Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey

Abed, Baraa Mohammed; Jasim, Wesam M.

doi:10.3991/ijoe.v18i15.34397

Cited by 3 publications

(5 citation statements)

References 42 publications

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“…Accordingly, various stochastic methods have been designed in recent years to address and overcome these flaws as well as improve the performance of various systems and minimize computation costs [5] [6] [7]. Thus, optimization problems can be found in any scientific subject [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics: A Review of Algorithms

Sadeeq

Abdulazeez

2023

Int. J. Onl. Eng.

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In science and engineering, many optimization tasks are difficult to solve, and the core concern these days is to apply metaheuristic (MH) algorithms to solve them. Metaheuristics have gained significant attention in recent years, with nature serving as the fundamental inspiration where self-organization property led to collective intelligence emerging from the behavior of a swarm of birds or colony of insects or more and more natural behavior. These swarms or colonies, even with extremely low individual competence, have the ability to accomplish many complicated activities that can be considered necessary for their existence. Accordingly, many MH algorithms have been developed based on natural phenomena. In this article, an analysis review of more than one hundred metaheuristics have been made. Further, the main contributions of this article are to give some vital insights about metaheuristics, presenting and proposing the general mathematical framework of MH algorithms and dividing it into a number of tasks with possible progress for each task. While there are still many open issues in this field, it is worth noting that there have been significant advancements in recent years. As a result, new algorithms are continuously being proposed to address these challenges.

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Section: Introductionmentioning

confidence: 99%

Metaheuristics: A Review of Algorithms

Sadeeq

Abdulazeez

2023

Int. J. Onl. Eng.

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“…A significant body of scholarship focuses on computer-assisted learning, such as research on mobile technologies [7], Artificial Intelligence in the context of training [8], or even the artistic impulses of programs [9]. However, this discourse still gives a significant place to the human factor, such as the development of a mathematical understanding of cloud technologies [10], process optimization [11].…”

Section: Introductionmentioning

confidence: 99%

What Does Digital Learning Teach?

Shulga

2023

Int. J. Interact. Mob. Technol.

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Digital learning does not determine its strategic benchmarks independently since it always represents only a training system component. Consequently, its goals and objectives cannot be understood without a preliminary understanding of the purposes and objectives put forward by the training system a part of which it forms. The issue of identifying this purpose as a person being a cognitive activity subject or a person being a bearer of a certain number of skills requires clarification. The digital learning concept will always contain a positive ("correct") or negative ("incorrect") answer to this question in the "default" status, as prescribed by the training system. And it will serve as an auxiliary tool only in promoting (and approving) different concepts of human capacity as a "correct" one.

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“…Others use two-dimensiona (or three-dimensional) rectangular or square workspaces, with the obstacles being poly gon, as shown in Figure 3. Many researchers have presented a survey paper on mobile robot navigation [9][10][11]; however, these surveys are insufficient to provide an in-depth analysis of individual navigational techniques. This proposed survey paper on mobile robot navigation aims to discover the research gaps and scope of innovation in a particular area.…”

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confidence: 99%

“…Ref. [11] focuses on multi-objective optimization algorithms for mobile robot path planning. Many researchers have presented a survey paper on mobile robot navigation [9-11] however, these surveys are insufficient to provide an in-depth analysis of individual nav igational techniques.…”

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confidence: 99%

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Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb

Kadhim

2023

Robotics

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The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.

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Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey

Cited by 3 publications

References 42 publications

Metaheuristics: A Review of Algorithms

Metaheuristics: A Review of Algorithms

What Does Digital Learning Teach?

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

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