Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

García, Miquel Barceló; Montiel, Oscar; Castillo, Oscar; Sepúlveda, Roberto; Melín, Patricia

doi:10.1016/j.asoc.2009.02.014

Cited by 351 publications

(174 citation statements)

References 3 publications

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“…Garcia et al [237] uses an ACO to solve the problem of path planning for mobile robots with obstacle avoidance. The workspace (search space) is discretized in a matrix of 50 × 50 nodes, where the mobile robot is able to navigate and build paths.…”

Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

View full text Add to dashboard Cite

show abstract

“…Step 1 : An OLMS (6) Evaluate every particle (7) Find (8) Find local best particle (9) Execute communication rules (a) (10) Find global best particle (11) Execute communication rules (b) (12) Update particle's velocity V(t) (13) Update particle's position X(t) (14) Execute point repair algorithm (15) Execute smoothness algorithm (16) Adjust each particle's moving direction (17) Dissolve elementary membrane…”

Section: Mmpsomentioning

confidence: 99%

“…The representative heuristic approaches for solving MR3P are neural networks, genetic algorithms [15], ant colony optimization, fuzzy logic [16], simulated annealing [17], PSO [21], probabilistic road maps, rapidly exploring random trees, etc. Although heuristic methods do not guarantee to find an optimal solution, they may be faster and may have higher efficiency than classical methods [19].…”

Section: Related Workmentioning

confidence: 99%

A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning

Wang

Zhang

Zhao

et al. 2015

INT J COMPUT COMMUN

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To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO.

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“…In the local navigation, the robot can decide or control its motion and orientation autonomously using equipped sensors such as ultrasonic range finder sensors, sharp infrared range sensors, and vision (camera) sensors, etc. Fuzzy logic [10], Neural network [11], Neuro-fuzzy [12], Genetic algorithm [13], Particle swarm optimization algorithm [14], Ant colony optimization algorithm [15], and Simulated annealing algorithm [16], etc. are successfully employed by various researchers to solve the local navigation problem.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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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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Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

Cited by 351 publications

References 3 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Modified Membrane-Inspired Algorithm Based on Particle Swarm Optimization for Mobile Robot Path Planning

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

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