UAV Swarm Intelligence: Recent Advances and Future Trends

Zhou, Yongkun; Rao, Bolla Govinda; Wang, Wei

doi:10.1109/access.2020.3028865

Cited by 184 publications

(78 citation statements)

References 142 publications

(167 reference statements)

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“…While the current optimization included only a handful of variables and imposed extremely limited ranges on them because of aviation authority flight restrictions and the imposition of a set of parallel lines to be followed by a manned aircraft, this is not necessarily reflective of the future aerial LiDAR. The past decade has shown rapidly growing capacities and commercial options of unmanned autonomous vehicles as individual units and swarms (e.g., [46][47][48]). The proposed framework could be further developed for planning complex flight paths that may be composed of curved and/or non-parallel flight lines as often seen in UAV mapping.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Urban LiDAR Flight Path Planning Using a Genetic Algorithm and a Dual Parallel Computing Framework

Laefer

Byrne

2021

Remote Sensing

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This paper introduces a genetic algorithm (GA) and a beam tracing algorithm incorporated within a dual parallel computing framework to optimize urban aerial laser scanning (ALS) missions to maximize vertical façade data capture, as needed for many three-dimensional reconstruction and modeling workflows. The optimization employs a low-density point cloud from the site of interest as a spatial representation of the urban scene. The GA is suitable for LiDAR flight path optimization due to its capability of handling open-ended problems that have many solutions. However, GAs require evaluating a very large number of candidates. The use of an initial point cloud allows realistic modeling of the urban environment in the optimization at the cost of high data input volumes. To cope with the computational and data demands, a dual parallel computing framework was devised. The parallel computing framework consists of two layers of parallelization. In the upper layer, multiple evaluators work in parallel and in conjunction with a main multi-threading GA optimizer to perform GA operations and evaluate the flight paths. In the lower layer, to evaluate assigned flight paths, each evaluator distributes its data and computation to multiple executors, which can reside on multiple physical nodes of a distributed-memory computing cluster. In addition to parallelism, the data partitioning on the lower layer allows out-of-core computation. Namely, data partitions are efficiently transferred between disks and memory so that only relevant subsets of data are kept in the main memory. The objective of the proposed method is threefold: (1) search for flight paths that yield the highest numbers of vertical points, (2) create a means to explicitly consider the detailed spatial configuration of urban environments, and (3) assure that the proposed optimization strategy is fast and can scale to large problem sizes. Multiple experiments were conducted and demonstrated the success of the proposed method. Converged results were achieved after dozens of generations within two hours. Two flight paths identified by the GA as the most and the least optimal candidates were deployed in real flight missions. The optimal flight path captured 16% more vertical points than the least optimal one, slightly higher than the 13% predicted. Both layers of parallelization were efficient: 13.1/16 for the lower layer and 3.2/4 for the upper layer. The two complementary layers of parallelization allowed flexible and efficient use of distributed computing resources to reduce the runtime. The scalability of the proposed approach was successfully demonstrated up to a data size of 460 million points. The optimization results were realistic and aligned well with the test flight results.

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Section: Discussionmentioning

confidence: 99%

Optimizing Urban LiDAR Flight Path Planning Using a Genetic Algorithm and a Dual Parallel Computing Framework

Laefer

Byrne

2021

Remote Sensing

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“…The application layer determines the environment in which the drone swarm is used. A detailed description of the classification of drone swarm mission management algorithms is given in the study [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

“…The description of even some of the most important articles constituting the basis of route planning algorithms on the directed network exceeds the scope of this article. The interested reader is referred to the very deep overview of algorithms given by Zhou [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment

Siemiątkowska

Stecz

2021

Sensors

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This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm’s flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.

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“…They can be achieved using swarm intelligence [9] as a way of modifying the collective behaviour of the swarm by using individual parameters. The members' interactions usually follow local rules based on pheromones and probabilities [10], upper confidence trees [11] or finite games [12], among others [13]. These strategies can be based on competitions [14] or collaborations [15] between members, having each approach its own advantages and disadvantages [16].…”

Section: Introductionmentioning

confidence: 99%

SuSy-EnGaD: Surveillance System Enhanced by Games of Drones

Stolfi

Brust

Danoy

et al. 2022

Drones

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In this article, we propose SuSy-EnGaD, a surveillance system enhanced by games of drones. We propose three different approaches to optimise a swarm of UAVs for improving intruder detection, two of them featuring a multi-objective optimisation approach, while the third approach relates to the evolutionary game theory where three different strategies based on games are proposed. We test our system on four different case studies, analyse the results presented as Pareto fronts in terms of flying time and area coverage, and compare them with the single-objective optimisation results from games. Finally, an analysis of the UAVs trajectories is performed to help understand the results achieved.

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UAV Swarm Intelligence: Recent Advances and Future Trends

Cited by 184 publications

References 142 publications

Optimizing Urban LiDAR Flight Path Planning Using a Genetic Algorithm and a Dual Parallel Computing Framework

Optimizing Urban LiDAR Flight Path Planning Using a Genetic Algorithm and a Dual Parallel Computing Framework

A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment

SuSy-EnGaD: Surveillance System Enhanced by Games of Drones

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