TY - GEN
T1 - Path Planning of Omnidirectional Mobile Vehicle Based on Road Condition
AU - Ding, Yazhe
AU - Ma, Hongbin
AU - Li, Shan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - This paper concerns with the vehicle path planning problem considering the actual natural environment and social environment, which may influence the reliability and stability. The basic path planning problem is determined to solve the optimal path problem. In here, the surface properties and terrain has been taken into consideration, based on improved ant colony algorithm(ACA). Unlike the assumptions of basic ACA, in this case the terrain slope and the surface condition are analyzed and described in order to set up terrain table and vehicle capacity table. Therefore, coordinating the terrain and the obstacles condition, the reliability is enhanced. For instance, this paper optimizes the initial pheromone distribution, path point selection strategy, pheromone updating method, upon the shortage of basic ACA, such as low initial search speed, poor convergence, the problem of local optimum. Eventually, the results of simulation are exhibited, which shows the model and algorithm's efficiency.
AB - This paper concerns with the vehicle path planning problem considering the actual natural environment and social environment, which may influence the reliability and stability. The basic path planning problem is determined to solve the optimal path problem. In here, the surface properties and terrain has been taken into consideration, based on improved ant colony algorithm(ACA). Unlike the assumptions of basic ACA, in this case the terrain slope and the surface condition are analyzed and described in order to set up terrain table and vehicle capacity table. Therefore, coordinating the terrain and the obstacles condition, the reliability is enhanced. For instance, this paper optimizes the initial pheromone distribution, path point selection strategy, pheromone updating method, upon the shortage of basic ACA, such as low initial search speed, poor convergence, the problem of local optimum. Eventually, the results of simulation are exhibited, which shows the model and algorithm's efficiency.
KW - Inhomogeneous pheromone
KW - Path planning
KW - Selection strategy
UR - http://www.scopus.com/inward/record.url?scp=85072404816&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2019.8816402
DO - 10.1109/ICMA.2019.8816402
M3 - Conference contribution
AN - SCOPUS:85072404816
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 1425
EP - 1429
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
Y2 - 4 August 2019 through 7 August 2019
ER -