Intelligence & Robotics最新文献

{"title":"UAV path planning based on a dual-strategy ant colony optimization algorithm","authors":"Xiaoming Mai, Na Dong, Shuai Liu, Hao Chen","doi":"10.20517/ir.2023.37","DOIUrl":"https://doi.org/10.20517/ir.2023.37","url":null,"abstract":"With the rapid development of modern communication and automatic control technologies, unmanned aerial vehicles (UAVs) have increasingly gained importance in both military and civilian domains. Path planning, a critical aspect for achieving autonomous aerial navigation, has consistently been a focal point in UAV research. However, traditional ant colony algorithms need to be improved for the drawbacks of susceptibility to local optima and weak convergence capabilities. Consequently, a novel path planning methodology is proposed based on a dual-strategy ant colony algorithm. In detail, an improved state transition probability rule is introduced, redefining ant movement rules by integrating the state transition strategy of deterministic selection during the iterative process. Additionally, heuristic information on adjacent node distance and mountain height is added to further improve the search efficiency of the algorithm. Then, a new dynamically adjusted pheromone update strategy is proposed. The update strategy is continuously adjusted during the iteration process, which is beneficial to the algorithm’s global search in the early stage and accelerated convergence in the later stage, preventing the algorithm from falling into local optimality and improving its convergence. Based on the above improvements, a new variation of ant colony optimization (ACO) called dual-strategy ACO algorithm is formed. Experimental results prove that dual-strategy ACO has superior global search capabilities and convergence characteristics from four key aspects: path length, fitness values, iteration number, and running time.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"56 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Degradation trend prediction of rail stripping for heavy haul railway based on multi-strategy hybrid improved pelican algorithm","authors":"Changfan Zhang, Chang Jiang, Jianhua Liu, Weifeng Yang, Jia He","doi":"10.20517/ir.2023.36","DOIUrl":"https://doi.org/10.20517/ir.2023.36","url":null,"abstract":"As a key component of the heavy-haul railway system, the rail is prone to damages caused by harsh operating conditions. To secure a safe operation, it is of great essence to detect the damage status of the rail. However, current damage detection methods are mainly manual, so problems such as strong subjectivity, lag in providing results, and difficulty in quantifying the degree of damage are easily generated. Therefore, a new prediction method based on the improved pelican algorithm and channel attention mechanism is proposed to evaluate the stripping of heavy-haul railway rails. By processing the rail vibration acceleration, it predicts the stripping damage degree. Specifically, a comprehensive health index measuring the degree of rail stripping is first established by principal component analysis and correlation analysis to avoid the one-sidedness of a single evaluation index. Then, the convolutional bidirectional gated recursive network is trained and generalized, and the pelican algorithm, improved by multiple hybrid strategies, is used to optimize the hyperparameters in the network so as to find the optimal solution by constantly adjusting the search strategy. The squeeze-excitation channel attention module is then incorporated to re-calibrate the weights of valid features and to improve the accuracy of the model. Finally, the proposed method is tested on a specific rail stripping dataset and a public dataset of PHM2012 bearings, and the generalization and effectiveness performance of the proposed method is proved.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"59 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrate memory mechanism in multi-granularity deep framework for driver drowsiness detection","authors":"Handan Zhang, Tie Liu, Jie Lyu, Dapeng Chen, Zejian Yuan","doi":"10.20517/ir.2023.34","DOIUrl":"https://doi.org/10.20517/ir.2023.34","url":null,"abstract":"Driver drowsiness detection is a critical task for early warning of safe driving, while existing spatial feature-based methods face the challenges of large variations of head pose. This paper proposes a novel approach to integrate the memory mechanism in a multi-granularity deep framework to detect driver drowsiness, and the temporal dependencies over sequential frames are well integrated with the spatial deep learning framework on the frontal faces. The proposed approach includes two steps. First, the spatial Multi-granularity Convolutional Neural Network is designed to utilize a group of parallel Convolutional Neural Network extractors on well-aligned facial patches of different granularities and extract facial representations effectively for large variations of head pose. Furthermore, it can flexibly fuse detailed appearance clues of the main parts and local-to-global spatial constraints. Second, the memory mechanism is set up using a deep long short-term memory network of facial representations to explore long-term relationships with variable length over sequential frames, which is capable of distinguishing the states with temporal dependencies, such as blinking and closing eyes. The proposed approach achieves 90.05% accuracy and about 37 frames per second (FPS) speed on the evaluation set of the National Tsing Hua University Driver Drowsiness Detection dataset, which is applied to the intelligent vehicle for driver drowsiness detection. A dataset named Forward Instant Driver Drowsiness Detection is also built and will be publicly accessible to speed up the study of driver drowsiness detection.