IEEE Transactions on Cognitive and Developmental Systems最新文献_第9页

A Two-Stage Foveal Vision Tracker Based on Transformer Model 基于变压器模型的两级眼窝视觉跟踪器

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-03-18 DOI: 10.1109/TCDS.2024.3377642

Guang Han;Jianshu Ma;Ziyang Li;Haitao Zhao

{"title":"A Two-Stage Foveal Vision Tracker Based on Transformer Model","authors":"Guang Han;Jianshu Ma;Ziyang Li;Haitao Zhao","doi":"10.1109/TCDS.2024.3377642","DOIUrl":"10.1109/TCDS.2024.3377642","url":null,"abstract":"With the development of transformer visual models, attention-based trackers have shown highly competitive performance in the field of object tracking. However, in some tracking scenarios, especially those with multiple similar objects, the performance of existing trackers is often not satisfactory. In order to improve the performance of trackers in such scenarios, inspired by the fovea vision structure and its visual characteristics, this article proposes a novel foveal vision tracker (FVT). FVT combines the process of human eye fixation and object tracking, pruning based on the distance to the object rather than attention scores. This pruning method allows the receptive field of the feature extraction network to focus on the object, excluding background interference. FVT divides the feature extraction network into two stages: local and global, and introduces the local recursive module (LRM) and the view elimination module (VEM). LRM is used to enhance foreground features in the local stage, while VEM generates circular fovea-like visual field masks in the global stage and prunes tokens outside the mask, guiding the model to focus attention on high-information regions of the object. Experimental results on multiple object tracking datasets demonstrate that the proposed FVT achieves stronger object discrimination capability in the feature extraction stage, improves tracking accuracy and robustness in complex scenes, and achieves a significant accuracy improvement with an area overlap (AO) of 72.6% on the generic object tracking (GOT)-10k dataset.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Converting Artificial Neural Networks to Ultralow-Latency Spiking Neural Networks for Action Recognition 将人工神经网络转换为超低延迟尖峰神经网络以进行动作识别

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-03-14 DOI: 10.1109/TCDS.2024.3375620

Hong You;Xian Zhong;Wenxuan Liu;Qi Wei;Wenxin Huang;Zhaofei Yu;Tiejun Huang

{"title":"Converting Artificial Neural Networks to Ultralow-Latency Spiking Neural Networks for Action Recognition","authors":"Hong You;Xian Zhong;Wenxuan Liu;Qi Wei;Wenxin Huang;Zhaofei Yu;Tiejun Huang","doi":"10.1109/TCDS.2024.3375620","DOIUrl":"10.1109/TCDS.2024.3375620","url":null,"abstract":"Spiking neural networks (SNNs) have garnered significant attention for their potential in ultralow-power event-driven neuromorphic hardware implementations. One effective strategy for obtaining SNNs involves the conversion of artificial neural networks (ANNs) to SNNs. However, existing research on ANN–SNN conversion has predominantly focused on image classification task, leaving the exploration of action recognition task limited. In this article, we investigate the performance degradation of SNNs on action recognition task. Through in-depth analysis, we propose a framework called scalable dual threshold mapping (SDM) that effectively overcomes three types of conversion errors. By effectively mitigating these conversion errors, we are able to reduce the time required for the spike firing rate of SNNs to align with the activation values of ANNs. Consequently, our method enables the generation of accurate and ultralow-latency SNNs. We conduct extensive evaluations on multiple action recognition datasets, including University of Central Florida (UCF)-101 and Human Motion DataBase (HMDB)-51. Through rigorous experiments and analysis, we demonstrate the effectiveness of our approach. Notably, SDM achieves a remarkable Top-1 accuracy of 92.94% on UCF-101 while requiring ultralow latency (four time steps), highlighting its high performance with reduced computational requirements.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140153797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EEG-Based Auditory Attention Detection With Spiking Graph Convolutional Network 利用尖峰图卷积网络进行基于脑电图的听觉注意力检测

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-03-12 DOI: 10.1109/TCDS.2024.3376433

