Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance

Di, Bin; Zhou, Rui; Duan, Haibin

doi:10.1016/j.ast.2015.08.006

Cited by 53 publications

(26 citation statements)

References 21 publications

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“…The past few decades have witnessed rapid development of application of unmanned aerial vehicles (UAVs), popularly known as drones. Given the characteristics of small volume, high mobility and low energy consumption, UAVs have been widely utilized in public, civil and military applications [1][2][3]. In particular, with proper deployment and operation, UAVs can provide reliable and efficient wireless communication solutions for various real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

et al. 2018

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Unmanned aerial vehicle (UAV) cooperative control has been an important issue in UAV-assisted sensor networks, thanks to the considerable benefit obtained from the cooperative mechanism of UAVs being applied as a flying base station. In a coverage scenarios, the trade-off between coverage and transmission performance often makes deployment of UAVs fall into a dilemma, since both indexes are related to the distance between UAVs. To address this issue, UAV coverage and data transmission mechanism is analyzed in this paper; then, an efficient multi-UAV cooperative deployment model is proposed. The problem is modeled as a coalition formation game (CFG). The CFG with Pareto order is proved to have a stable partition. Then, an effective approach consisting of coverage deployment and coalition selection is designed, wherein UAVs can decide strategies cooperatively to achieve better coverage performance. Combining analysis of game approach, coalition selection and the position deployment algorithm based on Pareto order (CSPDA-PO) is designed to execute coverage deployment and coalition selection. Finally, simulation results are shown to validate the proposed approach based on an efficient multi-UAV cooperative deployment model.

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Section: Introductionmentioning

confidence: 99%

A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

et al. 2018

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“…It is concerned with cooperative controlling of the movements of multi-UAVs in order to guarantee optimal search paths that maximize the possibility of finding targets and minimize the uncertainty in the environment. Different authors have developed several search path planning methods, such as reinforcement learning [ 15 ], potential field [ 16 ], group dispersion pattern [ 17 ], intelligence algorithm [ 18 ], dynamic programming [ 19 ], gradient optimization [ 20 ], mixed integer linear programming [ 21 ], Voronoi partitioning [ 22 , 23 ], and receding horizon optimization (RHO) [ 24 , 25 , 26 ]. In [ 22 , 23 ], the convex region is partitioned into Voronoi cells so that there is only one agent in each Voronoi cell according to the position of the agents.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [ 24 ] presents a receding horizon cooperative search algorithm that jointly optimizes paths and sensor orientations for a team of UAVs searching for a mobile target. In reference [ 25 ], a receding horizon, motion-planning algorithm is used to obtain the optimal search path in the given horizon. In reference [ 26 ], the distributed model predictive control method is presented to solve the cooperative search moving targets problem.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

Liu

Gao

2018

Sensors

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In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

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“…In recent years, the technology of UAVs has attracted much attention [1]- [3], among which the collaboration of multiple UAVs has been widely studied [4]- [8]. A clear division of responsibilities is the basis of the collaboration of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Area Partitioning for Multi-UAVs in Cooperative Monitoring Task

Xiao

Fang

Liang

et al. 2018

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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In resource and task allocation problems, reasonable allocation can make full use of the UAVs(Unmanned Aerial Vehicles) and bring massive improvement in efficiency. Specifically, the region distribution policies are studied in this paper. In order to make the workload match the ability of the UAVs, the environment is expected to be divided into several subregions of certain sizes. And the group of UAVs should always keep monitoring the whole region no matter how many UAVs are left, which means that the distribution of the area is supposed to react timely even when some UAVs quit the task. Additionally, for the convenience of single UAV's monitoring, the region of one partition is expected not to be too scattered. Therefore, both the area and the shape of the partition are considered in the partition strategies. In this paper, a power diagram based area partitioning method is proposed to rationalize both the allocation of the areas and the shape of each partition. By adjusting the weights of the partitions and the positions of the generators iteratively, the areas and shapes of the partitions can reach an expected and stable state finally. Simulation results are provided to verify the effectiveness and feasibility of the proposed method.

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Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance

Cited by 53 publications

References 21 publications

A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

Distributed Area Partitioning for Multi-UAVs in Cooperative Monitoring Task

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