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-triggered consensus control method with communication faults for multi-UAV","authors":"Zilong Guo, Chen Wei, Yankai Shen, Wanmai Yuan","doi":"10.20517/ir.2023.32","DOIUrl":"https://doi.org/10.20517/ir.2023.32","url":null,"abstract":"This paper investigates the event-triggered consensus for a group of unmanned aerial vehicles (UAVs) with communication faults under the assumption that the position sensors of some individuals are damaged. The objective is to make the UAV group reach consensus in urgent tasks such as obstacle avoidance or evasion. Using the Lyapunov stability theory, sufficient conditions to achieve system consensus are given based on different velocity and position interaction topologies. Considering the limited capabilities of sensors and processors, an event-triggered consensus protocol is adopted to reduce the sampling frequency. Finally, simulation results illustrate the effectiveness of our approach.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"128 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A wearable assistive system for the visually impaired using object detection, distance measurement and tactile presentation","authors":"Yiwen Chen, Junjie Shen, Hideyuki Sawada","doi":"10.20517/ir.2023.24","DOIUrl":"https://doi.org/10.20517/ir.2023.24","url":null,"abstract":"With the current development of society, ensuring traffic and walking safety for the visually impaired is becoming increasingly important. We propose a wearable system based on a system previously developed by us that uses object recognition, a distance measurement function, and the corresponding vibration pattern presentation to support the mobility of the visually impaired. The system recognizes obstacles in front of a user in real time, measures their distances, processes the information, and then presents safety actions through vibration patterns from a tactile glove woven with shape memory alloy (SMA) actuators. The deep learning model is compressed to achieve real-time recognition using a microcomputer while maintaining recognition accuracy. Measurements of the distances to multiple objects are realized using a stereo camera, and vibration patterns are presented through a tactile glove in response to these distances. Experiments are conducted to verify the system performance to provide safe navigation depending on the positions and the distances of multiple obstacles in front of the user.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133254759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning methods for network-based transfer parameter selection","authors":"Yue (Sophie) Guo, Yu Wang, I-Hsuan Yang, K. Sycara","doi":"10.20517/ir.2023.23","DOIUrl":"https://doi.org/10.20517/ir.2023.23","url":null,"abstract":"A significant challenge in self-driving technology involves the domain-specific training of prediction models on intentions of other surrounding vehicles. Separately processing domain-specific models requires substantial human resources, time, and equipment for data collection and training. For instance, substantial difficulties arise when directly applying a prediction model developed with data from China to the United States market due to complex factors such as differing driving behaviors and traffic rules. The emergence of transfer learning seems to offer solutions, enabling the reuse of models and data to enhance prediction efficiency across international markets. However, many transfer learning methods require a comparison between source and target data domains to determine what can be transferred, a process that can often be legally restricted. A specialized area of transfer learning, known as network-based transfer, could potentially provide a solution. This approach involves pre-training and fine-tuning \"student\" models using selected parameters from a \"teacher\" model. However, as networks typically have a large number of parameters, it raises questions about the most efficient methods for parameter selection to optimize transfer learning. An automatic parameter selector through reinforcement learning has been developed in this paper, named \"Automatic Transfer Selector via Reinforcement Learning\". This technique enhances the efficiency of parameter selection for transfer prediction between international self-driving markets, in contrast to manual methods. With this innovative approach, technicians are relieved from the labor-intensive task of testing each parameter combination, or enduring lengthy training periods to evaluate the impact of prediction transfer. Experiments have been conducted using a temporal convolutional neural network fully trained with the data from the Chinese market and one month's US data, focusing on improving the training efficiency of specific driving scenarios in the US. Results show that the proposed approach significantly improves the prediction transfer process.