IET Cybersystems and Robotics最新文献

{"title":"An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post-disaster infrastructures","authors":"Xin Peng,&nbsp;Gaofeng Su,&nbsp;Raja Sengupta","doi":"10.1049/csy2.12107","DOIUrl":"10.1049/csy2.12107","url":null,"abstract":"<p>Catastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution <i>r</i>. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed-loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139601248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to Chinese personalised text-to-speech synthesis for robot human–machine interaction","authors":"","doi":"10.1049/csy2.12109","DOIUrl":"https://doi.org/10.1049/csy2.12109","url":null,"abstract":"<p>Pang, B., et al.: Chinese personalised text-to-speech synthesis for robot human-machine interaction. IET Cyber-Syst. Robot. e12098 (2023). https://doi.org/10.1049/csy2.12098</p><p>Incorrect grant number was used for the funder name “National Key Research and Development Plan of China” in the funding and acknowledgement sections. The correct grant number is 2020AAA0108900.</p><p>We apologize for this error.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An audio-based risky flight detection framework for quadrotors","authors":"Wansong Liu,&nbsp;Chang Liu,&nbsp;Seyedomid Sajedi,&nbsp;Hao Su,&nbsp;Xiao Liang,&nbsp;Minghui Zheng","doi":"10.1049/csy2.12105","DOIUrl":"https://doi.org/10.1049/csy2.12105","url":null,"abstract":"<p>Drones have increasingly collaborated with human workers in some workspaces, such as warehouses. The failure of a drone flight may bring potential risks to human beings' life safety during some aerial tasks. One of the most common flight failures is triggered by damaged propellers. To quickly detect physical damage to propellers, recognise risky flights, and provide early warnings to surrounding human workers, a new and comprehensive fault diagnosis framework is presented that uses only the audio caused by propeller rotation without accessing any flight data. The diagnosis framework includes three components: leverage convolutional neural networks, transfer learning, and Bayesian optimisation. Particularly, the audio signal from an actual flight is collected and transferred into time–frequency spectrograms. First, a convolutional neural network-based diagnosis model that utilises these spectrograms is developed to identify whether there is any broken propeller involved in a specific drone flight. Additionally, the authors employ Monte Carlo dropout sampling to obtain the inconsistency of diagnostic results and compute the mean probability score vector's entropy (uncertainty) as another factor to diagnose the drone flight. Next, to reduce data dependence on different drone types, the convolutional neural network-based diagnosis model is further augmented by transfer learning. That is, the knowledge of a well-trained diagnosis model is refined by using a small set of data from a different drone. The modified diagnosis model has the ability to detect the broken propeller of the second drone. Thirdly, to reduce the hyperparameters' tuning efforts and reinforce the robustness of the network, Bayesian optimisation takes advantage of the observed diagnosis model performances to construct a Gaussian process model that allows the acquisition function to choose the optimal network hyperparameters. The proposed diagnosis framework is validated via real experimental flight tests and has a reasonably high diagnosis accuracy.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139435296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive neural tracking control for upper limb rehabilitation robot with output constraints","authors":"Zibin Zhang,&nbsp;Pengbo Cui,&nbsp;Aimin An","doi":"10.1049/csy2.12104","DOIUrl":"https://doi.org/10.1049/csy2.12104","url":null,"abstract":"<p>The authors investigate the trajectory tracking control problem of an upper limb rehabilitation robot system with unknown dynamics. To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot, an adaptive neural full-state feedback control is proposed. The neural network is utilised to approximate the dynamics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient. By incorporating a high-gain observer, unmeasurable state information is integrated into the output feedback control. Taking into consideration the issue of joint position constraints during the actual rehabilitation training process, an adaptive neural full-state and output feedback control scheme with output constraint is further designed. From the perspective of safety in human–robot interaction during rehabilitation training, log-type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region. The stability of the closed-loop system is proved by Lyapunov stability