Robot Path Planning Based on Ant Colony Optimization Algorithm for Environments with Obstacles

Neydorf, Rudolf; Yarakhmedov, Orhan; Polyakh, Victor; Chernogorov, Ivan; Vucinic, Dean

doi:10.1007/978-3-319-59590-0_14

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…It should be noted that the algorithm requires the involvement of some additional mechanisms to improve performance [14][15][16].…”

Section: Resultsmentioning

confidence: 99%

“…To divide it into fragments, the rival ant colonies method (RACM) is used, which simulates the behavior of ant colonies in the division of their habitats [10][11][12][13][14][15][16], and adapted for the task. It is based on the mechanisms of interaction of ants with each other, with the environment, as well as colonies as societies among themselves.…”

Section: Algorithmic Modelmentioning

confidence: 99%

“…All investigated space is traditionally divided by a grid, but not conditional, but rigidly tied to segments of step-by-step variation of factors when obtaining ED [14][15][16]. The number of colonies is varied by the "Cut-Glue" approximation algorithm, because it affects the number of fragments obtained after the partition.…”

Section: Algorithmic Modelmentioning

confidence: 99%

“…Moving virtual ants is carried out on a probabilistic basis [14][15][16]. At each iteration, each agent considers 8 possible directions to move as shown in Figure 1.…”

Section: Algorithmic Modelmentioning

confidence: 99%

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Structural-parametric optimization of the experimental data decomposition into approximated fragments

Neydorf

Iarakhmedov

2017

MATEC Web Conf.

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Abstract. In the process of modeling non-linear objects are often used piecewise approximation, spline methods or other methods based on their experimental data fragmentation. To effectively use fragmentation, it is necessary to develop and examine all the described stages of the approach. In this paper, a new approach to the selection of the structure of the decomposition of matrix data has been developed and proposed. It is based on the method of the ant colony, specially adapted for solving similar problems. Along with the characteristic properties of ant algorithms, special elements of interaction between agents of different colonies were introduced into this modification, for the efficient operation of the algorithm. The approach is considered as a tool for implementing the initial stage of any methods using data fragmentation. In the process of work, a software tool was designed and implemented in C #. The main data structures used in the software are described. Adjustment of the tuning parameters and the graphic output interface of the work process are implemented in the software tool. The graphical output was implemented by built-in tools. Test cases in detail describe the stages of data fragmentation under different initial parameters.

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“…It should be noted that the algorithm requires the involvement of some additional mechanisms to improve performance [14][15][16].…”

Section: Resultsmentioning

confidence: 99%

Section: Algorithmic Modelmentioning

confidence: 99%

Section: Algorithmic Modelmentioning

confidence: 99%

“…Moving virtual ants is carried out on a probabilistic basis [14][15][16]. At each iteration, each agent considers 8 possible directions to move as shown in Figure 1.…”

Section: Algorithmic Modelmentioning

confidence: 99%

See 2 more Smart Citations

Structural-parametric optimization of the experimental data decomposition into approximated fragments

Neydorf

Iarakhmedov

2017

MATEC Web Conf.

Self Cite

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“…In Ref. [65] presented a modified ant colony optimization (ACO) algorithm-based path-planning software tool for 2D environments containing obstacles.…”

Section: Literature Reviewmentioning

confidence: 99%

Path Planning Algorithms for Mobile Robots: A Survey

Haruna,

Bashir Mu’azu,

Umar

et al. 2023

Motion Planning for Dynamic Agents

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Mobile robots have applications in military (for reconnaissance, search and rescue operations, bomb detection, surveillance), transportation (for cargo and packet delivery), data acquisition, etc. For the mobile robots to be able to execute these tasks with minimum or no human intervention, they need to be autonomous and intelligent. Path planning (PP) is one of the most critical areas of concern in the field of autonomous mobile robots. It is about obtaining a collision-free motion optimal path based on either time, distance, energy or cost in a static or dynamic environment containing obstacles. However, power limitation hinders the mobile robots to accomplish their task of reaching the target location as there are several paths they can follow. Each of these paths has its own path length, cost (i.e., time to reach destination), and energy constraint, thus, the need to plan for an optimal path according to a certain performance criterion. Significant research has been conducted in recent years to address the PP problem. Hence, this chapter is aimed at presenting the different approaches for PP of mobile robots with respect to different optimality criteria (time, distance, energy and cost), challenges and making recommendations on possible areas of future research.

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