Siqi Cai;Ran Zhang;Malu Zhang;Jibin Wu;Haizhou Li

{"title":"EEG-Based Auditory Attention Detection With Spiking Graph Convolutional Network","authors":"Siqi Cai;Ran Zhang;Malu Zhang;Jibin Wu;Haizhou Li","doi":"10.1109/TCDS.2024.3376433","DOIUrl":"10.1109/TCDS.2024.3376433","url":null,"abstract":"Decoding auditory attention from brain activities, such as electroencephalography (EEG), sheds light on solving the machine cocktail party problem. However, effective representation of EEG signals remains a challenge. One of the reasons is that the current feature extraction techniques have not fully exploited the spatial information along the EEG signals. EEG signals reflect the collective dynamics of brain activities across different regions. The intricate interactions among these channels, rather than individual EEG channels alone, reflect the distinctive features of brain activities. In this study, we propose a spiking graph convolutional network (SGCN), which captures the spatial features of multichannel EEG in a biologically plausible manner. Comprehensive experiments were conducted on two publicly available datasets. Results demonstrate that the proposed SGCN achieves competitive auditory attention detection (AAD) performance in low-latency and low-density EEG settings. As it features low power consumption, the SGCN has the potential for practical implementation in intelligent hearing aids and other brain–computer interfaces (BCIs).","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Perception-Based Visual Simultaneous Localization and Tracking in Dynamic Environments 动态环境中基于感知的稳健视觉同步定位与跟踪

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-28 DOI: 10.1109/TCDS.2024.3371073

Song Peng;Teng Ran;Liang Yuan;Jianbo Zhang;Wendong Xiao

{"title":"Robust Perception-Based Visual Simultaneous Localization and Tracking in Dynamic Environments","authors":"Song Peng;Teng Ran;Liang Yuan;Jianbo Zhang;Wendong Xiao","doi":"10.1109/TCDS.2024.3371073","DOIUrl":"10.1109/TCDS.2024.3371073","url":null,"abstract":"Visual simultaneous localization and mapping (SLAM) in dynamic scenes is a prerequisite for robot-related applications. Most of the existing SLAM algorithms mainly focus on dynamic object rejection, which makes part of the valuable information lost and prone to failure in complex environments. This article proposes a semantic visual SLAM system that incorporates rigid object tracking. A robust scene perception frame is designed, which gives autonomous robots the ability to perceive scenes similar to human cognition. Specifically, we propose a two-stage mask revision method to generate fine mask of the object. Based on the revised mask, we propose a semantic and geometric constraint (SAG) strategy, which provides a fast and robust way to perceive dynamic rigid objects. Then, the motion tracking of rigid objects is integrated into the SLAM pipeline, and a novel bundle adjustment is constructed to optimize camera localization and object six-degree of freedom (DoF) poses. Finally, the evaluation of the proposed algorithm is performed on publicly available KITTI dataset, Oxford Multimotion dataset, and real-world scenarios. The proposed algorithm achieves the comprehensive performance of \u0000<inline-formula><tex-math>$text{RPE}_{text{t}}$</tex-math></inline-formula>\u0000 less than 0.07 m per frame and \u0000<inline-formula><tex-math>$text{RPE}_{text{R}}$</tex-math></inline-formula>\u0000 about 0.03\u0000<inline-formula><tex-math>${}^{circ}$</tex-math></inline-formula>\u0000 per frame in the KITTI dataset. The experimental results reveal that the proposed algorithm enables accurate localization and robust tracking than state-of-the-art SLAM algorithms in challenging dynamic scenarios.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain Connectivity Analysis for EEG-Based Face Perception Task 基于脑电图的人脸感知任务的大脑连接性分析

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-27 DOI: 10.1109/TCDS.2024.3370635

Debashis Das Chakladar;Nikhil R. Pal

{"title":"Brain Connectivity Analysis for EEG-Based Face Perception Task","authors":"Debashis Das Chakladar;Nikhil R. Pal","doi":"10.1109/TCDS.2024.3370635","DOIUrl":"10.1109/TCDS.2024.3370635","url":null,"abstract":"Face perception is considered a highly developed visual recognition skill in human beings. Most face perception studies used functional magnetic resonance imaging to identify different brain cortices related to face perception. However, studying brain connectivity networks for face perception using electroencephalography (EEG) has not yet been done. In the proposed framework, initially, a correlation-tree traversal-based channel selection algorithm is developed to identify the “optimum” EEG channels by removing the highly correlated EEG channels from the input channel set. Next, the effective brain connectivity network among those “optimum” EEG channels is developed using multivariate transfer entropy (TE) while participants watched different face stimuli (i.e., famous, unfamiliar, and scrambled). We transform EEG channels into corresponding brain regions for generalization purposes and identify the active brain regions for each face stimulus. To find the stimuluswise brain dynamics, the information transfer among the identified brain regions is estimated using several graphical measures [global efficiency (GE) and transitivity]. Our model archives the mean GE of 0.800, 0.695, and 0.581 for famous, unfamiliar, and scrambled faces, respectively. Identifying face perception-specific brain regions will enhance understanding of the EEG-based face-processing system. Understanding the brain networks of famous, unfamiliar, and scrambled faces can be useful in criminal investigation applications.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D-FaST: Cognitive Signal Decoding With Disentangled Frequency–Spatial–Temporal Attention D-FaST：频率-空间-时间注意力分离的认知信号解码