theory. The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139047594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments","authors":"Daniel Mitchell,&nbsp;Paul Dominick Emor Baniqued,&nbsp;Abdul Zahid,&nbsp;Andrew West,&nbsp;Bahman Nouri Rahmat Abadi,&nbsp;Barry Lennox,&nbsp;Bin Liu,&nbsp;Burak Kizilkaya,&nbsp;David Flynn,&nbsp;David John Francis,&nbsp;Erwin Jose Lopez Pulgarin,&nbsp;Guodong Zhao,&nbsp;Hasan Kivrak,&nbsp;Jamie Rowland Douglas Blanche,&nbsp;Jennifer David,&nbsp;Jingyan Wang,&nbsp;Joseph Bolarinwa,&nbsp;Kanzhong Yao,&nbsp;Keir Groves,&nbsp;Liyuan Qi,&nbsp;Mahmoud A. Shawky,&nbsp;Manuel Giuliani,&nbsp;Melissa Sandison,&nbsp;Olaoluwa Popoola,&nbsp;Ognjen Marjanovic,&nbsp;Paul Bremner,&nbsp;Samuel Thomas Harper,&nbsp;Shivoh Nandakumar,&nbsp;Simon Watson,&nbsp;Subham Agrawal,&nbsp;Theodore Lim,&nbsp;Thomas Johnson,&nbsp;Wasim Ahmad,&nbsp;Xiangmin Xu,&nbsp;Zhen Meng,&nbsp;Zhengyi Jiang","doi":"10.1049/csy2.12103","DOIUrl":"https://doi.org/10.1049/csy2.12103","url":null,"abstract":"<p>Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop (HITL) robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites. The authors present a novel implementation of a Cyber-Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi-robot (MR) team coordinated by a HITL operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi-staged mission within an inspection scenario for the heterogeneous Symbiotic Multi-Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together, where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139047599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Off-policy correction algorithm for double Q network based on deep reinforcement learning","authors":"Qingbo Zhang,&nbsp;Manlu Liu,&nbsp;Heng Wang,&nbsp;Weimin Qian,&nbsp;Xinglang Zhang","doi":"10.1049/csy2.12102","DOIUrl":"https://doi.org/10.1049/csy2.12102","url":null,"abstract":"<p>A deep reinforcement learning (DRL) method based on the deep deterministic policy gradient (DDPG) algorithm is proposed to address the problems of a mismatch between the needed training samples and the actual training samples during the training of intelligence, the overestimation and underestimation of the existence of Q-values, and the insufficient dynamism of the intelligence policy exploration. This method introduces the Actor-Critic Off-Policy Correction (AC-Off-POC) reinforcement learning framework and an improved double Q-value learning method, which enables the value function network in the target task to provide a more accurate evaluation of the policy network and converge to the optimal policy more quickly and stably to obtain higher value returns. The method is applied to multiple MuJoCo tasks on the Open AI Gym simulation platform. The experimental results show that it is better than the DDPG algorithm based solely on the different policy correction framework (AC-Off-POC) and the conventional DRL algorithm. The value of returns and stability of the double-Q-network off-policy correction algorithm for the deep deterministic policy gradient (DCAOP-DDPG) proposed by the authors are significantly higher than those of other DRL algorithms.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139041971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Printed circuit board solder joint quality inspection based on lightweight classification network","authors":"Zhicong Zhang,&nbsp;Wenyu Zhang,&nbsp;Donglin Zhu,&nbsp;Yi Xu,&nbsp;Changjun Zhou","doi":"10.1049/csy2.12101","DOIUrl":"https://doi.org/10.1049/csy2.12101","url":null,"abstract":"<p>Solder joint quality inspection is a crucial step in the qualification inspection of printed circuit board (PCB) components, and efficient and accurate inspection methods will greatly improve its production efficiency. In this paper, we propose a PCB solder joint quality detection algorithm based on a lightweight classification network. First, the Select Joint segmentation method was used to obtain the solder joint information, and colour space conversion was used to locate the solder joint. The mask method, contour detection, and box line method were combined to complete the extraction of solder joint information. Then, by combining the respective characteristics of convolutional neural network and Transformer and introducing Cross-covariance attention to reduce the computational complexity and resource consumption of the model and evenly distribute the global view mutual information in the whole training process, a new lightweight network model MobileXT is proposed to complete defect classification. Only 16.4% of the Vision Transformer computing resources used in this model can achieve an average accuracy improvement of 31%. Additionally, the network is trained and validated using a dataset of 1804 solder joint images constructed from 93 PCB images and two external datasets to evaluate MobileXT performance. The proposed method achieves more efficient localization of the solder joint information and more accurate classification of weld joint defects, and the lightweight model design is more appropriate for industrial edge device deployments.