Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 34, Issue 1, Pages 62-80Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2017.2766265
Keywords
Mobile robots; motion planning; nonlinear control systems; unmanned autonomous vehicles
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Funding
- Army Research Office [W911NF-14-1-0461]
- National Science Foundation [CMMI-1200321, IIS-1426961]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1426961] Funding Source: National Science Foundation
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Although a number of solutions exist for the problems of coverage, search, and target localization-commonly addressed separately-whether there exists a unified strategy that addresses these objectives in a coherent manner without being application specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model-predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real time on computationally limited hardware platforms.
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