TY - GEN
T1 - Global path planning using improved ant colony optimization algorithm through bilateral cooperative exploration
AU - Lee, Joon Woo
AU - Lee, Dong Hyun
AU - Lee, Ju Jang
PY - 2011
Y1 - 2011
N2 - We proposed the Heterogeneous Ant Colony Optimization (HACO) algorithm to solve the global path planning problem for autonomous mobile robot in the previous paper. The HACO algorithm was modified and optimized to solve the global path planning problem unlike the conventional ACO algorithm which was proposed to solve the Traveling Salesman Problem (TSP) or Quadratic Assignment Problem (QAP). However, there is a common shortcoming in the ACO algorithms for global path planning, including HACO algorithm. Ants carry out the exploration task relatively well around the starting point. On the other hand, they are hindered in their work as they approached the goal point, because they are attracted by the intensity of heuristic value and the accumulated pheromone while the ACO algorithm works. As a result, they have a strong tendency not to explore and most of them follow the path that found in the beginning of the search. This could cause the local optimal solutions. Thus, we propose a way to solve this problem in this paper. It is the Bilateral Cooperative Exploration (BCE) method. The BCE is the idea that performs the search task again by changing the goal point into the starting point and vice versa. The simulation shows the effectiveness of the proposed method.
AB - We proposed the Heterogeneous Ant Colony Optimization (HACO) algorithm to solve the global path planning problem for autonomous mobile robot in the previous paper. The HACO algorithm was modified and optimized to solve the global path planning problem unlike the conventional ACO algorithm which was proposed to solve the Traveling Salesman Problem (TSP) or Quadratic Assignment Problem (QAP). However, there is a common shortcoming in the ACO algorithms for global path planning, including HACO algorithm. Ants carry out the exploration task relatively well around the starting point. On the other hand, they are hindered in their work as they approached the goal point, because they are attracted by the intensity of heuristic value and the accumulated pheromone while the ACO algorithm works. As a result, they have a strong tendency not to explore and most of them follow the path that found in the beginning of the search. This could cause the local optimal solutions. Thus, we propose a way to solve this problem in this paper. It is the Bilateral Cooperative Exploration (BCE) method. The BCE is the idea that performs the search task again by changing the goal point into the starting point and vice versa. The simulation shows the effectiveness of the proposed method.
KW - Ant Colony Optimization (ACO) algorithm
KW - Bilateral Cooperative Exploration (BCE)
KW - Global Path Planning
KW - Heterogeneous Ants
UR - http://www.scopus.com/inward/record.url?scp=80055061057&partnerID=8YFLogxK
U2 - 10.1109/DEST.2011.5936607
DO - 10.1109/DEST.2011.5936607
M3 - Conference contribution
AN - SCOPUS:80055061057
SN - 9781457708725
T3 - IEEE International Conference on Digital Ecosystems and Technologies
SP - 109
EP - 113
BT - Proceedings of the 5th IEEE International Conference on Digital Ecosystems and Technologies, DEST 2011
T2 - 5th IEEE International Conference on Digital Ecosystems and Technologies, DEST 2011
Y2 - 31 May 2011 through 3 June 2011
ER -