Ant Colony System Algorithm for Real-Time Globally
Optimal Path Planning of Mobile Robots

Tan, Guozhen; He, Haibing; Sloman, Aaron

doi:10.1360/aas-007-0279

Cited by 108 publications

(35 citation statements)

References 10 publications

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“…In order to enhance generality of the algorithm, we can contrast with basic PSO, Dijkstra algorithm and the results of literature [18], which utilizes the Ant Colony System (ACS) algorithm to optimize the path, which shows in table II. In order to compare more performances of the algorithm, we compare convergence speed, mean value and standard deviation.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Robot Path Planning Based on Random Coding Particle Swarm Optimization

Su¹,

Wang²,

Hu³

2015

ijacsa

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Abstract-Mobile robot navigation is to find an optimal path to guide the movement of the robot, so path planning is guaranteed to find a feasible optimal path. However, the path planning problem must be solve two problems, i.e., the path must be kept away from obstacles or avoid the collision with obstacles and the length of path should be minimized. In this paper, a path planning algorithm based on random coding particle swarm optimization (RCPSO) algorithm is proposed to get the optimal collision-free path. Dijstra algorithm is applied to search a suboptimal collision-free path in our algorithm; then the RCPSO algorithm is developed to tackle this optimal path planning problem in order to generate the global optimal path. The crossover operator of genetic algorithm and random coding are introduced into the particle swarm optimization to optimize the location of the sub-optimal path. The experiment results show that the proposed method is effective and feasible compared with different algorithms.

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Section: Experiments and Resultsmentioning

confidence: 99%

Robot Path Planning Based on Random Coding Particle Swarm Optimization

Su¹,

Wang²,

Hu³

2015

ijacsa

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show abstract

“…ACO is a probabilistic algorithm proposed by Dorigo et al [134] in 1999, which is originated from bionics. Guan-Zheng et al [135] have presented the modern global path planning method for a mobile robot by applying Ant Colony System (ACS) algorithm and the Dijkstra algorithm. Purian & Sadeghian [136] have explored the optimal path for a mobile robot in an unknown dynamic environment using Ant Colony Optimization (ACO) algorithm and fuzzy controller.…”

Section: Ant Colony Optimization Algorithm and Other Nondeterministicmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

186

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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“…This process can be described as follows [5]: 1) find out the peripheral boundaries of all the obstacles, which are free MAKLINK lines, for example, 1 P P , and 10 3 P P in Figure 1; 2) the perpendicular line from each vertex of the peripheral boundaries to its adjacent boundary of the environment is a free MAKLINK line; find out all of this kind of free MAKLINK lines;…”

Section: Free Space Modeling Of Aircraftmentioning

confidence: 99%

Path Planning for Aircraft Based on MAKLINK Graph Theory and Multi Colony Ant Algorithm

Hao¹,

Zhi-feng²,

Zhao³

2009

2009 International Joint Conference on Computational Sciences and Optimization

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The task of path planning for aircraft has received considerable attention in the research literature. The problem involves computing a collision-free path between a start point and a target point in environment of known obstacles. In this paper, we investigate an obstacle avoidance path planning problem using the MAKLINK graph theory and multi ant colony system, in which several colonies of ants cooperate in finding optimal solution by exchanging good information. The result of computer simulation experiment shows that the proposed method is effective and can be used in the path planning of aircraft.

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Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots

Cited by 108 publications

References 10 publications

Robot Path Planning Based on Random Coding Particle Swarm Optimization

Robot Path Planning Based on Random Coding Particle Swarm Optimization

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Path Planning for Aircraft Based on MAKLINK Graph Theory and Multi Colony Ant Algorithm

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