基于APF的AGV局部路径规划改进算法研究

doi:10.3778/j.issn.1002-8331.2102-0339

计算机工程与应用 ›› 2022, Vol. 58 ›› Issue (22): 305-312.DOI: 10.3778/j.issn.1002-8331.2102-0339

• 工程与应用 • 上一篇

基于APF的AGV局部路径规划改进算法研究

丁承君，阎欣怡，冯玉伯，贾丽臻

1.河北工业大学机械工程学院，天津 300130
2.天津通信广播集团有限公司智慧云网事业部，天津 300143
3.中国民航大学航空工程学院，天津 300300

出版日期:2022-11-15 发布日期:2022-11-15

Improved Algorithm of AGV Local Path Planning Based on APF

DING Chengjun, YAN Xinyi, FENG Yubo, JIA Lizhen

1.School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
2.Intelligence Industry Division, Tianjin Communication & Broadcasting Group Co., Ltd., Tianjin 300143, China
3.School of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China

Online:2022-11-15 Published:2022-11-15

摘要/Abstract

摘要： 针对原有人工势场法（artificial potential field，APF）在局部路径规划时的避障效果不良问题，提出一种APF-PSO的改进算法改善原算法优化路径规划的效果。将速度势场引入位置势场中使AGV（automated guided vehicle）动态避开不同速度的移动障碍物；当算法陷入局部最小值时，采取PSO（particle swarm optimization）算法，并对其惯性权重因子和学习因子做出调整，通过三次样条曲线插值来平滑路径，使得AGV找到最短路径。结果表明APF-PSO改进算法可根据障碍物速度不同动态避障，解决了APF算法运算中避障效果不良问题。

关键词: 局部路径规划, 人工势场法改进, 斥力, 局部最小值, 收敛效率

Abstract: Aiming at the problem of poor obstacle avoidance effect of the original APF（artificial potential field） algorithm in local path planning, an improved APF-PSO algorithm is proposed to improve the effect of original algorithm for optimizing path planning. The velocity potential field is introduced into the position potential field to make the AGV（automated guided vehicle） dynamically avoid moving obstacles of different speeds. When the algorithm falls into a local minimum, the PSO algorithm is adopted and its inertia weight factor and learning factor are adjusted, the path is smoothed by cubic spline interpolation, so that the AGV can find the shortest path. The results show that the improved APF-PSO algorithm can dynamically avoid obstacles according to different obstacle speeds, and solve the problem of poor obstacle avoidance effects in the calculation of APF algorithm.

Key words: local path planning, improvement of artificial potential field method, repulsion, local minimum, convergence efficiency

丁承君, 阎欣怡, 冯玉伯, 贾丽臻. 基于APF的AGV局部路径规划改进算法研究[J]. 计算机工程与应用, 2022, 58(22): 305-312.

DING Chengjun, YAN Xinyi, FENG Yubo, JIA Lizhen. Improved Algorithm of AGV Local Path Planning Based on APF[J]. Computer Engineering and Applications, 2022, 58(22): 305-312.

参考文献

[1] 赵江，孟晨阳，王晓博，等.特征点提取下的AGV栅格法建模与分析[J].计算机工程与应用，2022，58（5）：156-167.
ZHAO J，MENG C Y，WANG X B，et al.Modeling and analysis of AGV grid method based on feature point extraction[J].Computer Engineering and Application，2022，58（8）：156-167.
[2] 陈秋莲，郑以君，蒋环宇，等.基于神经网络改进粒子群算法的动态路径规划[J].华中科技大学学报（自然科学版），2021，49（2）：51-55.
CHEN Q L，ZHENG Y J，JIANG H Y，et al.Improved particle swarm optimization algorithm based on neural network for dynamic path planning[J].Journal of Huazhong University of Science and Technology（Natural Science Edition），2021，49（2）：51-55.
[3] 王洪斌，尹鹏衡，郑维，等.基于改进的A*算法与动态窗口法的移动机器人路径规划[J].机器人，2020，42（3）：346-353.
WANG H B，YIN P H，ZHENG W，et al.Mobile robot path planning based on improved A* algorithm and dynamic window method[J].Robotics，2020，42（3）：346-353.
[4] LIU Z Q，WANG X H，LI K Y，et al.Research on path planning of multi-rotor UAV based on improved artificial potential field method[J].MATEC Web of Conferences，2021，336：07006.
[5] AZMI M Z，ITO T.Artificial potential field with discrete map transformation for feasible indoor path planning[J].Applied Sciences，2020，10（24）：8987.
[6] WANG Z，LI G F，REN J.Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm[J].Computer Communications，2021，166：49-56.
[7] MA X，LIN Z，SONG R.Local path planning for unmanned surface vehicle with improved artificial potential field method[J].Journal of Physics Conference Series，2020，1634：012125.
[8] 段书用，王启帆，韩旭，等.具有确保安全距离的A*路径优化方法[J].机械工程学报，2020，56（18）：205-215.
DUAN S Y，WANG Q F，HAN X，et al.Improved A-star algorithm for safety insured optimal path with smoothed corner turns[J].Journal of Mechanical Engineering，2020，56（18）：205-215.
[9] WANG N，XU H W，LI C Z，et al.Hierarchical path planning of unmanned surface vehicles：a fuzzy artificial potential field approach[J].International Journal of Fuzzy Systems，2021，23：1797-1808.
[10] HE Y Y，LI H Y，WANG S，et al.Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression[J].Neurocomputing，2021，430：121-137.
[11] 丁承君，王鑫，冯玉伯，等.基于粒子群优化算法的AGV路径规划[J].传感器与微系统，2020，39（8）：123-126.
DING C J，WANG X，FENG Y B，et al.AGV path planning based on PSO algorithm[J].Transducer and Microsystem Technologies，2020，39（8）：123-126.
[12] MOHANTY P K，DEWANG H S.A smart path planner for wheeled mobile robots using adaptive particle swarm optimization[J].Journal of the Brazilian Society of Mechanical Sciences and Engineering，2021，43（2）：101.
[13] 姜建国，田旻，王向前，等.采用扰动加速因子的自适应粒子群优化算法[J].西安电子科技大学学报，2012，39（4）：74-80.
JIANG J G，TIAN M，WANG Q Q，et al.Adaptive particle swarm optimization via disturbing acceleration coefficents[J].Journal of Xidian University，2012，39（4）：74-80.
[14] YU X，QIAO Y.Enhanced comprehensive learning particle swarm optimization with dimensional independent and adaptive parameters[J].Computational Intelligence and Neuroscience，2021：6628564.
[15] 刘星辰，贾俊铖，张莉，等.图像超分辨率特征浓缩网络[J].计算机工程与应用，2021，57（16）：213-219.
LIU X C，JIA J C，ZHANG L，et al.Feature concentration network for image super-resolution[J].Computer Engineering and Applications，2021，57（16）：213-219.

基于APF的AGV局部路径规划改进算法研究

Improved Algorithm of AGV Local Path Planning Based on APF

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