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134806524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A proposal on centralised and distributed optimisation via proportional–integral–derivative controllers (PID) control perspective","authors":"Jiaxu Liu,&nbsp;Song Chen,&nbsp;Shengze Cai,&nbsp;Chao Xu","doi":"10.1049/csy2.12100","DOIUrl":"10.1049/csy2.12100","url":null,"abstract":"<p>Motivated by the excellent performance of proportional–integral–derivative controllers (PIDs) in the field of control, the authors injected the philosophy of PID into optimisation and introduced two types of novel PID optimisers from a continuous-time view, which benefit from the idea that discrete-time optimisation algorithm can be modelled as a continuous dynamical system/controlled system. For centralised optimisation, the authors discuss the idea of the first-order PID optimiser and the second-order accelerated PID optimiser. Furthermore, this framework is extended into distributed optimisation settings, and a distributed PID optimiser is proposed. Finally, some numerical examples are given to verify our ideas.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spherical robot: A novel robot for exploration in harsh unknown environments","authors":"Wei Ren,&nbsp;You Wang,&nbsp;Haoxiang Liu,&nbsp;Song Jin,&nbsp;Yixu Wang,&nbsp;Yifan Liu,&nbsp;Ziang Zhang,&nbsp;Tao Hu,&nbsp;Guang Li","doi":"10.1049/csy2.12099","DOIUrl":"https://doi.org/10.1049/csy2.12099","url":null,"abstract":"<p>The authors propose a complete software and hardware framework for a novel spherical robot to cope with exploration in harsh and unknown environments. The proposed robot is driven by a heavy pendulum covered by a fully enclosed spherical shell, which is strongly protected, amphibious, anti-overturn and has a long-battery-life. Algorithms for location and perception, planning and motion control are comprehensively designed. On the one hand, the authors fully consider the kinematic model of a spherical robot, propose a positioning algorithm that fuses data from inertial measurement units, motor encoder and Global Navigation Satellite System, improve global path planning algorithm based on Hybrid A* and design an instruction planning controller based on model predictive control (MPC). On the other hand, the dynamic model is built, linear MPC and robust servo linear quadratic regulator algorithm is improved, and a speed controller and a direction controller are designed. In addition, based on the pose and motion characteristics of a spherical robot, a visual obstacle perception algorithm and an electronic image stabilisation algorithm are designed. Finally, the authors build physical systems to verify the effectiveness of the above algorithms through experiments.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71984763","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 novel multifunctional intelligent bed integrated with multimodal human–robot interaction approach and safe nursing methods","authors":"Donghui Zhao,&nbsp;Yuhui Wu,&nbsp;Chenhao Yang,&nbsp;Junyou Yang,&nbsp;Houdei Liu,&nbsp;Shuoyu Wang,&nbsp;Yinlai Jiang,&nbsp;Yokoi Hiroshi","doi":"10.1049/csy2.12097","DOIUrl":"https://doi.org/10.1049/csy2.12097","url":null,"abstract":"<p>The authors propose a multifunctional intelligent bed (MIB) that integrates multiple modes of interaction to improve the welfare of mobility-impaired users and reduce the workload of medical personnel. The MIB features independent autonomous omnidirectional movement, position adjustment, multi-degree-of-freedom (DOF) movement regulation and posture memory functions to facilitate comfortable and convenient interaction for mobility-impaired users. In particular, an integrated “MIB-state perception-interaction interfaces” system is established, and a bed fall risk detection algorithm and assisted get-up-transfer algorithm is proposed. By recognising and sharing human body state characteristics, nursing collaboration can be achieved with caregivers or other nursing robots. Comprehensive experiments demonstrate that the MIB is a novel MIB that is highly adaptable to the environment, convenient to interact with and safe. By integrating the proposed algorithms, daily safety monitoring, assisted get-up and defecation tasks can be effectively accomplished. This technology demonstrates excellent applicability and promising prospects for implementation in hospitals, nursing centres and homes catering to elderly and disabled individuals with mobility impairments.</p>","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}