A Memetic Algorithm With Variable Length Chromosome for Robot Path Planning Under Dynamic Environments

Ni, Jianjun; Wang, Kang; Cao, Qingqing; Khan, Zubair; Fan, Xinnan

doi:10.2316/journal.206.2017.4.206-4998

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…The feasible region of many problems is uncertain in real life. Many scholars [23][24][25][26][27][28][29] have improved the NSGA-III-VLC according to the characteristics of the problem. In the formulation of the equipment usage plan considering rotation, because the quantity of rotation will affect the rotation cost and risk, the length of the chromosome also affects the decision-making.…”

Section: Principle Of I-nsga-iii-vlc Methodsmentioning

confidence: 99%

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

Miao¹,

Zhang²,

Qin³

et al. 2022

Applied Sciences

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Optimizing the aircraft equipment usage scheme of different units according to their task intensity has great significance in improving aircraft reliability and health management. This paper studied the modeling and solving methods of the rotation and echelon usage problems of aircraft equipment measured by dual-life indexes, one of which cannot be controlled. In order to maximize the waste rate of the rotation quantity, echelon uniformity index, life matching index and life utilization index, a decision-making model of the equipment rotation and echelon usage problem under uncertainty was constructed, and an improved NSGA-III with a variable length chromosome was proposed. An improved segmented coding method and operators were proposed, and the repeated individual control mechanism was used to improve the population diversity. When the scale of the problem was large, this method could search a wider range in a short time and obtain more feasible solutions, which verified the feasibility of this method.

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Section: Principle Of I-nsga-iii-vlc Methodsmentioning

confidence: 99%

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

Miao¹,

Zhang²,

Qin³

et al. 2022

Applied Sciences

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“…At the theoretical level, some methods used in the robotic field can be applied in self-driving cars for the similarity between them, including path planning, environmental sensing, autonomous navigation and control, etc. [23,24]. Various artificial intelligence algorithms have been used in self-driving cars, such as fuzzy logic, neural network, and so on.…”

Section: Driving Pathmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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“…The completeness is one of the advantages of these traditional methods in addition to that increasing the problem size increases the execution time. On the other hand, soft computing methods have many examples like particle swarm optimization algorithm [31,40,41] which is ten times faster than the Fuzzy logic [42][43][44], Genetic algorithm [45][46][47] which is used to avoid the local minima and the high execution time problems, Memetic algorithm [48], neural networks [49][50][51] and Ant colony [52,53]. Although the solutions of the soft computing methods suffer from the unpredictability, the uncertainty and near-optimal path instead of exact solutions, these methods can be used in environments that higher artificial intelligence is needed and not enough information and noisy information is provided.…”

mentioning

confidence: 99%

A Supervisory-Based Collaborative Obstacle-Guided Path Refinement Algorithm for Path Planning in Wide Terrains

2020

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Robotic exploration of wide terrains, such as agricultural fields, could be challenging while considering the limited robot's capabilities in terms of sensing and power. Thus, in this paper, we proposed OGPR*, an Obstacle Guided Path Refinement algorithm for quickly planning collision-free paths utilizing the obstacles existing in the environment. To tackle the issue of exploring wide terrains, a supervisorybased collaboration between quadcopter and mobile robots is proposed. The quadcopter is responsible for streaming subsequently live two-dimensional images for the environment under discussion while planning safe paths for the ground the mobile robot is planning safe paths to manoeuvre. Numerical simulations proved the significant performance of the proposed OGBR* algorithm when compared to the state of the art algorithms exist in the literature. INDEX TERMS collaborative, mobile robot, OGPR, path planning, tracking I. INTRODUCTION

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A Memetic Algorithm With Variable Length Chromosome for Robot Path Planning Under Dynamic Environments

Cited by 6 publications

References 22 publications

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

Modeling and I-NSGA-III-VLC Solution of Aircraft Equipment Rotation and Echelon Usage under Uncertainty

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

A Supervisory-Based Collaborative Obstacle-Guided Path Refinement Algorithm for Path Planning in Wide Terrains

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