TY - GEN
T1 - Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization
AU - Yang, Siyuan
AU - Li, Dongguang
AU - Wang, Yuze
AU - Wang, Yue
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2024.
PY - 2024
Y1 - 2024
N2 - Path planning is a core to improve the autonomy of Unmanned Ground Vehicle (UGV). In autonomous navigation applications, the use of Ant Colony Optimization (ACO) in solving the path planning problem is difficult to obtain the global optimal solution, which make the waste of resources. Focus on fast optimization search, this paper proposes Fuzzy Logic Genetic Ant Colony Optimization (FLGACO), which adopt crossover and mutation operations in genetic algorithms. By using the fuzzy logic system, dynamic adjustment for pheromone and heuristic values can be realized. Simulation experiments on path planning for fast arrival were conducted using ACO,GA and FLGACO under the same map. The results show that FLGACO reduces the path length by 15% compared to ACO and 9% compared to genetic algorithm, which can effectively reduce the energy consumption and verify the feasibility and effectiveness of the improved method.
AB - Path planning is a core to improve the autonomy of Unmanned Ground Vehicle (UGV). In autonomous navigation applications, the use of Ant Colony Optimization (ACO) in solving the path planning problem is difficult to obtain the global optimal solution, which make the waste of resources. Focus on fast optimization search, this paper proposes Fuzzy Logic Genetic Ant Colony Optimization (FLGACO), which adopt crossover and mutation operations in genetic algorithms. By using the fuzzy logic system, dynamic adjustment for pheromone and heuristic values can be realized. Simulation experiments on path planning for fast arrival were conducted using ACO,GA and FLGACO under the same map. The results show that FLGACO reduces the path length by 15% compared to ACO and 9% compared to genetic algorithm, which can effectively reduce the energy consumption and verify the feasibility and effectiveness of the improved method.
KW - Ant Colony Optimization
KW - Fuzzy Logic
KW - Mobile Robot
KW - Path Planning
UR - http://www.scopus.com/inward/record.url?scp=85192874277&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-1099-7_40
DO - 10.1007/978-981-97-1099-7_40
M3 - Conference contribution
AN - SCOPUS:85192874277
SN - 9789819710980
T3 - Lecture Notes in Electrical Engineering
SP - 418
EP - 429
BT - Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6
A2 - Qu, Yi
A2 - Gu, Mancang
A2 - Niu, Yifeng
A2 - Fu, Wenxing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023
Y2 - 9 September 2023 through 11 September 2023
ER -
