Optimal path planning of UAV using grey wolf optimiser

Soundarya, M.S.; Anusha, Danashaker K.; Rohith, P.; Panneerselvam, Kavitha; Srinivasan, Seshadari

doi:10.1504/ijcsyse.2019.10022445

Cited by 7 publications

(3 citation statements)

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“…A dual-model experiment is established to simulate both single AUV trajectory planning and multi-AUV trajectory planning, enhancing the credibility of the proposed algorithm. In single AUV trajectory planning, the algorithm presented in this paper is compared with GWO [21], SSA [22], MFO [23], and BES [24]. In multi-AUV trajectory planning, the algorithm presented in this paper is compared with the solution performance of the Multi-Objective Grey Wolf Optimization (MOGWO), Multi-Objective Sparrow Search Algorithm (MOSSA), Multi-Objective Moth-Flame Optimization (MOMFO), Multi-Objective Bald Eagle Search (MOBES), and NSGA-III [25] algorithms.…”

Section: Simulation Experimental Conditions and Algorithm Parametersmentioning

confidence: 99%

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Huang,

Wen,

Niu

et al. 2024

JMSE

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Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, susceptibility to local optima, and difficulty in convergence. To address these issues, we propose an improved multi-objective manta ray foraging optimization (IMMRFO) method, which can improve the accuracy of trajectory planning through a comprehensive three-stage approach. Firstly, basic model sets are established, including a three-dimensional ocean terrain model, a threat source model, the physical constraints of AUV, path smoothing constraints, and spatiotemporal coordination constraints. Secondly, an innovative chaotic mapping technique is introduced to initialize the position of the manta ray population. Moreover, an adaptive rolling factor “S” is introduced from the manta rays’ rolling foraging. This allows the collaborative-vehicle population to jump out of local optima through “collaborative rolling." In the processes of manta ray chain feeding and manta ray spiral feeding, Cauchy reverse learning is integrated to broaden the search space and enhance the global optimization ability. The optimal Pareto front is then obtained using non-dominated sorting. Finally, the position of the manta ray population is mapped to the spatial positions of multi-AUVs, and cubic spline functions are used to optimize the trajectory of multi-AUVs. Through detailed analysis and comparison with five existing multi-objective optimization algorithms, it is found that the IMMRFO algorithm proposed in this paper can significantly reduce the average planned path length by 3.1~9.18 km in the path length target and reduce the average cost by 18.34~321.872 in the cost target. In an actual off-shore measurement process, IMMRFO enables AUVs to effectively bypass obstacles and threat sources, reduce risk costs, and improve mobile surveillance safety.

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Section: Simulation Experimental Conditions and Algorithm Parametersmentioning

confidence: 99%

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Huang,

Wen,

Niu

et al. 2024

JMSE

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“…From Figure 11, it can be observed that the IGWO algorithm successfully completes the path planning task from three different starting points. However, in Figure 11a, it should be noted that the paths generated by the IGWO algorithm are too close to the mountainous obstacles from (12,14,19) to (12,21,25). This proximity increases the risk of the UAV colliding with the mountains, potentially leading to a crash.…”

Section: Analysis Of Path Planning Effectivenessmentioning

confidence: 99%

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

Han,

Yu,

Jiang

et al. 2024

Applied Sciences

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The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.

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“…Grey wolf optimization [11][12][13][14][15] is a novel intelligent simulation optimization algorithm inspired by the hunting behavior of grey wolf packs. It has advantages over other intelligent simulation algorithms, such as fewer adjustable parameters, simple structure, ease of implementation, and good global search capability.…”

Section: Introductionmentioning

confidence: 99%

UAV path planning: Integration of grey wolf algorithm and artificial potential field

Chen,

Yu,

Han

et al. 2024

Concurrency and Computation

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SummaryUnmanned aerial vehicle (UAV) path planning is an important issue in UAV applications, with the goal of finding the optimal path to meet mission requirements, while considering factors such as avoiding obstacles and optimizing flight performance. To improve the efficiency of UAV path planning and enhance the smoothness and safety of UAV operation, this paper proposes a fusion optimization algorithm (GWO‐APF), which combines the grey wolf algorithm (GWO) and artificial potential field method (APF) for UAV path planning algorithms. Based on the GWO algorithm, this algorithm first sets planning constraints such as turning angle and tolerance to reduce the probability of local optima occurring; Second, using dimensionality reduction to improve the search efficiency of the GWO algorithm and generate the initial path; Finally, gravity is given to each point on the initial path, and the APF algorithm is used for path planning again. The simulation results show that the fused GWO‐APF algorithm has stronger path planning ability compared to the traditional GWO algorithm, and has the characteristics of short range and high safety.

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Optimal path planning of UAV using grey wolf optimiser

Cited by 7 publications

References 0 publications

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

UAV path planning: Integration of grey wolf algorithm and artificial potential field

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