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132388154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based scene understanding for autonomous robots: a survey","authors":"J. Ni, Yuanchun Chen, Guangyi Tang, Jiamei Shi, Weidong Cao, P. Shi","doi":"10.20517/ir.2023.22","DOIUrl":"https://doi.org/10.20517/ir.2023.22","url":null,"abstract":"Autonomous robots are a hot research subject within the fields of science and technology, which has a big impact on social-economic development. The ability of the autonomous robot to perceive and understand its working environment is the basis for solving more complicated issues. In recent years, an increasing number of artificial intelligence-based methods have been proposed in the field of scene understanding for autonomous robots, and deep learning is one of the current key areas in this field. Outstanding gains have been attained in the field of scene understanding for autonomous robots based on deep learning. Thus, this paper presents a review of recent research on the deep learning-based scene understanding for autonomous robots. This survey provides a detailed overview of the evolution of robotic scene understanding and summarizes the applications of deep learning methods in scene understanding for autonomous robots. In addition, the key issues in autonomous robot scene understanding are analyzed, such as pose estimation, saliency prediction, semantic segmentation, and object detection. Then, some representative deep learning-based solutions for these issues are summarized. Finally, future challenges in the field of the scene understanding for autonomous robots are discussed.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115096427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of intelligent methods of health assessment technology","authors":"Diyi Liu, Linyuan Peng, Zhiyao Zhao","doi":"10.20517/ir.2023.16","DOIUrl":"https://doi.org/10.20517/ir.2023.16","url":null,"abstract":"The core technology of prognostics and health management, a key technology that detects system anomalies, is health assessment, which analyzes and diagnoses the current system working status and quantitatively assesses the health of the system. This paper reviews the development of health assessment technology in recent years from three aspects: health definition, health assessment indicators, and health assessment approaches. In terms of health definition, this paper summarizes three common definition methods. Health assessment indicators are reviewed from four levels: process variables, data features, residuals, and fusion indicators. Finally, health assessment approaches are divided into model-based, data-driven, and fusion approaches. Concerning the data-driven approach, rapidly developing health assessment research based on an intelligent approach is discussed. The paper also compares various approaches and identifies the current challenges and development prospects of this technology.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131013366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrete sequence rearrangement based self-supervised chinese named entity recognition for robot instruction parsing","authors":"Cong Jiang, Qingyang Xu, Yong Song, Xianfeng Yuan, Bao Pang, Yibin Li","doi":"10.20517/ir.2023.21","DOIUrl":"https://doi.org/10.20517/ir.2023.21","url":null,"abstract":"Named entity recognition (NER) plays an important role in information extraction tasks, but most models rely on large-scale labeled data. Getting the model to move away from large-scale labeled datasets is challenging. In this paper, a SCNER (Self-Supervised NER) model is proposed. The BiLSTM (Bidirectional LSTM) is adopted as the named entity extractor, and an Instruction Generation Subsystem (IGS) is proposed to generate \"Retelling Instructions\", which analyzes the similarities between the input instructions and \"Retelling Instructions\" as the losses for model training. A series of rules based on traditional learning rules have been proposed for discrete forward computation and error backpropagation. It mimics language learning in human infants and constructs a SCNER model. This model is used for robot instruction understanding and can be trained on unlabeled datasets to extract named entities from instructions. Experimental results show that the proposed model is competitive with the supervised BiLSTM-CRF and BERT-NER models. In addition, the model is applied to a real robot, which verifies the practicality of SCNER.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127825556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPI observer-based active disturbance rejection control for a morphing quadrotor","authors":"Chunhui He, Haosheng Sun, Qingxian Wu, Yuanhao Su, Ning Sun","doi":"10.20517/ir.2023.18","DOIUrl":"https://doi.org/10.20517/ir.2023.18","url":null,"abstract":"Quadrotors are widely used in transportation, aerial photography, agricultural protection, and other important fields. Nevertheless, quadrotors with a fixed structure will face great challenges when crossing through or entering narrow spaces for operations. To improve quadrotor crossing ability in different environments, a morphing quadrotor is designed in this paper, and four servo motors are added to independently change four arm rotation angles. Meanwhile, the dynamic model and dynamic control allocation matrix are established. In addition, considering that the internal dynamic variation caused by morphologic changes and external disturbances may compromise system stability, a control method based on the generalized proportional integral (GPI) observer is proposed to increase the system robustness, and the corresponding stability analysis is provided. Finally, simulation results demonstrate the effectiveness of the proposed GPI observer-based active disturbance rejection control method.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126639596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}