UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm

Yao-zhong, Zhang; Wen-cheng, Feng; Shi, Guangming; Jiang, Frank; Chowdhury, Mihir; Ling, Sai Ho

doi:10.3390/s20082307

Cited by 36 publications

(17 citation statements)

References 24 publications

(9 reference statements)

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“…The resource constraint in (3) indicates that the total resources of RS-UAVs in the attacking coalition should sa-…”

Section: Description Of Uav Swarm Cooperative Search-attack Mission Planning Problemmentioning

confidence: 99%

“…Due to the capability limitation of a single UAV in complex mission environment, UAV swarm has attracted increasing attention because of its higher working efficiency, stronger task capability, and better system robustness [1,2]. In the military field, cooperative search-attack in unknown environments is an important application of UAV swarms, and the efficient mission planning technology is the basis for UAV swarms to perform the cooperative search-attack task autonomously [3,4]. Due to the complex and dynamic nature of task environments, the tight coupling between UAV path planning and task allocation, and the heterogeneity of UAVs, UAV swarm cooperative mission planning (USCMP) has become a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

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Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Zhan

et al. 2021

J. of Syst. Eng. Electron.

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Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed selforganizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

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“…The resource constraint in (3) indicates that the total resources of RS-UAVs in the attacking coalition should sa-…”

Section: Description Of Uav Swarm Cooperative Search-attack Mission Planning Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Zhan

et al. 2021

J. of Syst. Eng. Electron.

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“…For the real-time complex mission planning problem of the multi-heterogeneous system in a dynamic environment. Yaozhong Zhang et al effectively adjusted the consensusbased beam algorithm under the limitation of task timing, controlled UAV resources, dynamic task addition, and instant requirements (CBBA), developed a new method [45]. Yimeng Lu has built a safety planning framework for multifarious tasks in an unstable environment.…”

Section:  Complex Task Planning In Dynamic Environmentmentioning

confidence: 99%

“…Complex task planning in dynamic environment [45] Consensus-Based Bundle Algorithm Solved the complex task planning problem of multi-heterogeneous UAV system under the limited resources. [46] A Safe Planning Framework  Integrate dynamic uncertainty into a safe planning framework  Use the Monte Carlo method to obtain a safe path.…”

Section:  Complex Task Planning In Dynamic Environmentmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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“…The cooperation between UAVs is an important factor to ensure the high quality of tasks. Yaozhong Zhang et al [25], based on the consistency bundling algorithm, introduces the concept of asynchronous task allocation, and studies the task planning of UAV group in dynamic environment. The combination of virtual structure and path tracking method has a good performance in crowd collaborative control of mobile machines [26,27].…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Sun

Huang

et al. 2020

IEEE Access

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In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.INDEX TERMS Cluster control, virtual core algorithm, cluster reconstruction, track planning, improved APF

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UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm

Cited by 36 publications

References 24 publications

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

UAV Swarm Intelligence: Recent Advances and Future Trends

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

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