Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications
Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications
复制标题
DOI：
10.1007/978-1-84800-015-5
复制
发表时间：
2007-12
期刊：
影响因子：
--
通讯作者：
W. Ren;R. Beard
中科院分区：
文献类型：
--
作者：
W. Ren;R. Beard
最近在计算、通信、传感和致动的计算机化方面取得的进展使得设想大量自主车辆（空中、地面和水上）协同工作以实现目标变得可行。多车辆系统的协同控制在许多民用、国土安全和军事应用中具有潜在的影响。潜在的民用应用包括监测森林火灾、油田、管道和跟踪野生动物。潜在的国土安全应用包括边境巡逻和监控核电站周边。对于军事，应用包括监视，侦察和战斗损失评估。然而，对于所有这些应用，通信带宽和功率限制将排除集中式命令和控制。这本书解决了信息共识的问题，其中一个车队的车辆必须与它的邻居沟通，以达成一致的关键信息，使他们能够以协调的方式一起工作。这个问题特别具有挑战性，因为通信信道具有有限的范围并且经历衰落和丢失。研究车辆群中多辆车之间的信息流和共享对于理解车辆的协调运动起着重要作用。因此，一个关键的问题，合作控制是设计适当的分布式算法，使集团的车辆可以达成共识的共享信息，在有限的和不可靠的信息交换和动态变化的互动拓扑。我们对分布式一致性算法及其应用的兴趣是由我们在多车辆系统，特别是无人驾驶飞行器团队的协同控制方面的研究工作所激发的。飞行器在不断地移动，因此它们的通信能力也在动态地变化。此外，在目前涉及无人驾驶飞行器的军事场景中，像“捕食者”这样的大型资产可能具有双向通信能力，但微型飞行器可能只有接收命令的能力。因此，我们有动力研究分布式协调
Recent advances in miniaturizing of computing, communication, sensing, and actuation have made it feasible to envision large numbers of autonomous vehicles (air, ground, and water) working cooperatively to accomplish an objective. Cooperative control of multiple vehicle systems has potential impact in numerous civilian, homeland security, and military applications. Potential civilian applications include monitoring forest fires, oil fields, pipelines, and tracking wildlife. Potential homeland security applications include border patrol and monitoring the perimeter of nuclear power plants. For the military, applications include surveillance, reconnaissance, and battle damage assessment. However, for all of these applications, communication bandwidth and power constraints will preclude centralized command and control. This book addresses the problem of information consensus, where a team of vehicles must communicate with its neighbors to agree on key pieces of information that enable them to work together in a coordinated fashion. The problem is particularly challenging because communication channels have limited range and experience fading and dropout. The study of information flow and sharing among multiple vehicles in a group plays an important role in understanding the coordinated movements of these vehicles. As a result, a critical problem for cooperative control is to design appropriate distributed algorithms such that the group of vehicles can reach consensus on the shared information in the presence of limited and unreliable information exchange and dynamically changing interaction topologies. Our interest in distributed consensus algorithms and their applications was motivated by our research efforts in cooperative control of multiple vehicle systems and, in particular, teams of unmanned air vehicles. Air vehicles are constantly moving and consequently their ability to communicate is dynamically changing. In addition, in current military scenarios involving unmanned air vehicles, large assets like the Predator may have two-way communication capabilities, but micro air vehicles may have only the ability to receive commands. Therefore, we were motivated to study distributed coordination