Cooperative Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance, and Reconnaissance Missions

Liu, Yao; Luo, Zhihao; Liu, Zhong; Shi, Jianmai; Cheng, Guangquan

doi:10.1109/access.2019.2914352

Cited by 79 publications

(25 citation statements)

References 22 publications

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“…However, only a few studies demonstrated how to deploy UAVs in specific battlefields for cooperative mission planning. Due to the limitation of current hardware and technical conditions, how to reasonably dispatch UAVs to perform cooperative tasks and choose the best reconnaissance route for a group or cluster of UAVs is of great significance to operations [7,8].…”

Section: The Multi-unmanned Aerial Vehicle (Uav) Systemmentioning

confidence: 99%

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

2020

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Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs.

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Section: The Multi-unmanned Aerial Vehicle (Uav) Systemmentioning

confidence: 99%

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

2020

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“…Each customer can be served only once by only one vehicle. To the best of our knowledge, there are a variety of optimal and heuristic algorithms to MVRPSDP problems in the existing literature [24]- [26], [37]- [39]. In this study, we apply the state-ofthe-art MIP resolver tool, ILOG-CPLEX (version 12.80) to efficiently resolve the relaxed model with Constraints (4)- (10) and (12) and obtain the optimal routes for vehicles.…”

Section: Subalgorithm In the Second Stagementioning

confidence: 99%

Optimization Model and Algorithm for Dockless Bike-Sharing Systems Considering Unusable Bikes in China

Teng

Zhang

et al. 2020

IEEE Access

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The damaged dockless shared-bikes are increasingly common in large cities of China, which not only reduce users' satisfactions but also harm the public environment. It is a merging issue for dockless bike-sharing systems to timely recall unusable bikes and replenish new/usable bikes. This paper addresses a maintenance network for a dockless bike-sharing system within a specific region in which the random demands and returns of shared-bikes are considered and the returned bikes have a certain probability to be unusable. The problem is to jointly determine the numbers of unusable and usable bikes to be recalled and replenished, respectively, and the corresponding routes of maintenance vehicles via the maintenance network. The problem is formulated as a stochastic mixed-integer programming model with the objective to maximize the expected revenue of the maintenance network within a operational period. To resolve the such complicated integration-optimization problem efficiently, the formulated model is decomposed into a multilocation newsvendor subproblem and a multiple vehicle routing subproblem with simultaneous delivery and pickup. An efficient two-stage heuristics is then proposed, which resolves the two subproblems in the two stages of the algorithm, respectively. The computational results based on an real numerical case and a large number of numerical instances validate the performance of the proposed model and algorithm.

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“…The number of applications for unmanned aerial vehicles (UAVs) is widely increasing in the civil arena such as surveillance [1,2], delivery of goods [3,4], power line inspection [5,6], and mapping [7,8].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Wan

Gao

et al. 2020

Sensors

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Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

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Cooperative Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance, and Reconnaissance Missions

Cited by 79 publications

References 22 publications

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

Optimization Model and Algorithm for Dockless Bike-Sharing Systems Considering Unusable Bikes in China

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

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