Decentralized Cooperative Search in UAV's Using Opportunistic Learning

Yang, Yanli; Minai, Ali A.; Polycarpou, Marios M.

doi:10.2514/6.2002-4590

Cited by 37 publications

(54 citation statements)

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“…Maximizing this function, we find the N best actions to minimize the time required to find a target. The last paper, [37], focuses on designing a multi-objective utility function with some heuristics to learn the locations of some targets in a search space. The idea of an expected discounted reward is introduced, defining it as the mean of the rewards that the UAV could possibly obtain in the future exploring region.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper we propose a decision making algorithm for the search problem that incorporates the advantages of decentralized continuous optimization [14,27,6] for computing actions N-steps ahead, and the myopicity reduction benefits of including an expected reward as in the discrete approaches [37,38]. This is achieved by estimating heuristically the expected observation that is related to the future UAV team reward.…”

Section: Introductionmentioning

confidence: 99%

“…This reduces the vulnerability to central server failure, which further enhances the overall system robustness. In particular, there is an active research community proposing mobile sensor decision making solutions for target search [37,5,12,4,16,27,30,20,13,21,7] and tracking [15,8,17] problems. These solutions intrinsically exploit the action-perception loop [28,10] to design autonomous cognitive agents based on recent computational and decision models of the human brain [1,24].…”

Section: Introductionmentioning

confidence: 99%

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Multi-UAV target search using decentralized gradient-based negotiation with expected observation

Lanillos

Gan

Besada-Portas

et al. 2014

Information Sciences

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-UAV target search using decentralized gradient-based negotiation with expected observation

Lanillos

Gan

Besada-Portas

et al. 2014

Information Sciences

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“…Yang et al (2002aYang et al ( , b, 2004, Ghose (2003, 2004a, b), Polycarpou et al (2001), Flint et al (2002a, and Bertuccelli and How (2005) Bertuccelli and How (2005) introduced the beta distribution to compute target existence probability.…”

mentioning

confidence: 99%

Waypoint Planning Algorithm Using Cost Functions for Surveillance

Lim¹,

Bang²

2010

International Journal of Aeronautical and Space Sciences

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This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

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“…The first step in the cooperative search path planning decision process is to model the environment in a suitable manner for autonomous route planning. Some of these methods have been proposed recently in [5], [9], and in previous work on this topic that has been evolving in several other papers by the authors of this work: [3] and [4]. However, this paper differs from the previous work by creating an formal software architecture.…”

Section: Introductionmentioning

confidence: 99%

Cooperative control for UAV's searching risky environments for targets

Flint

Fernández-Gaucherand

Polycarpou

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475)

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The public repoTting burden for this collection of information is estinuted to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, artd completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of infonnalion, including suggestions for reducing this burden, AFRLA^ACA SPONSORING/MONITORING AGENCY REPORT NUMBER(S)AFRL-VA-WP-TP-2003-304 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTESTo be presented at the Conference on Decision and Control, Maui, HI, 9-12 Dec 03.© 2003 IEEE. This work is copyrighted. This work, resulting from Department of Air Force contract number F33615-01-C-3150, has been submitted for publication in the Proceedings of the 2003 IEEE Conference on Decision and Control. If published, IEEE may assert copyright. If so, the United States has for itself and others acting on its behalf an unlimited, nonexclusive, irrevocable, paid-up royalty-free worldwide license to use for its purposes. ABSTRACT (Maximum 200 Words)A software architecture is presented, which introduces several agents which focus on different aspects of path planning for multiple autonomous unmanned aerial vehicles (UAV's) that are searching an uncertain and threatening environment for targets. One agent models threats in the environment. Another develops a model of the environment that allows targets to be defined by individual probability distribution. Lastly, an agent is presented that utilizes the information from the other agents to generate a near optimal path plan using a Dynamic Programming algorithm. Abstract A software architecture is presented, which introduces several agents which focus on different aspects of path planning for multiple autonomous unmanned aerial vehicles (UAV's) that are searching an uncertain and threatening environment for targets. One agent models threats in the environment. Another develops a model of the environment that allows targets to be defined by individual probability distribution. Lastly, an agent is presented that utilizes the information from the other agents to generate a near optimal path plan using a Dynamic Programming algorithm.

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Decentralized Cooperative Search in UAV's Using Opportunistic Learning

Cited by 37 publications

References 0 publications

Multi-UAV target search using decentralized gradient-based negotiation with expected observation

Multi-UAV target search using decentralized gradient-based negotiation with expected observation

Waypoint Planning Algorithm Using Cost Functions for Surveillance

Cooperative control for UAV's searching risky environments for targets

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