|Table of Contents|

BFO-ACO roaming path planning based on multi-constraint scenarios(PDF)

Journal of Shenzhen University Science and Engineering[ISSN:1000-2618/CN:44-1401/N]

Issue:
2022 Vol.39 No.4(363-488)
Page:
463-471
Research Field:
Electronics and Information Science

Info

Title:
BFO-ACO roaming path planning based on multi-constraint scenarios
Author(s):
LIN Xiaoling1;  WANG Zhiqiang1;  2;  GUO Yanyan2;  and ZHU Zexuan1
1) College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P. R. China
2) Information Center, Shenzhen University, Shenzhen 518060, Guangdong Province, P. R. China
Keywords:
computer application;  path planning;  multiple constraints;  roaming;  deadlock;  ant colony algorithm;  bacterial foraging algorithm;  virtual environment
PACS:
P315.69
DOI:
10.3724/SP.J.1249.2022.04463
Abstract:
In recent years, when the path planning based on ant colony algorithm is used to find the optimal path under multiple constraints, it is easy to fall into the local optimal solution and lead to slow convergence. Under the constraints of path length, number of effective scenic spots, path smoothness and path obstacle distance, this paper constructs a fitness function model to evaluate the quality of roaming path. A hybrid algorithm of bacterial foraging optimization and ant colony optimization (BFO-ACO) is proposed by using the taboo table optimization strategy to solve the deadlock problem in the traditional ant colony algorithm, which improves the path diversity in the early stage of the algorithm. The replication and dispersion mechanism of bacterial foraging algorithm is introduced to improve the convergence speed as well as jump out of the local optimum. Experimental results show that the BFO-ACO algorithm obtains a higher-quality roaming path with the fewer iterations under multi-constrained environments, which provides the foundation for designing roaming path.

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