UAV Formation Obstacle Avoidance Based on Improved Consistency Algorithm

Hu, Shaoli; Tang, Jiankai; Chen, Chen; Xu, Hongli; Wang, Shuai

doi:10.1109/iccasit55263.2022.9986809

Cited by 2 publications

(1 citation statement)

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“…When single UAV performs large and complex task, its completion rate is very low due to its own shortcomings such as small combat radius, small load and other factors 1 . In order to improve the combat efficiency of single UAV, multi-UAVs formation 2 is proposed. Formation can make full use of the advantages of UAVs, integrating resources and cooperate to complete complex tasks.…”

Section: Introductionmentioning

confidence: 99%

A dense formation control method for UAVs based on improved ant colony algorithm

Gao¹,

Zeng²

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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The classical Ant Colony Optimization (ACO) has some problems such as slow convergence speed and easy to fall into local optimum, a control method for dense formation of UAVs is proposed in this thesis. Firstly, cubic mapping is used to initialize the ACO distribution for making full use of map information and avoiding falling into local optimum; Secondly, the pheromone concentration updated by reward and punishment mechanism. Adaptive pheromone volatility factor is used to balance the global search and local search, as well as accelerate the convergence of the algorithm; Then, the artificial potential field is combined with the ACO to optimize the obstacle avoidance path. Finally, the state information from the dense formation of UAVs is corrected according to the consistency control theory, and the formation consistency control model is designed to realize the dense formation control. The experimental results shows that the proposed algorithm can achieve obstacle avoidance while maintaining tight formation, the formation can converge to the desired formation quickly after the obstacle avoidance is completed.

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Section: Introductionmentioning

confidence: 99%

A dense formation control method for UAVs based on improved ant colony algorithm

Gao¹,

Zeng²

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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Distributed safety flight control for LAV swarm in a hostile environment under multi-constraints and sudden appearing risks

Zhao,

Wang,

Zheng

2024

AEAT

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Purpose Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden. Design/methodology/approach To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC). Findings The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks. Originality/value The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

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UAV Formation Obstacle Avoidance Based on Improved Consistency Algorithm

Cited by 2 publications

References 0 publications

A dense formation control method for UAVs based on improved ant colony algorithm

A dense formation control method for UAVs based on improved ant colony algorithm

Distributed safety flight control for LAV swarm in a hostile environment under multi-constraints and sudden appearing risks

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