Complete 3-D dynamic coverage in energy-constrained multi-UAV sensor networks

Bentz, William; Hoang, Tru; Bayasgalan, Enkhmurun; Panagou, Dimitra

doi:10.1007/s10514-017-9661-x

Cited by 34 publications

(16 citation statements)

References 32 publications

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“…It is hence more motivating to work towards addressing 3D coverage problems exploiting the rich geometric properties of 3D MWSNs [67]. Some efforts have been made in that area such as [32,[67][68][69][70][71][72][73]. Thus, one of the main motivations of this work is to contribute towards the state-of-the-art 3D static and coverage problems using MWSNs.…”

Section: Related Workmentioning

confidence: 99%

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Elmokadem

Savkin

2021

Drones

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This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

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Section: Related Workmentioning

confidence: 99%

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Elmokadem

Savkin

2021

Drones

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“…UAVs have many potential applications, such as area surveillance, and search and rescue [ 1 , 2 , 3 , 4 , 5 ]. Collaborative multi-UAV systems can significantly improve the work efficiency, especially when they perform challenging and complex tasks, such as target search in dangerous environments, i.e., a seismic region, or monitoring a large area-of-interest [ 6 , 7 , 8 , 9 ]. Area coverage is a typical fundamental application problem for multi-UAV systems, and the coverage problem is optimizing notions of quality-of-service (QoS) provided by an adaptive sensor network in a dynamic environment [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement

Zhang

Cui

et al. 2021

Sensors

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This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

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“…Other factors that affect the choice of control scheme are the dynamics of the agents used, 6,7 the geometrical characteristics of the region of interest, 8,9 and the sensing performance of the agents' sensors. 10,11 Several varying approaches to the coverage problem have been proposed, including annealing algorithms, 12 game theory, 13 distributed optimization, 14 model predictive control, 15,16 optimal control, 17 and event-triggered control.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed area coverage control with imprecise robot localization

Papatheodorou

Tzes

Giannousakis

et al. 2018

International Journal of Advanced Robotic Systems

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This article examines the static area coverage problem by a network of mobile, sensor-equipped agents with imprecise localization. Each agent has uniform radial sensing ability and is governed by first-order kinodynamics. To partition the region of interest, a novel partitioning scheme, the Additively Weighted Guaranteed Voronoi diagram is introduced which takes into account both the agents' positioning uncertainty and their heterogeneous sensing performance. Each agent's region of responsibility corresponds to its Additively Weighted Guaranteed Voronoi cell, bounded by hyperbolic arcs. An appropriate gradient ascent-based control scheme is derived so that it guarantees monotonic increase of a coverage objective and is extended with collision avoidance properties. Additionally, a computationally efficient simplified control scheme is offered that is able to achieve comparable performance. Several simulation studies are offered to evaluate the performance of the two control schemes. Finally, two experiments using small differential drive-like robots and an ultrawideband positioning system were conducted, highlighting the performance of the proposed control scheme in a real world scenario.

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Complete 3-D dynamic coverage in energy-constrained multi-UAV sensor networks

Cited by 34 publications

References 32 publications

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement

Distributed area coverage control with imprecise robot localization

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