Artificial Immune Network-Based Multi-Robot Formation Path Planning With Obstacle Avoidance

Deng, Lixia; Ma, Xin; Li, Yibin; Xu, Zhijie; Wang, Yafang

doi:10.2316/journal.206.2016.3.206-4746

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Harmonic Search algorithm has been used to control under water robot in obstacles prone environments [91]. Artificial Immune system is a biological inspired algorithm and can be used for handling various optimisation problems [92][93][94]. Robot kinematic analysis is discussed by several researchers, required for studying robot movements [95][96][97].…”

Section: Discussionmentioning

confidence: 99%

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

2020

AMLAI

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The current research focuses on development and analysis of novel Average Neuro-Fuzzy Controller for path planning and navigation of mobile robot in highly cluttered environment. During the investigation various researches related to robot, control and navigation have been analysed. For mapping the environments several distance sensors mounted on the robot are used. The sensors readings about the environments have been segmented into various sectors (front, left, right and back sectors). Using the sensors reading robots negotiate with the obstacles present in the environments during navigation from start to goal point. Experimental and simulation results obtained during the current research from various exercises are in agreement and are within 3%. Comparisons between results show the effectiveness of the proposed technique for robot navigation in complex environments. This technique can be used to address various engineering optimisation problems.

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Section: Discussionmentioning

confidence: 99%

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

2020

AMLAI

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“…Another challenging problem that arises in CCPP is obstacle avoidance ( An et al, 2018 ; Wang et al, 2021 ). Based on the excellent optimization and search capabilities of nature-inspired algorithms, researchers have recently explored many nature-inspired computational approaches to solve vehicle collision-free navigation problems ( Deng et al, 2016 ; Ewerton et al, 2019 ; Lei et al, 2019 , 2021 ; Segato et al, 2019 ). For instance, a hybrid fireworks algorithm with LIDAR-based local navigation was developed by Lei et al (2020a) , capable of generating short collision-free trajectories in unstructured environments.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

et al. 2022

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With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCPP) research, however, autonomous vehicles need to consider mapping, obstacle avoidance, and route planning simultaneously during operating in the workspace, which results in an extremely complicated and computationally expensive navigation system. In this study, a new framework is developed in light of a hierarchical manner with the obtained environmental information and gradually solving navigation problems layer by layer, consisting of environmental mapping, path generation, CCPP, and dynamic obstacle avoidance. The first layer based on satellite images utilizes a deep learning method to generate the CCPP trajectory through the position of the autonomous vehicle. In the second layer, an obstacle fusion paradigm in the map is developed based on the unmanned aerial vehicle (UAV) onboard sensors. A nature-inspired algorithm is adopted for obstacle avoidance and CCPP re-joint. Equipped with the onboard LIDAR equipment, autonomous vehicles, in the third layer, dynamically avoid moving obstacles. Simulated experiments validate the effectiveness and robustness of the proposed framework.

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“…In recent year, wireless network technologies have been widely applied in the fields of industrial automation [1], smart power grid [2], robots control [3], unmanned aerial vehicle (UAV) infrastructure [4], etc. [5]. Although they are characterized by impressive cost-effective energy consumption, high flexibility, and strong expansibility, the increased demand for wireless communication brought out new problems, where the spectrum resource scarcity is highlighted [6].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Radio Resource Allocation Based on the Improved Quantum Genetic Algorithm

Han¹,

Jiang²,

Luo³

et al. 2019

International Journal of Robotics and Automation

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In the spectrum allocation of cognitive radio (CR) network, the problems of local optimum and premature convergence remain challenging. To further improve the efficiency of the spectrum allocation, this paper proposes a novel method based on an improved quantum genetic algorithm. This is an algorithm that is designed to dynamically adjust the quantum rotation angle to speed up the convergence rate. In particular, the variation threshold was introduced to the mutation operation on chromosomes, establishing new interference constraint rules in the process of spectrum allocation. The simulation work was implemented for validation, and the results revealed that the proposed methodology achieved better average benefits of the CR network for a reasonable allocation of the spectrum.

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Artificial Immune Network-Based Multi-Robot Formation Path Planning With Obstacle Avoidance

Cited by 13 publications

References 17 publications

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

Cognitive Radio Resource Allocation Based on the Improved Quantum Genetic Algorithm

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