Robot Navigation Strategy in Complex Environment Based on Episode Cognition

Yuan, Jinsheng; Wei, Guo; Hou, Zhipeng; Zha, Fusheng; Li, Mantian; Sun, Lining; Wang, Pengfei

doi:10.1007/s42235-022-00265-2

Cited by 18 publications

(1 citation statement)

References 28 publications

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“…Firstly, rats explore the environment and search for the navigation goal. During this process, rats utilize their sensory systems and employ heuristic strategies to gather crucial information about the environment [ 4 ]. For example, they tend to follow walls, seek out bright lights, or avoid hazardous areas.…”

Section: Introductionmentioning

confidence: 99%

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism

Liao,

Yu,

Yan

2023

Biomimetics

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Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat’s brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain’s cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.

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Section: Introductionmentioning

confidence: 99%

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism

Liao,

Yu,

Yan

2023

Biomimetics

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CARNet: An Efficient Cascaded and Attention‐Based RNN Architecture for Modulation Classification in Cognitive Radio Network Using Improved Kookaburra Optimization Strategy

Krishna

2024

Int J Communication

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In cognitive radio (CR) networks, the automatic modulation classification (AMC) is considered as the significant role in smart wireless communications. Due to the high growth of deep learning in the modern days, neural network‐aided automated modulation categorization tasks have become highly demanded. Nevertheless, an enormous amount of attributes and the neural network's complexity make them complex to adopt in various scenarios. Moreover, the receiver systems are limited by the latency and less storage resources. Additionally, the detection system of the signal modulation is mostly hampered by overfitting issues and insufficient information. Therefore, a robust AMC model based on adaptive deep learning is implemented to determine the type of modulation used at the transmitter by observing the received signal. Initially, the necessary raw data for the suggested model is garnered from benchmark dataset. Also, the optimal features from the raw data are selected with the help of the fitness‐revised position updating in Kookaburra optimization (FPUKO) for minimizing the computation time and enhancing the accuracy rates in the classification process. Moreover, this optimal feature selection makes the modulation selection process quick and efficient. Finally, the optimal features are fed to a cascaded and attention‐based recurrent neural network (CA‐RNN) in the modulation classification, which is designed to classify the type of modulation used on the transmitter side. Various experiments are conducted for the designed framework by comparing it with the existing models to view its efficiency.

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Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

Yuan,

Yang,

Wang

et al. 2024

Quality & Reliability Eng

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Accurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

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Robot Navigation Strategy in Complex Environment Based on Episode Cognition

Cited by 18 publications

References 28 publications

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism

CARNet: An Efficient Cascaded and Attention‐Based RNN Architecture for Modulation Classification in Cognitive Radio Network Using Improved Kookaburra Optimization Strategy

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

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