Abstract
Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.
Highlights
Path planning is an important topic in navigation aspect for mobile robotics.[1]
Based on the good impact left in solving the quadratic assignment problem,[13] the job shop scheduling problem,[14] the travelling salesman problem,[15] the network routing selecting problem[16] and the vehicle routing problem,[17] due to its advantages such as parallel processing, distributed computing and strong robustness, the ant colony algorithm has been gradually applied in the field of mobile robot navigation.[18,19]
An improved ant colony algorithm was proposed and compared to the original one based on the performance delivered while solving the mobile robot path planning problem in grid maps
Summary
Path planning is an important topic in navigation aspect for mobile robotics.[1]. The aim of path planning is to find an optimal collision-free path between a starting point and a target in a given environment. Designed two fuzzy controllers to optimize three parameters a, b and g and produced a new evaluation criterion to select the best path[21]; Zaho and Fu adopted an improved two-way parallel searching strategy to accelerate the searching speed and used a new method that rationally distributes the initial pheromone to increase the convergence speed[22]; Chaari et al presented a new hybrid ant colony-genetic algorithm approach for fast path selection and global solution[23]; Huang and Zheng proposed an improved ant colony algorithm based on rolling window to show good analytical and disposing ability of dead ends in the path planning process[24]; and Cheng et al combined ant colony algorithm with simulated annealing algorithm to Figure 1.
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