Tower crane path planning based on improved ant colony algorithm

Abstract

In order to solve the problem of path planning of tower cranes, an improved ant colony algorithm was proposed. Firstly, the tower crane was simplified into a three-degree-of-freedom mechanical arm, and the D-H motion model was established to solve the forward and inverse kinematic equations. Secondly, the traditional ant colony algorithm was improved. The heuristic function was improved by introducing the distance between the optional nodes and the target point into the function. Then the transition probability was improved by introducing the security factor of surrounding points into the transition probability. In addition, the local path chunking strategy was used to optimize the local multi-inflection path and reduce the local redundant inflection points. Finally, according to the position of the hook, the kinematic inversion of the tower crane was carried out, and the variables of each joint were obtained. More specifically, compared with the traditional ant colony algorithm, the simulation results showed that improved ant colony algorithm converged faster, shortened the optimal path length, and optimized the path quality in the simple and complex environment.

Keywords

tower crane / ant colony algorithm / transition probability / local path chunking strategy / path planning

References

[1] SHEN J, WANG Y, ZOU D. A review of artificial intelligence in tower management//The 7th BIM Technology International Exchange Conference on Intelligent Construction and Building Industrialization Innovation Development, September 23-25, 2020, Guangzhou, China. Beijing: Journal of Information Technology in Civil Engineering and Architecture, 2020: 7.

[2] DUTTA S, CAI Y Y, HUANG L H, et al. Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments. Automation in Construction, 2020, 110: 102998.

[3] KHODABANDELU A, PARK J, ARTEAGA C. Improving multitower crane layout planning by leveraging operational flexibility related to motion paths. Journal of Management in Engineering, 2023, 39(5): 04023035.

[4] CHAARI I, KOUBAA A, BENNACEUR H, et al. Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments. International Journal of Advanced Robotic Systems, 2017, 14(2): 1729881416663663.

[5] MONTIEL O, SEPÚLVEDA R, OROZCO-ROSAS U. Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. Journal of Intelligent & Robotic Systems, 2015, 79(2): 237-257.

[6] ZHOU W, XU J. RRT*unmanned ship path planning algorithm based on improved artificial potential field method. Journal of North University of China (Natural Science Edition), 2024. 45(2): 123-131.

[7] LUAN P G, THINH N T. Hybrid genetic algorithm based smooth global-path planning for a mobile robot. Mechanics Based Design of Structures and Machines, 2023, 51(3): 1758-1774.

[8] WU G, WAN L. Research on particle swarm algorithm to optimize robot path planning. Mechanical Science and Technology, 2021, 40: 1-7.

[9] ZHANG S, PU J, SI Y, et al. A review of the application of ant colony algorithm in mobile robot path planning. Computer Engineering and Applications, 2020. 56(8): 10-19.

[10] LUO Q, WANG H B, ZHENG Y, et al. Research on path planning of mobile robot based on improved ant colony algorithm. Neural Computing and Applications, 2020, 32(6): 1555-1566.

[11] LI C, HUANG Y, LIU J. Improved ant colony algorithm path planning based on location and energy inspiration. Sensors and Microsystems, 2024, 43(10): 132-136.

[12] YANG P, ZHAO Z, ZHENG H. Research on global path planning method of mobile robot based on improved ant colony algorithm. Machinery Manufacturing and Automation, 2017. 46(6): 155-157.

[13] XIE Z, LU D, WANG J, et al. Research on robot path planning based on improved ant colony algorithm. Mechanics and Electronics, 2019, 37(6): 70-74.

[14] ZHAO H, LEI C, JIANG. Path planning of six-degree-of-freedom manipulator based on improved ant colony algorithm.Journal of Zhengzhou University (Science Edition), 2020, 52(1): 120-126.

[15] ZHANG T, SU J. Robot end path sequencing optimization based on improved ant colony algorithm. China Mechanical Engineering, 2016, 27(19): 2624-2629.

[16] FENG Y, WAN G, ZENG P. Improved ant colony algorithm search strategy in three-dimensional path planning. Computer Engineering and Design. 2023, 44(12): 3613-3620.

[17] LÜ Y, LIU F, ZHENG L, et al. Application analysis of general and modified D-H method in kinematics modeling. Computer System Application, 2016, 25(5): 197-202.

[18] PENG G, DONG H, MA B. Comparative research on robot kinematics modeling of two DH models. Mechanical Research and Application, 2019, 32(6): 62-65.