Stochastic Heuristic Algorithms for Multi-UAV Cooperative Path Planning

Zhao, Chang; Liu, Yungang; Yu, Linzhen; Li, Wenjuan

doi:10.23919/ccc52363.2021.9549984

Cited by 6 publications

(8 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The threat level includes the cost of threat distance and threat time, meaning the smaller the distance between the UAV and the threat, the greater the threat level; the longer the flight time in the danger zone, the greater the threat level. The costs of flight time and threat distance are given by equations ( 6) and (7), respectively. The sum of threat time and threat distance costs for all segments yields the threat level cost function:…”

Section: ) Path Lengthmentioning

confidence: 99%

“…In the military domain, UAVs contribute to reconnaissance, target tracking, and coordinated strikes, significantly enhancing the efficiency and success rate of military operations [1]- [3]. In civil domains, UAVs are used for infrastructure inspection, logistics delivery, agricultural spraying, search and rescue tasks, showing great application value and market potential [4]- [7]. The challenges in these application domains can be summarized as three-dimensional path planning problems for UAVs [8], which are global optimization problems under multiple constraints, considering environmental characteristics, mission requirements, and UAV performance constraints.…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Improved Plant Growth Algorithm for UAV Three-Dimensional Path Planning

Xiao,

Mu,

Zhou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In response to the challenge of three-dimensional path planning for unmanned aerial vehicles (UAVs), this paper introduces a Multi-Strategy Plant Growth Path Planning (MSPGPP) algorithm. Initially, a threat sphere and obstacle avoidance cone model are constructed to ensure safety in avoidance and enhance algorithm accuracy and convergence speed, while B-spline curves are introduced to improve the quality of the path. Next, the concept of threat level is incorporated along with UAV maneuverability to establish a multi-objective optimization function, enabling the algorithm to plan optimal paths. Finally, an algorithm framework is developed, and comparative simulation results of the improved algorithm against the original algorithm and other optimization algorithms are presented in a three-dimensional environment. The results demonstrate that compared to the original algorithm, the improved algorithm better balances obstacle avoidance safety and UAV maneuverability, planning safer and more effective paths. Additionally, it achieves higher accuracy and faster convergence. Compared to other optimization algorithms, the improved algorithm has better optimization capabilities, higher computational efficiency, and superior path quality.INDEX TERMS Unmanned aerial vehicle, path planning, multi-objective optimization, plant growth, obstacle avoidance cone.

show abstract

Section: ) Path Lengthmentioning

confidence: 99%

mentioning

confidence: 99%

An Improved Plant Growth Algorithm for UAV Three-Dimensional Path Planning

Xiao,

Mu,

Zhou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Stochastic Heuristic Algorithms (SHA) are reviewed in reference [10], and the characteristics, improvements, applications, advantages and disadvantages of some of them The purpose of trajectory planning of UAV formation is based on the specific tasks, terrain, weather and other environmental factors of each UAV as well as its own flight performance. Under the premise of satisfying multiple constraints, the specified performance index can be optimized or better so that all UAVs in the formation can safely reach the target from the starting point.…”

Section: Introductionmentioning

confidence: 99%

“…Stochastic Heuristic Algorithms (SHA) are reviewed in reference [10], and the characteristics, improvements, applications, advantages and disadvantages of some of them are discussed, but non-SHA algorithms in UAV formation flight paths are not discussed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

View full text Add to dashboard Cite

With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

show abstract

An efficient path planning approach for autonomous multi-UAV system in target coverage problems

Pehlivanoglu,

Pehlivanoğlu

2024

AEAT

View full text Add to dashboard Cite

Purpose The purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems. Design/methodology/approach An enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy. Findings Numerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively. Practical implications Simulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems. Originality/value The proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.

show abstract

Stochastic Heuristic Algorithms for Multi-UAV Cooperative Path Planning

Cited by 6 publications

References 49 publications

An Improved Plant Growth Algorithm for UAV Three-Dimensional Path Planning

An Improved Plant Growth Algorithm for UAV Three-Dimensional Path Planning

UAV Formation Trajectory Planning Algorithms: A Review

An efficient path planning approach for autonomous multi-UAV system in target coverage problems

Contact Info

Product

Resources

About