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-26 DOI: 10.1109/TCDS.2024.3370261

WeiGuo Chen;Changjian Wang;Kele Xu;Yuan Yuan;Yanru Bai;Dongsong Zhang

{"title":"D-FaST: Cognitive Signal Decoding With Disentangled Frequency–Spatial–Temporal Attention","authors":"WeiGuo Chen;Changjian Wang;Kele Xu;Yuan Yuan;Yanru Bai;Dongsong Zhang","doi":"10.1109/TCDS.2024.3370261","DOIUrl":"10.1109/TCDS.2024.3370261","url":null,"abstract":"Cognitive language processing (CLP), situated at the intersection of natural language processing (NLP) and cognitive science, plays a progressively pivotal role in the domains of artificial intelligence, cognitive intelligence, and brain science. Among the essential areas of investigation in CLP, cognitive signal decoding (CSD) has made remarkable achievements, yet there still exist challenges related to insufficient global dynamic representation capability and deficiencies in multidomain feature integration. In this article, we introduce a novel paradigm for CLP referred to as disentangled frequency–spatial–temporal attention (D-FaST). Specifically, we present a novel cognitive signal decoder that operates on disentangled frequency–space–time domain attention. This decoder encompasses three key components: frequency domain feature extraction employing multiview attention (MVA), spatial domain feature extraction utilizing dynamic brain connection graph attention, and temporal feature extraction relying on local time sliding window attention. These components are integrated within a novel disentangled framework. Additionally, to encourage advancements in this field, we have created a new CLP dataset, MNRED. Subsequently, we conducted an extensive series of experiments, evaluating D-FaST's performance on MNRED, as well as on publicly available datasets including ZuCo, BCIC IV-2A, and BCIC IV-2B. Our experimental results demonstrate that D-FaST outperforms existing methods significantly on both our datasets and traditional CSD datasets including establishing a state-of-the-art accuracy score 78.72% on MNRED, pushing the accuracy score on ZuCo to 78.35%, accuracy score on BCIC IV-2A to 74.85%, and accuracy score on BCIC IV-2B to 76.81%.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139979066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification DTCM：用于多变量时间序列分类的深度变压器胶囊互馏法

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-26 DOI: 10.1109/TCDS.2024.3370219

Zhiwen Xiao;Xin Xu;Huanlai Xing;Bowen Zhao;Xinhan Wang;Fuhong Song;Rong Qu;Li Feng

{"title":"DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification","authors":"Zhiwen Xiao;Xin Xu;Huanlai Xing;Bowen Zhao;Xinhan Wang;Fuhong Song;Rong Qu;Li Feng","doi":"10.1109/TCDS.2024.3370219","DOIUrl":"10.1109/TCDS.2024.3370219","url":null,"abstract":"This article proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.e., Head_A and Head_B), each containing two squash convolutional neural network (CNN) blocks and one dynamic routing block to extract the local features from the data and mine the connections among them. The GRN consists of two capsule-based transformer blocks and one dynamic routing block to capture the global patterns of each variable and correlate the useful information of multiple variables. Unfortunately, it is difficult to directly deploy PCapN on mobile devices due to its strict requirement for computing resources. So, this article designs a lightweight capsule network (LCapN) to mimic the cumbersome PCapN. To promote knowledge transfer from PCapN to LCapN, this article proposes a deep transformer capsule mutual (DTCM) distillation method. It is targeted and offline, using one- and two-way operations to supervise the knowledge distillation (KD) process for the dual-network-based student and teacher models. Experimental results show that the proposed PCapN and DTCM achieve excellent performance on University of East Anglia 2018 (UEA2018) datasets regarding top-1 accuracy.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139979417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agree to Disagree: Exploring Partial Semantic Consistency Against Visual Deviation for Compositional Zero-Shot Learning 同意到不同意：探索部分语义一致性与视觉偏差对组合式零镜头学习的影响

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-20 DOI: 10.1109/TCDS.2024.3367957

