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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Williams, R.K. Sukhatme, G.S. |
| Copyright Year | 2012 |
| Description | Author affiliation: Departments of Electrical Engineering and Computer Science at the University of Southern California, Los Angeles, 90089 USA (Williams, R.K.; Sukhatme, G.S.) |
| Abstract | In this paper we consider a probabilistic method for mapping a spatial process over a distributed multi-agent system and a coordinated level curve tracking algorithm for adaptive sampling. As opposed to assuming the independence of spatial features (e.g. an occupancy grid model), we adopt a novel model of spatial dependence based on the grid-structured Markov random field that exploits spatial structure to enhance mapping. The multi-agent Markov random field framework is utilized to distribute the model over the system and to decompose the problem of global inference into local belief propagation problems coupled with neighbor-wise inter-agent message passing. A Lyapunov stable control law for tracking level curves in the plane is derived and a method of gradient and Hessian estimation is presented for applying the control in a probabilistic map of the process. Simulation results over a real-world dataset with the goal of mapping a plume-like oceanographic process demonstrate the efficacy of the proposed algorithms. Scalability and complexity results suggest the feasibility of the approach in realistic multi-agent deployments. |
| Starting Page | 1125 |
| Ending Page | 1130 |
| File Size | 2105631 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314039 |
| ISSN | 10504729 |
| e-ISBN | 9781467314053 |
| e-ISBN | 9781467314046 |
| DOI | 10.1109/ICRA.2012.6224689 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Probabilistic logic Markov processes Multiagent systems Message passing Process control Computational modeling Ocean temperature |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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