Computational Intelligence and Neuroscience

Volume 2019, Article ID 1932812, 10 pages

https://doi.org/10.1155/2019/1932812

## Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, Hubei, China

Correspondence should be addressed to Guoliang Chen; nc.ude.tuhw@nehclg

Received 30 November 2018; Accepted 8 April 2019; Published 6 May 2019

Academic Editor: Carmen De Maio

Copyright © 2019 Guoliang Chen and Jie Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

For the problem of mobile robot’s path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.

#### 1. Introduction

Nowadays, one of the prime concerns of mobile robot is path planning. A path planning optimization method is proposed to calculate the shortest collision free path from source to destination by avoiding static as well as dynamic obstacles. Therefore, it is necessary to select an appropriate optimization technique for optimization of paths [1, 2]. Concerning this problem, scholars proposed a lot of path planning methods, including APF [3], grid method [4], and ACO [5] and some intelligent methods, like fuzzy logic [6], neural network [7], genetic algorithm [8], artificial bee colony (ABC) [9], and particle swarm optimization (PSO) [10]. Among these methods, grid method can find an optimal path, but its efficiency is affected by environment and grid density. Neural network is good at learning, but its net structure is large, and its neuron thresholds change with time under the condition of multiobstacle and dynamic environment. Genetic algorithm has a good global search capability, but its search space is large, and its model is needed to reestablish with environment changing. ABC has the advantages of simple calculation, few parameters, and easy implementation. But, ABC also has some disadvantages such as a slow convergence. In addition, ABC is first applied to solve the problems of function optimization, while the path planning of mobile robot is a combinatorial optimization problem. Therefore, there are some difficulties in algorithm construction. PSO has a rather fast speed of approaching the optimal solution, which is an advantage to solve optimization problems of continuous systems. But, PSO also has some deficiencies, including the premature convergence and poor local optimization capability.

APF is a popular approach for solving path planning problems because of its simplicity, fast execution time, and applicability to mobile robot, unmanned aerial vehicles. Li et al. [11] proposed an innovative yaw angle-based APF function to achieve the desired road trajectory within certain road boundary and minimize the yaw angle change rate of the autonomous electric vehicle. Rasekhipour et al. [12] developed a path planning controller based on APF for Autonomous Road Vehicles which was able to consider any PF for obstacles and road structures while calculating the optimal path. Malone et al. [13] utilized the APF framework for runtime planning and leveraged a method of stochastic reachable sets to generate accurate potential fields for moving obstacles. APF easily traps into a local optimum, which leads the robot to be into a deadlock because of its zero resultant force. Many improved algorithms are developed to fix these defects of APF. Sun et al. [14] proposed an improvement based on a distance factor and jump strategy to solve the various situations of the target unreachable problem of the traditional APF. Liu et al. [15] introduced a critical value based on the distance between the robot and the obstacle to modify the repulsion direction for avoiding the local minimum point. Some scholars attempt to combine the intelligent algorithm with APF to eliminate the defects of APF. Pan et al. [16] used the fuzzy controller to improve APF method and safeguarded the reliability of the path planning and path smoothness.

ACO is an intelligence-optimized algorithm that simulates the heuristic mechanism of the shortest route based on pheromone in the process of ants foraging for food [17]. ACO has been used in mobile robot’s path planning. Akbarimajd and Hassanzadeh [18] presented a path planning method for mobile robots based on two-dimensional cellular automata, which can be applied for environments with both concave and convex obstacles and be appropriate for multirobot problems as well as dynamic environments. Montiel-Ross et al. [19] developed the navigation software called Ant Colony Test Center. This software based on ant colonies is able to achieve path generation, path planning, and virtual path tracking at once. You et al. [20] designed a new dynamic search model for path planning problem of mobile robot, which increases the diversity of the ant population by using a bigger parameter in the prophase and accelerated convergence by using a smaller parameter to adjust the attenuation model in the anaphase. However, ACO has some problems such as the premature convergence and ant colony lost. For the inherent shortcomings of ACO, there are many improved methods that can be classified into improvements based on single classical ACO and improvements based on mixed algorithms combined ACO and other intelligent algorithm. Liu and Hu [21] proposed the multigranularity pattern ant colony algorithm, in which ants had different window sizes to enhance the diversity of the ants. The proposed algorithm was used for UAV path planning problem. Wang et al. [22] applied genetic algorithm to optimization and configuration parameters of the basic ant colony algorithm. Hsu and Juang [23] proposed a multiobjective, rule-coded, advanced, continuous-ant-colony optimization algorithm that was applied to design the fuzzy controller of wall-following control for a mobile robot.

