Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Journal of the ACM (JACM)
A highly asynchronous minimum spanning tree protocol
Distributed Computing
Planning Algorithms
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Safe and Distributed Kinodynamic Replanning for Vehicular Networks
Mobile Networks and Applications
Algorithms and theory of computation handbook
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This work deals with the problem of planning in real-time, collision-free motions for multiple communicating vehicles that operate in the same, partially-observable environment. A challenging aspect of this problem is how to utilize communication so that vehicles do not reach states from which collisions cannot be avoided due to second-order motion constraints. This paper provides a distributed communication protocol for realtime planning that guarantees collision avoidance with obstacles and between vehicles. It can also allow the retainment of a communication network when the vehicles operate as a networked team. The algorithm is a novel integration of sampling-based motion planners with message-passing protocols for distributed constraint optimization. Each vehicle uses the motion planner to generate candidate feasible trajectories and the message-passing protocol for selecting a safe and compatible trajectory. The existence of such trajectories is guaranteed by the overall approach. Experiments on a distributed simulator built on a cluster of processors confirm the safety properties of the approach in applications such as coordinated exploration. Furthermore, the distributed protocol has better scalability properties when compared against typical priority-based schemes.