Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Journal of the ACM (JACM)
An algorithm for distributed computation of a spanningtree in an extended LAN
SIGCOMM '85 Proceedings of the ninth symposium on Data communications
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
A highly asynchronous minimum spanning tree protocol
Distributed Computing
Planning Algorithms
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Proceedings of the 1st international conference on Robot communication and coordination
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Safe distributed motion coordination for second-order systems with different planning cycles
International Journal of Robotics Research
Maintaining team coherence under the velocity obstacle framework
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
| Hi-index | 0.00 |
This work deals with the problem of planning collision-free motions for multiple communicating vehicles that operate in the same, partially-observable environment in real-time. 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 initially shows how it is possible to provide theoretical safety guarantees with a priority-based coordination scheme. Safety means avoiding collisions with obstacles and between vehicles. This notion is also extended to include the retainment of a communication network when the vehicles operate as a networked team. The paper then progresses to extend this safety framework into a fully distributed communication protocol for real-time planning. The proposed algorithm integrates 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. The theoretical results have also been experimentally confirmed with a distributed simulator built on a cluster of processors and using applications such as coordinated exploration. Furthermore, experiments show that the distributed protocol has better scalability properties when compared against the priority-based scheme.