Multi-objective constraint-based smooth path generation for UAVs global optimization method

doi:10.3785/j.issn.1008-973X.2025.07.016

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (7): 1481-1491 DOI: 10.3785/j.issn.1008-973X.2025.07.016

Multi-objective constraint-based smooth path generation for UAVs global optimization method

Yuxin LIAO1(

),Wei WANG1,*(

),Weiming TENG2,Haiyan HE2,Zhan WANG3,Jin WANG4

1. School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
2. Zhejiang Baima Lake Laboratory Limited Company, Hangzhou 310051, China
3. Zhejiang Energy Digital Technology Limited Company, Hangzhou 310012, China
4. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

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Abstract

Path planning in complex environments is fundamental to ensuring safe UAV operations. A global optimization method was proposed that comprehensively considered obstacle avoidance capability, flight efficiency, and stability constraints. The cylindrical envelope method was employed to standardize environmental data and obstacles, and the impact of UAV attitude variations on motion stability and flight efficiency was analyzed. Three optimization objectives—efficiency, obstacle avoidance, and stability—were established, expanding the optimization space from traditional one-dimensional or two-dimensional to three-dimensional. A reference-point-based non-dominated sorting genetic algorithm III (NSGA-III) was introduced to accelerate the search process in high-dimensional space, achieving approximately 49% faster convergence compared to the improved A* algorithm. A potential risk point detection mechanism was designed for obstacle avoidance, and a quintic B-spline was used to perform secondary optimization on the initial optimal path, enhancing the geometric characteristics and smoothness of the UAV trajectory. As a result, flight stability and efficiency were improved by 66.7% and 25%, respectively. The effectiveness of NSGA-III in trajectory planning was validated through simulations and experiments.

Key words： path planning multiple objectives spline curve trajectory smoothness UAV

Received: 11 June 2024 Published: 25 July 2025

CLC:

TP 242

Fund: 国家自然科学基金资助项目（52405041）；浙江省“尖兵领雁”资助项目（2023C01180）；浙江省自然科学基金资助项目（LQ22E050022）.

Corresponding Authors: Wei WANG E-mail: lyxzstu@163.com;wangw@zstu.edu.cn

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Cite this article:

Yuxin LIAO,Wei WANG,Weiming TENG,Haiyan HE,Zhan WANG,Jin WANG. Multi-objective constraint-based smooth path generation for UAVs global optimization method. Journal of ZheJiang University (Engineering Science), 2025, 59(7): 1481-1491.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.07.016 OR https://www.zjujournals.com/eng/Y2025/V59/I7/1481

基于多目标约束的无人机光顺路径生成全局优化方法

复杂环境中的路径规划是无人机安全作业的基础，为此提出综合考虑避障能力、飞行效率和稳定性约束的全局优化方法. 采用柱面包络法对环境信息及障碍物进行规则化处理，分析无人机姿态变化对运动稳定性和飞行效率的潜在影响. 建立效率、避障和稳定性3个优化目标，将优化空间从传统的一维或二维提升到三维. 引入基于参考点的第三代非支配排序遗传算法 (NSGA-III)加速高维空间中的搜索过程，与改进的A*算法相比，收敛速度提高约49%. 针对障碍物设计潜在风险点判断机制，结合五次B样条对初始最优路径进行二次优化，改善无人机轨迹的几何特性及光顺性，使飞行稳定性及效率分别提高66.7%和25%. 通过仿真和实验验证NSGA-III在轨迹规划方面的有效性.

关键词： 路径规划, 多目标, 样条曲线, 轨迹平滑, 无人机

Fig.1 Schematic diagram of flight path cost

Fig.2 Collision risk of UAVs

Fig.3 Two key parameters of UAV flight

Fig.4 Individuals classification based on dominated sorting

Fig.5 Reference point construction with hyperplane

Fig.6 Process of generating new populations

Fig.7 Optimisation flowchart of non-dominated sorting genetic algorithm III

Fig.8 Interpolation method using potential risk points as transition paths

Fig.9 Geometric relationship among path points and obstacles

Tab.1 Coordinates of reference points in hyperplane

Fig.10 Design of reference points

Fig.11 Simulation scenarios

Tab.2 Performance comparison of different algorithms for optimization in three scenarios

Fig.12 Optimization iteration process of non-dominated sorting genetic algorithm

Fig.13 3D views of UAV flight paths in three scenarios with different algorithms

Fig.14 Top view of UAV flight paths in three scenarios with different algorithms

Tab.3 Qualitative description of characteristics for different algorithms

Fig.15 Distribution of optimal results for different non-dominated sorting genetic algorithms

Fig.16 UAV flight trajectory after secondary optimization (scenario 1)

Fig.17 Flight-time variation curve for initial optimal path

Fig.18 Flight-time variation curve for smooth path

Fig.19 Position variation curve of UAV flight path

Fig.20 Experimental platform of UAV

Fig.21 Diagram of UAV control system

Fig.22 UAV flight experimental results before and after optimization by non-dominated sorting genetic algorithm III


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