A Heuristic Learning Algorithm for Preferential Area Surveillance by Unmanned Aerial Vehicles

Ramasamy, Manickam; Ghose, Debasish

doi:10.1007/s10846-017-0498-5

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…The problem may be more complex considering the necessity of increasing or reducing the frequency of visits depending on the zone profile-an interesting one or a risky one. Ramasamy and Ghose [85] extend their work dedicating more attention to the preferential surveillance in a known area. They explore an approach considering different ways of quantitative priority specifications.…”

Section: Coverage With Uncertaintymentioning

confidence: 98%

“…Considering the CPP problem with aerial vehicles, several authors have explored different approaches in the literature, including real-time search methods [36], random walk [71], cellular systems [72][73][74], evolutionary computation [75,76], and swarm intelligence [77][78][79]. Coverage with uncertainty considering information points is also addressed [80][81][82][83][84][85]. Most of the approaches are pheromone-based and explore the natural behavior of ants to guide the vehicles through a grid-discretized scenario.…”

Section: Partial Informationmentioning

confidence: 99%

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Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Cabreira

Brisolara

Ferreira

2019

Drones

421

207

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Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

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Section: Coverage With Uncertaintymentioning

confidence: 98%

Section: Partial Informationmentioning

confidence: 99%

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Cabreira

Brisolara

Ferreira

2019

Drones

421

207

View full text Add to dashboard Cite

show abstract

“…Xung et al [35] introduced a guidance law and heading rate control that allow the minimisation of the exposure time proving their approach in a simplified environment. Ramasamy and Ghose [36] proposed an algorithm that maximizes the visitation on preferential areas and that reduces the frequency of risk areas visiting using a heuristic learning technique. Redding et al [37] approached the issue with an intelligent Cooperative Control Architecture implementing an active learning approach.…”

Section: Previous Workmentioning

confidence: 99%

“…Partially Observable Markov Decision Processes (POMDPs) were proposed by Capitan et al [42,43] although the implementation leads to a great computational complexity. Moreover, Ramasamy et al [44] presented a heuristic learning algorithm which allows the definition of quantitative priorities per region.…”

Section: Previous Workmentioning

confidence: 99%

“…As shown in Eqn. (36), higher importance is given to the term that accounts if the target does not decelerate when it is going to continue in the same direction. "High turning ratio" If we detect that the target is performing a significant number of turnings we can use this information in order to improve the probability of target changing direction at the next intersection.…”

Section: Behavioural Pattern Informationmentioning

confidence: 99%

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Information-driven Persistent Sensing of a Non-cooperative Mobile Target Using UAVs

Shin

García

Álvarez

2017

J Intell Robot Syst

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This paper addresses the persistent sensing problem of moving ground targets of interest using a group of fixed wing UAVs. Especially, we aim to overcome the challenge of physical obscuration in complex mission environments. To this end, the persistent sensing problem is formulated under an optimal control framework, i.e. deploying and managing UAVs in a way maximising the visibility to the non-cooperative target.The main issue with such a persistent sensing problem is that it generally requires the knowledge of future target positions, which is uncertain. To mitigate this issue, a probabilistic map of the future target position is widely utilised. However, most of the probabilistic models use only limited information of the target. This paper proposes an innovative framework that can make the best use of all available information, not only limited information. For the validation of the feasibility, the performance of the proposed framework is tested in a Manhattan-type controlled urban environment. All the simulation tests use the same framework proposed, but utilise different level of information. The simulation results confirm that the performance of the persistent sensing significantly improves, up to 30%, when incorporating all available target information.

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Distributed Control for Groups of Unmanned Aerial Vehicles Performing Surveillance Missions and Providing Relay Communication Network Services

Moraes

Freitas

2017

J Intell Robot Syst

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A Heuristic Learning Algorithm for Preferential Area Surveillance by Unmanned Aerial Vehicles

Cited by 17 publications

References 20 publications

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Information-driven Persistent Sensing of a Non-cooperative Mobile Target Using UAVs

Distributed Control for Groups of Unmanned Aerial Vehicles Performing Surveillance Missions and Providing Relay Communication Network Services

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