A Mobile Robot Path Planning Algorithm Based on Multi-objective Optimization

Guo, Hongwei; Shang, Yuxin; Qu, Wenyao

doi:10.1109/aemcse50948.2020.00015

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Over the past decade, various adaptations of the Standard Genetic Algorithm (SGA) framework have emerged to tackle the challenges posed by multi-objective optimization. These adaptations include the Multi-Objective Genetic Algorithm (MOGA) [139], Non-Dominated Sorting Genetic Algorithm II (NSGA-II) [39], and Non-Dominated Sorting Genetic Algorithm III (NSGA-III) [40]. These variants have been designed to address the complexities inherent in optimizing multiple conflicting objectives.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…To fully harness the power of evolutionary algorithms in population-based searches, methodologies centered on Pareto dominance have gained widespread traction for fitness evaluation. Guo et al [139] seamlessly integrated the GA framework into a multi-objective evolutionary algorithm, resulting in a collection of Pareto optimal solutions that encompass diverse paths. This array of options aids decision-making in practical scenarios.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Path Planning Technique for Mobile Robots: A Review

Yang,

Li,

Qian

et al. 2023

Machines

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Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Path Planning Technique for Mobile Robots: A Review

Yang,

Li,

Qian

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…In [25], the researchers proposed a multi-objective path planning algorithm for mobile robot, this algorithm has three objectives: length, smoothness and safety. The applicable environment of this algorithm is relatively simple, the environment was only divided into passable and impassable, and the safety optimization only considers the distance between the robot and the obstacles.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

Liu

Wang

et al. 2022

Micromachines

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The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This paper proposes a path planning algorithm for MLR based on a multi-objective genetic algorithm with elitist strategy (MLRMOEGA), which has four optimization objectives: power consumption, time consumption, path falling risk, and path smoothness. We propose two operators: a map analysis operator and a population diversity expansion operator, to improve the global search ability of the algorithm and solve the problem so that it is easy to fall into the local optimal solution. We conduct simulations on MATLAB, and the results show that the proposed algorithm can effectively optimize the objective function value compared with the traditional genetic algorithm under the equal weight of the four optimization objectives, and, under alternative weights, the proposed algorithm can effectively generate the corresponding path of the decision maker’s intention under the weight of preference. Compared with the traditional genetic algorithm, the global search ability is improved effectively.

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A Mobile Robot Path Planning Algorithm Based on Multi-objective Optimization

Cited by 2 publications

References 10 publications

Path Planning Technique for Mobile Robots: A Review

Path Planning Technique for Mobile Robots: A Review

Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

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