Xiangyu Li;Xu Yang;Xi Wang;Cheng Deng

{"title":"Agree to Disagree: Exploring Partial Semantic Consistency Against Visual Deviation for Compositional Zero-Shot Learning","authors":"Xiangyu Li;Xu Yang;Xi Wang;Cheng Deng","doi":"10.1109/TCDS.2024.3367957","DOIUrl":"10.1109/TCDS.2024.3367957","url":null,"abstract":"Compositional zero-shot learning (CZSL) aims to recognize novel concepts from known subconcepts. However, it is still challenging since the intricate interaction between subconcepts is entangled with their corresponding visual features, which affects the recognition accuracy of concepts. Besides, the domain gap between training and testing data leads to the model poor generalization. In this article, we tackle these problems by exploring partial semantic consistency (PSC) to eliminate visual deviation to guarantee the discrimination and generalization of representations. Considering the complicated interaction between subconcepts and their visual features, we decompose seen images into visual elements according to their labels and obtain the instance-level subdeviations from compositions, which is utilized to excavate the category-level primitives of subconcepts. Furthermore, we present a multiscale concept composition (MSCC) approach to produce virtual samples from two aspects, which augments the sufficiency and diversity of samples so that the proposed model can generalize to novel compositions. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three benchmark datasets.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Compressed Video Anomaly Detection of Human Behavior Based on Abnormal Region Determination 基于异常区域判定的人类行为压缩视频异常检测

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-20 DOI: 10.1109/TCDS.2024.3367493

Lijun He;Miao Zhang;Hao Liu;Liejun Wang;Fan Li

{"title":"Compressed Video Anomaly Detection of Human Behavior Based on Abnormal Region Determination","authors":"Lijun He;Miao Zhang;Hao Liu;Liejun Wang;Fan Li","doi":"10.1109/TCDS.2024.3367493","DOIUrl":"10.1109/TCDS.2024.3367493","url":null,"abstract":"Video anomaly detection has a wide range of applications in video monitoring-related scenarios. The existing image-domain-based anomaly detection algorithms usually require completely decoding the received videos, complex information extraction, and network structure, which makes them difficult to be implemented directly. In this article, we focus on anomaly detection directly for compressed videos. The compressed videos need not be fully decoded and auxiliary information can be obtained directly, which have low computational complexity. We propose a compressed video anomaly detection algorithm based on accurate abnormal region determination (ARD-VAD), which is suitable to be deployed on edge servers. First, to ensure the overall low complexity and save storage space, we sparsely sample the prior knowledge of I-frame representing the appearance information and motion vector (MV) representing the motion information from compressed videos. Based on the sampled information, a two-branch network structure, which consists of MV reconstruction branch and future I-frame prediction branch, is designed. Specifically, the two branches are connected by an attention network based on the MV residuals to guide the prediction network to focus on the abnormal regions. Furthermore, to emphasize the abnormal regions, we develop an adaptive sensing of abnormal regions determination module based on motion intensity represented by the second derivative of MV. This module can enhance the difference of the real anomaly region between the generated frame and the current frame. The experiments show that our algorithm can achieve a good balance between performance and complexity.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning With Multicritic TD3 for Decentralized Multirobot Path Planning 利用多批判 TD3 进行深度强化学习，实现分散式多机器人路径规划

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-02-20 DOI: 10.1109/TCDS.2024.3368055

Heqing Yin;Chang Wang;Chao Yan;Xiaojia Xiang;Boliang Cai;Changyun Wei

{"title":"Deep Reinforcement Learning With Multicritic TD3 for Decentralized Multirobot Path Planning","authors":"Heqing Yin;Chang Wang;Chao Yan;Xiaojia Xiang;Boliang Cai;Changyun Wei","doi":"10.1109/TCDS.2024.3368055","DOIUrl":"10.1109/TCDS.2024.3368055","url":null,"abstract":"Centralized multirobot path planning is a prevalent approach involving a global planner computing feasible paths for each robot using shared information. Nonetheless, this approach encounters limitations due to communication constraints and computational complexity. To address these challenges, we introduce a novel decentralized multirobot path planning approach that eliminates the need for sharing the states and intentions of robots. Our approach harnesses deep reinforcement learning and features an asynchronous multicritic twin delayed deep deterministic policy gradient (AMC-TD3) algorithm, which enhances the original gate recurrent unit (GRU)-attention-based TD3 algorithm by incorporating a multicritic network and employing an asynchronous training mechanism. By training each critic with a unique reward function, our learned policy enables each robot to navigate toward its long-term objective without colliding with other robots in complex environments. Furthermore, our reward function, grounded in social norms, allows the robots to naturally avoid each other in congested situations. Specifically, we train three critics to encourage each robot to achieve its long-term navigation goal, maintain its moving direction, and prevent collisions with other robots. Our model can learn an end-to-end navigation policy without relying on an accurate map or any localization information, rendering it highly adaptable to various environments. Simulation results reveal that our proposed approach surpasses baselines in several environments with different levels of complexity and robot populations.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139954488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0