Research on path planning of mobile robot based on improved ant colony algorithm

Abstract

At present, the problem of global path planning research most concentrated in the known information conditions. But the path planning of mobile robot is still an important problem to be solved in dynamic environment. In this paper, the ant colony algorithm is improved to solve the robot path planning problem in the unknown environment. Introduction In the early 90 century, Italy scholar M.Dorigo [1] proposed the ant colony algorithm, inspired by the foraging behavior of ants in nature. As the ant colony algorithm proposed, many scholars have a great interest in it. The application of the ant colony algorithm is the combination of optimization problem initially. Currently the application of ant colony algorithm has gradually extended to engineering science and technology field, such as the traveling salesman problem(TSP)、 scheduling problem, graph coloring problem, communication network routing problem, vehicle routing problem, mobile robot path planning and so on[2]. Ant colony algorithm is a typical local path planning algorithm. In the process of searching the path, each ant is based on the information of its environment, and carries out real-time route planning according to some simple rules[3]. Although each ant acts a simple behavior, many ants in the ant colony that interact with the environment will show a complex and flexible behavior. The advantages of the ant colony algorithm: It has a strong robustness: the ant colony algorithm model can be modified to resolve other problem[4]. It is an essentially parallel algorithm. Because of large-scale parallel computing, it can significantly reduce the computation time. It is a positive feedback algorithm. The positive feedback accelerates the search convergence. Ant algorithm is easy to combine with a variety of heuristic algorithms to improve the performance of the algorithm[5]. However, this algorithm also has some defects,Compared with other methods, this algorithm generally takes longer search time[6]. Although the speed of computer Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015) © 2015. The authors Published by Atlantis Press 1085 computing and the nature of ant colony algorithm can reduce some problem. But this is still a big obstacle for some large-scale optimization problems. In addition, the ant colony algorithm is easy to appear stagnation behavior. After searching to some extent, all the individuals have the same solution. It cannot be further searching for the rest solution space, so it cannot find a better solution. In this paper, we will improve the ant colony algorithm, and the improved algorithm will overcome its defects, and make it have faster convergence. Improvement of algorithm In order to guarantee the algorithm can converge to the global optimal solution or approximate optimal solution and solve the deadlock problem, the following improvement is done in this chapter. Transfer probability adjustment. In order to increase the multiplicity of solutions, we can use the random selection strategy in the transfer probability. First, we set a random selection parameter ) 1 , 0 ( 0  q ,q is a random number between 0 and 1. When 0 q q  , we select any feasible node around the current node r randomly. Otherwise, the select the next feasible node according formula(1).      otherwise n q q allowed rand n k 0 ) ( (1) In which, rand(allowedk) represents to selects a node randomly from allowedk, n is represented the next node chosen by the transfer probability formula. In addition, two heuristic factors are added to the transfer probability formula in order to enhancing the global search capability of the improved ant colony algorithm.The new transfer probability formula is shown below:              otherwise allowed s G S t t G S t t