TY - GEN
T1 - Mobile robot path planning in complex environments using ant colony optimization algorithm
AU - Uriol, Ronald
AU - Moran, Antonio
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/7
Y1 - 2017/6/7
N2 - Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.
AB - Ant Colony Optimization (ACO) algorithm has been applied to solve the path planning problem of mobile robot in complex environments. The algorithm parameters have been analysed and tuned for different working areas with obstacles in different number, sizes and shapes. Also, the performance of ACO algorithm was tested for different resolutions of working area representations. In all cases, it was possible to find optimal or near-optimal minimum-length paths from the initial to final desired positions without collision with obstacles or wall-borders.
KW - Ant colony optimization
KW - Combinatorial optimization
KW - Mobile robots
KW - Path planning
UR - http://www.scopus.com/inward/record.url?scp=85022340341&partnerID=8YFLogxK
U2 - 10.1109/ICCAR.2017.7942653
DO - 10.1109/ICCAR.2017.7942653
M3 - Conference contribution
AN - SCOPUS:85022340341
T3 - 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
SP - 15
EP - 21
BT - 2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
Y2 - 22 April 2017 through 24 April 2017
ER -