Of course, ACO has more applications than just robot path planning, and there are other metaheuristic methods that can be used for reference or application in robot path planning. Chu and Tsai [24] proposed a new optimization algorithm of cat swarm optimization (CSO), and CSO is generated by observing the behavior of cats and composed of seeking mode and tracking mode by simulating the behavior of cats. The proposed CSO somehow belongs the swarm intelligence, which may give much better performance than PSO with a weighting factor. The studies deserve sustained attention. For numerical optimization problems, Tsai et al. [25] proposed an enhanced ABC optimization algorithm, in which the onlooker bee is designed to move straightly to the picked coordinate indicated by the employed bee and evaluates the fitness values near it in the original ABC, and the concept of universal gravitation is introduced into the consideration of the affection between employed bees and the onlooker bees. Aiming at the weaknesses of computational swarm intelligence such as particle swarm optimization (PSO), Ever [26] utilized simplified swarm optimization (SSO) to enhance the path planning of mobile robot in working environment with irregular obstacles, which driven from an inspiration of communal behavior of birds flocking and fish schooling.

APF is a typical online algorithm for robot path planning and has a simple and beautiful mathematical description. So, APF is still very attractive in robot path planning, although it has some inherent limitations and no learning ability. ACO uses all paths of the entire ant colony to describe the solution space of an optimization problem, and obtains the best path through positive feedback based on pheromone. The idea of ACO directly maps the mobile robot’s path optimization problem, which is easy to understand and has very intuitive results. Based on the above reasons, this paper combines APF and ACO to research the problem of mobile robot’s path planning under the condition of known robot’s motion environment described by grid map and proposes a path planning algorithm of ACO mixed with an improved APF. The proposed algorithm can provide an initial path obtained by an improved APF to be the inspired factors of ACO for avoiding its premature convergence and local optimum.

This paper is organized in the following manner: Section 2 presents the basic approach of building a grid map of robot’s environment and describes the problem of robot path planning based on the grid map. Section 3 proposes some improved methods about APF after analyzing the existing defects of APF applied to the grid map. Section 4 develops some improved methods about transition algorithm of ant colony. Section 5 demonstrates results of the proposed mixed path planning method by computer simulation experiments and mobile robot’s motion experiments in real environments. Finally, the contributions of this paper are summarized in Section 6.

#### 2. Rasterizing Environment Map and Problem Description

The work space of robot is set to a limited area on a two-dimensional plane. The coordinates of obstacles edge points can be determined by processing the original environment map. If we expand the size of the obstacles to a safe distance (usually taken as mobile robot’s radius) and add the size of the mobile robot to the obstacles, the robot can be seen as a scale-free particle, and the grid granularity can be calculated aswhere represents the grid size; , , and are the weighing coefficients; is the map size; and and is mobile robot’s size and safe distance.

In this paper, the size of the environment map is (pixel unit), and then the robot environment can be divided into ( means integer arithmetic) grid units.

Grids are numbered from left to right and from top to bottom. Meanwhile, grids are assigned respectively to distinguish whether the grid is an obstacle space or a free space. The values of grid units are decided by the state value of these pixels. The grid is an obstacle grid if the amount of pixel with value 0 exceeds 1 in the area of the grid and defined as 0 or 1. Eventually, the typical grid model is obtained as shown in Figure 1.