Adaptive Source Localization Based Station Keeping of Autonomous Vehicles
KAUST DepartmentElectrical Engineering Program
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AbstractWe study the problem of driving a mobile sensory agent to a target whose location is specied only in terms of the distances to a set of sensor stations or beacons. The beacon positions are unknown, but the agent can continuously measure its distances to them as well as its own position. This problem has two particular applications: (1) capturing a target signal source whose distances to the beacons are measured by these beacons and broadcasted to a surveillance agent, (2) merging a single agent to an autonomous multi-agent system so that the new agent is positioned at desired distances from the existing agents. The problem is solved using an adaptive control framework integrating a parameter estimator producing beacon location estimates, and an adaptive motion control law fed by these estimates to steer the agent toward the target. For location estimation, a least-squares adaptive law is used. The motion control law aims to minimize a convex cost function with unique minimizer at the target location, and is further augmented for persistence of excitation. Stability and convergence analysis is provided, as well as simulation results demonstrating performance and transient behavior.
CitationGuler S, Fidan B, Dasgupta S, Anderson BDO, Shames I (2016) Adaptive Source Localization Based Station Keeping of Autonomous Vehicles. IEEE Transactions on Automatic Control: 1–1. Available: http://dx.doi.org/10.1109/tac.2016.2621764.
SponsorsThis work is supported by the Canadian NSERC Discovery Grant 116806. This work is supported in part by US NSF grants EPS-1101284, CNS-1329657, CCF-1302456, ONR grant N00014-13-1-0202 and under the Thousand Talents Program of the State and Shandong Province, administered by the Shandong Academy of Sciences, China.