TY - GEN
T1 - A novel path planning method for wheel-legged unmanned vehicles based on improved ant colony algorithm
AU - Zhao, Jing
AU - Li, Hongcai
AU - Yang, Chao
AU - Wang, Weida
N1 - Publisher Copyright:
© 2021 The Society of Instrument and Control Engineers-SICE.
PY - 2021/9/8
Y1 - 2021/9/8
N2 - Wheel-legged unmanned vehicles combine the efficiency of wheeled mobility with the terrain adaptability of legged mobility, and perform well in the exploration, rescue and other tasks of complex mountain terrain. The successful completion of tasks in complex mountain terrain depends on good perception, decision and planning of the wheel-legged unmanned vehicles, among which excellent path planning will help to improve the efficiency of passing. A novel path planning method based on improved ant colony algorithm for wheel-legged unmanned vehicles is proposed in this paper. Combined with the mechanical structure characteristics of wheel-legged vehicles, the obstacle crossing parameters are added to the heuristic function to relax the obstacle constraint in the path search, which is an improvement of the path planning algorithm. Through a 3D terrain map to carry out simulation experiments, the results show that the improved ant colony algorithm has faster convergence speed, and the search path is closer to the linear distance.
AB - Wheel-legged unmanned vehicles combine the efficiency of wheeled mobility with the terrain adaptability of legged mobility, and perform well in the exploration, rescue and other tasks of complex mountain terrain. The successful completion of tasks in complex mountain terrain depends on good perception, decision and planning of the wheel-legged unmanned vehicles, among which excellent path planning will help to improve the efficiency of passing. A novel path planning method based on improved ant colony algorithm for wheel-legged unmanned vehicles is proposed in this paper. Combined with the mechanical structure characteristics of wheel-legged vehicles, the obstacle crossing parameters are added to the heuristic function to relax the obstacle constraint in the path search, which is an improvement of the path planning algorithm. Through a 3D terrain map to carry out simulation experiments, the results show that the improved ant colony algorithm has faster convergence speed, and the search path is closer to the linear distance.
KW - Ant colony algorithm
KW - Heuristic function
KW - Path planning
KW - Wheel-legged unmanned vehicles
UR - http://www.scopus.com/inward/record.url?scp=85117696430&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85117696430
T3 - 2021 60th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2021
SP - 696
EP - 701
BT - 2021 60th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2021
Y2 - 8 September 2021 through 10 September 2021
ER -