Mobile robot path planning using an improved ant colony optimization

Akka, Khaled; Khaber, Farid

doi:10.1177/1729881418774673

Cited by 107 publications

(53 citation statements)

References 21 publications

(27 reference statements)

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“…In Table 6, additional information from previous research related to PSO is shown. Ant colony optimization (ACO) is usually studied under varying network environments, such as grid network and Voronoi diagram [107][108][109][110][111][112][113][114][115][116][117][118][119]. Few papers, likewise, researched GA with ACO and coordinate system [120].…”

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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“…To verify the performance of the improved algorithm, the ACA [20], SP-ACA [24], and CP-ACA are tested separately using a grid test model (30 * 30 squares) with obstacles. All experiences for the grid test model are implemented using R2016b as the operating environment with an Intel core i5-2410 processor and 10.0 GB memory.…”

Section: Grid Test Modelmentioning

confidence: 99%

“…All experiences for the grid test model are implemented using R2016b as the operating environment with an Intel core i5-2410 processor and 10.0 GB memory. e parameters of the ACA are set as α � 1, β � 2, rho � 0.7, CN � 100, T ij (0) � 1. e parameters α � 1, β � 5, CN � 2, T ij (0) � 1 of the SP-ACA refer to [24]. e parameters of the CP-ACA are set as α � 1, β � 2, rho � 0.7, CN � 100, CN 1 � 80, T ij (0) � 1, c � 0.5, μ � 1.…”

Section: Grid Test Modelmentioning

confidence: 99%

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Path Planning of Slab Library Crane Based on Improved Ant Colony Algorithm

Zhao

Weigang²,

Xiao³

et al. 2019

Mathematical Problems in Engineering

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Due to the equipment characteristics (for example, the crane of each span cannot transfer products directly to other spans and path has less turning points and no slash lines) in a slab library, slab transportation is mainly realized by manually operating the crane. Firstly, the grid method is used to model the slab library. Secondly, an improved ant colony algorithm is proposed. e algorithm is used to solve the path planning of the slab library crane, which is improved by integrating the turning points, filtering the candidate solutions, dynamically evaporating pheromone, setting the dynamic region, etc. Finally, the algorithm is applied to plan the crane path of the slab library. e results show that the obstacle-free optimal path with fewer turning points, no slash lines, and short paths is found automatically.

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“…In this method, a combination of the triangular decomposition approach, constrained multi-objective PSO (particle swarm optimization) and Dijkstra's algorithm is presented in order to obtain an optimal path planning trajectory. In addition, due to good feedback information and better distributed computing, the authors proposed a path planning method for mobile robots via an improved ant colony algorithm in grid maps in Reference [12].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

Huang

Jiang

et al. 2019

Applied Sciences

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The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

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Mobile robot path planning using an improved ant colony optimization

Cited by 107 publications

References 21 publications

A Performance Review of Collision-Free Path Planning Algorithms

A Performance Review of Collision-Free Path Planning Algorithms

Path Planning of Slab Library Crane Based on Improved Ant Colony Algorithm

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

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