro robot and three Khepera III robots.Comment: 8 pages version of 2016ACC conference paper, experimental results
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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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