Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
SCG '85 Proceedings of the first annual symposium on Computational geometry
Future Generation Computer Systems
Robot Motion Planning
A Study of Some Properties of Ant-Q
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Planning multiple paths with evolutionary speciation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mobile robot navigation in 2-D dynamic environments using an electrostatic potential field
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization of route planning and exploration using multi agent system
Multimedia Tools and Applications
Optimisation of autonomous ship manoeuvres applying Ant Colony Optimisation metaheuristic
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Path planning is an important task in mobile robot control. When the robot must move rapidly from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. In this paper, an obstacle avoidance path planning approach for mobile robots is proposed by using Ant-Q algorithm. Ant-Q is an algorithm in the family of ant colony based methods that are distributed algorithms for combinatorial optimization problems based on the metaphor of ant colonies. In the simulation, we experimentally investigate the sensitivity of the Ant-Q algorithm to its three methods of delayed reinforcement updating and we compare it with the results obtained by other heuristic approaches based on genetic algorithm or traditional ant colony system. At last, we will show very good results obtained by applying Ant-Q to bigger problem: Ant-Q find very good path at higher convergence rate.