Research on path planning of mobile robot based on improved ant colony algorithm
- 28 Downloads
To solve the problems of local optimum, slow convergence speed and low search efficiency in ant colony algorithm, an improved ant colony optimization algorithm is proposed. The unequal allocation initial pheromone is constructed to avoid the blindness search at early planning. A pseudo-random state transition rule is used to select path, the state transition probability is calculated according to the current optimal solution and the number of iterations, and the proportion of determined or random selections is adjusted adaptively. The optimal solution and the worst solution are introduced to improve the global pheromone updating method. Dynamic punishment method is introduced to solve the problem of deadlock. Compared with other ant colony algorithms in different robot mobile simulation environments, the results showed that the global optimal search ability and the convergence speed have been improved greatly and the number of lost ants is less than one-third of others. It is verified the effectiveness and superiority of the improved ant colony algorithm.
KeywordsPath planning Ant colony algorithm Mobile robot Pheromone
This study was supported by Chongqing Municipal Education Commission (Grant No. KJ1601032), Chongqing Engineering Research Center for Advanced Intelligent Manufacturing Technology (Grant No. 2019yjzx0101).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no competing interests.
- 2.Zhu TX, Dong GJ, Yan BX et al (2016) Research for the path planning of the agricultural robot based on the improved ant colony algorithm. J Agric Mech Res 9:48–52Google Scholar
- 3.Luo Q, Wang HB, Cui XJ et al (2018) Research on autonomous navigation system of warehousing mobile robot based on improved artificial potential field method in dynamic environment. Appl Res Comput. https://doi.org/10.3969/j.issn.1001-3695.2018.09.0640 Google Scholar
- 10.Dorigo M, Gambardella LM (1996) A study of some properties of Ant-Q. In: Proceedings of the 4th international conference on parallel problem solving from nature, pp 656–665Google Scholar
- 13.Liu CA, Yan XH, Liu CY et al (2011) Dynamic path planning for mobile robot based on improved ant colony optimization algorithm. Acta Electronica Sinica 39(5):1220–1224Google Scholar
- 14.Zeng MR, Xu XY, Liu L et al (2015) Improved ant colony optimization with potential field heuristic for robot path planning. Comput Eng Appl 51(22):33–37Google Scholar
- 17.Zhang YL, Niu XM (2011) Simulation research on mobile robot path planning based on ant colony optimization. Comput Simul 28(6):231–234Google Scholar
- 18.Zhou MX, Cheng K, Wang ZX (2013) Improved ant colony algorithm with planning of dynamic path. Comput Sci 40(1):314–316Google Scholar
- 19.Duan HB (2005) Ant colony algorithms: theory and applications. Science Press, Beijing, pp 1–420Google Scholar
- 20.Liu JH, Yang JG, Liu HP et al (2015) Robot global path planning based on ant colony optimization with artificial potential field. Trans Chin Soc Agric Mach 46(9):18–27Google Scholar
- 21.Zhang C, Ling YZ, Chen MY (2016) Path planning of mobile robot based on an improved ant colony algorithm. J Electron Meas Instrum 30(11):1758–1764Google Scholar
- 22.Dong SW, Hua FY (2011) Path planning of mobile robot in dynamic environments. In: 2nd International conference on intelligent control and information processing (ICICIP). IEEE, vol 2, 691–696Google Scholar
- 23.Qu H, Huang LW, Ke X (2015) Research of improved ant colony based robot path planning under dynamic environment. J Univ Electron Sci Technol China 44(2):260–265Google Scholar
- 24.Ouyang XY, Yang SG (2014) Obstacle avoidance path planning of mobile robots based on potential grid method. Control Eng China 21(1):134–137Google Scholar
- 25.Zhu QB, Zhang YL (2005) An ant colony algorithm based on grid method for mobile robot path planning. Robot 27(2):132–136Google Scholar
- 26.Shi EX, Chen MM, Jun L et al (2014) Research on method of global path-planning for mobile robot based on ant-colony algorithm. Trans Chin Soc Agric Mach 45(6):53–57Google Scholar