.png){kind=icon}
.png){kind=icon}
Return
Distributed Connectivity Preserving Target Tracking With Random Sensing
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2018.2867594.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A networked multiagent system is tasked to cooperatively track a mobile target, where agents experience random loss of sensing due to occlusions resulting from the target moving in a complex environment. A directed random graph is used to model the time-varying availability of the target states to agents, where the connection of the directed edge in the graph is assumed to be probabilistic and evolves according to a two-state Markov Model. An almost sure consensus algorithm is developed for all agents to achieve consensus on the target position. Due to limited communication capabilities (i.e.. the agents can only communicate within a certain range), agent motion may result in a disconnected communication network, leading to the failure of consensus to the target states. Motivated to preserve the graph connectivity of the position-dependent communication network, an algebraic-connectivity-based distributed motion controller is developed to ensure that the communication network remains connected during cooperative target tracking. Compared to existing results, our approach allows agents to break existing links when necessary as long as the algebraic connectivity remains positive, which provides more motion freedom for agents in mission operation. Moreover, our approach only requires information exchange within two-hop neighbors to preserve the network connectivity, which eliminates the need of iterative estimation of the algebraic connectivity.
Keywords:
Distributed control
network connectivity
random graphs
target tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
View more organizations