Path Planning for Autonomous Underwater Vehicles: An Ant Colony Algorithm Incorporating Alarm Pheromone

Ma, Yining; Gong, Yue-Jiao; Xiao, Chufeng; Gao, Ying; Zhang, Mengjie

doi:10.1109/tvt.2018.2882130

Cited by 141 publications

(51 citation statements)

References 45 publications

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“…Few papers, likewise, researched GA with ACO and coordinate system [120]. A paper, which solves the CFPP problem with automated underwater vehicles (AUV), is studied [121]. This paper suggests an ACO based algorithm to solve large-scale CFPP problems.…”

Section: Refmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.

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Section: Refmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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“…Ant colony optimization has been successfully used for problem sets that can be discretized into a combinatory problem such as in path planning [66][67][68], trajectory optimization [21,69], and even in spacecraft load bearing [70].…”

Section: Ant Colony Optimization Search Algorithmmentioning

confidence: 99%

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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“…From the methodologies review on the solution approaches to solve the proposed problem, ALNS will be used in connection with the ant colony optimization. The ant colony optimization is used in the present study in combination with ALNS due to many researchers having proved that the ACO is good for path planning problems, scheduling problems, and assignment problems [34][35][36][37][38][39][40][41][42][43]. In the present study, the destroy and repair methods were used to determine and select a sequence of the farms which were assigned for pig production.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Large Neighborhood Search for a Production Planning Problem Arising in Pig Farming

Praseeratasang

Pitakaso

Sethanan

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

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This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480–620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.

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Path Planning for Autonomous Underwater Vehicles: An Ant Colony Algorithm Incorporating Alarm Pheromone

Cited by 141 publications

References 45 publications

A Performance Review of Collision-Free Path Planning Algorithms

A Performance Review of Collision-Free Path Planning Algorithms

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Adaptive Large Neighborhood Search for a Production Planning Problem Arising in Pig Farming

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