Multi-UAV target search using explicit decentralized gradient-based negotiation

Gan, Seng Keat; Sukkarieh, Salah

doi:10.1109/icra.2011.5979704

Cited by 64 publications

(61 citation statements)

References 10 publications

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“…The above optimization problem can be solved using a gradient-based approach, which only requires the partial derivatives of the utility function with respect to each action in the action vector @J h @v k M and iteratively updates the action vector with steepest descent [27,14].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…It should be noted that each UAV can have a different sensor model, but to simplify the problem formulation, we have chosen the range sensor model in [14] for all sensing platforms. This sensor model represents the likelihood of producing a target detection event D from the observation z at time step k, if the sensor is at state s k and the target is placed at position s. This sensor model (a.k.a.…”

Section: Sensor Modelmentioning

confidence: 99%

“…Besides, the partial derivatives @J h @v k M can be computed directly by using the explicit formulation previously developed in [14].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…We provide a performance comparison between our proposal (NDH) and the local controller that only uses the short term non-detection reward (ND) proposed in [14].…”

Section: Multi-uav Coordinationmentioning

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%

See 4 more Smart Citations

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

Lanillos

Gan

Besada-Portas

et al. 2014

Information Sciences

Self Cite

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Section: Optimization Algorithmmentioning

confidence: 99%

Section: Sensor Modelmentioning

confidence: 99%

“…Besides, the partial derivatives @J h @v k M can be computed directly by using the explicit formulation previously developed in [14].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…We provide a performance comparison between our proposal (NDH) and the local controller that only uses the short term non-detection reward (ND) proposed in [14].…”

Section: Multi-uav Coordinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Lanillos

Gan

Besada-Portas

et al. 2014

Information Sciences

Self Cite

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Adaptive Action for Multi‐Agent Persistent Coverage

Franco¹,

López‐Nicolás²,

Sagüés³

et al. 2015

Asian Journal of Control

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Persistent coverage with a team of agents refers to the task of covering an area where the coverage degrades with time. This is a dynamic task as opposed to the deployment problem. A key issue here is the coverage degradation that prevents the complete coverage fulfilment and requires persistence in the action. We propose a new method for persistent coverage where the agents' actions are computed in a partially distributed manner. The contribution is a control strategy based on variable coverage action and variable coverage range of the agents. This control provides adaptive behaviour in terms of actuator power and actuator domain in order to reduce the coverage error and energy consumption. The proposal is tested in simulation, showing clear improvement in terms of efficiency, flexibility, and scalability.

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Characteristics of optical flow from aerial thermal imaging, “thermal flow”

Nguyen

Rosser

Perera

et al. 2022

Journal of Field Robotics

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This study explores the utility of optical flow calculated from thermal imaging cameras, "thermal flow," mounted on an aircraft for localization in day and night conditions. Our sensor implementation utilizes a long wave infrared (LWIR) micro sensor to capture sequences of thermal images and an on-board computer to compute an optical flow estimate. We compared the performance of optical flow from the LWIR camera with the output of visible spectrum optical flow sensor.Flights were conducted spanning a 24 h window to explore how thermal flow performs relative to optical flow as the ground heats and cools. Agreement between optical and thermal flow was found during daylight when both sensors were functional. Additionally, thermal flow results were reliable in the middle of the day through to late evening, gradually degrading until shortly after sunrise.

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Multi-UAV target search using explicit decentralized gradient-based negotiation

Cited by 64 publications

References 10 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

Adaptive Action for Multi‐Agent Persistent Coverage

Characteristics of optical flow from aerial thermal imaging, “thermal flow”

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