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当期目录

2021年, 第4期 刊出日期:2021-12-31 上一期   
本期栏目:
Cover Image   收藏
Neng Pan, Ruibin Zhang, Tiankai Yang, Can Cui, Chao Xu, Fei Gao
IET Cyber-Systems and Robotics. 2021 (4): 1-1.   DOI: https://doi.org/10.1049/csy2.12039
摘要( 18 )  
Guest Editorial: Autonomous systems: Navigation, learning, and control   收藏
Yu Zhang, Fei Gao, Yuxiang Sun, Naira Hovakimyan, Zheng Fang
IET Cyber-Systems and Robotics. 2021 (4): 279-280.   DOI: https://doi.org/10.1049/csy2.12038
摘要( 21 )  
This is the IET Cyber-systems and Robotics special issue of Autonomous systems: Navigation, learning, and control. Autonomous systems, as very important representatives of Artificial Intelligence technologies, combine mechanical and electronic hardware, operating system, low-level dynamic control, and high-level intelligent decision components to address challenges that demand high-level autonomy and machine intelligence. Autonomous systems such as aerial robotics, ground vehicles, unmanned surface vehicles, and even all-terrain vehicles for aerospace applications, etc., have played an essential role in many aspects. For example, during the COVID-19 pandemic, autonomous systems have been used for disinfection and food delivery to reduce infection risks. However, navigation, learning, and control are critical for realizing true autonomy, which are still left for research. Navigation is a critical technology that works as the high-level intelligent decision component and directly determines the efficiency of conducting autonomous tasks. Learning, which is regarded as the most representative technology of artificial intelligence, has become a dominating method in many fields, such as image processing and understanding. There are also trends of adopting learning-based methods on autonomous robots, which are the perfect field to adopt state-of-the-art artificial intelligence technologies. The low-level dynamic control part, which builds the foundation of autonomous tasks, receives long-term research interest from modern control theory to intelligent control theory.
RGB-D SLAM with moving object tracking in dynamic environments   收藏
Weichen Dai, Yu Zhang, Yuxin Zheng, Donglei Sun, Ping Li
IET Cyber-Systems and Robotics. 2021 (4): 281-291.   DOI: https://doi.org/10.1049/csy2.12019
摘要( 50 )     PDF(0KB)( 24 )
Simultaneous localization and mapping methods are fundamental to many robotic applications. In dynamic environments, SLAM methods focus on eliminating the influence of moving objects to construct a static map since they assume a static world. To improve localization robustness in dynamic environments, an RGB-D SLAM method is proposed to build a complete 3D map containing both static and dynamic maps, the latter of which consists of the trajectories and points of the moving objects. Without any prior knowledge of the moving targets, the proposed method uses only the correlation between map points to discriminate between the static scene and the moving objects. After the static points are determined, camera motion estimation is performed only on reliable static map points to eliminate the influence of moving objects. The motion of the moving objects will then be estimated with the obtained camera motion by tracking their dynamic points in subsequent frames. Moreover, multiple groups of dynamic points that belong to the same moving object are fused by a volume overlap checking step. Experimental results are presented to demonstrate the feasibility and performance of the proposed method.
Fast-Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression   收藏
Neng Pan, Ruibin Zhang, Tiankai Yang, Can Cui, Chao Xu, Fei Gao
IET Cyber-Systems and Robotics. 2021 (4): 292-301.   DOI: https://doi.org/10.1049/csy2.12033
摘要( 41 )  
In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast-Tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: (1) the oversimplification in target detection by using artificial markers and (2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this study, we upgrade the target detection in Fast-Tracker to detect and localise a human target based on deep learning and non-linear regression to solve the former problem. For the latter one, we equip the quadrotor system with 360° active vision on a customised gimbal camera. Furthermore, we improve the tracking trajectory planning in Fast-Tracker by incorporating an occlusion-aware mechanism that generates observable tracking trajectories. Comprehensive real-world tests confirm the proposed system's robustness and real-time capability. Benchmark comparisons with Fast-Tracker validate that the proposed system presents better tracking performance even when performing more difficult tracking tasks. The cover image is based on the Original Article Fast-Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression by Can Cui et al., https://doi.org/10.1049/csy2.12033.
A survey of learning-based robot motion planning   收藏
Jiankun Wang, Tianyi Zhang, Nachuan Ma, Zhaoting Li, Han Ma, Fei Meng, Max Q.-H. Meng
IET Cyber-Systems and Robotics. 2021 (4): 302-314.   DOI: https://doi.org/10.1049/csy2.12020
摘要( 41 )     PDF(0KB)( 14 )
A fundamental task in robotics is to plan collision-free motions among a set of obstacles. Recently, learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments. This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning-related definition of motion-planning problem are provided in this article. Different learning-based motion-planning algorithms are introduced, and the combination of classical motion-planning and learning techniques is discussed in detail.
Deep reinforcement learning for shared control of mobile robots   收藏
Chong Tian, Shahil Shaik, Yue Wang
IET Cyber-Systems and Robotics. 2021 (4): 315-330.   DOI: https://doi.org/10.1049/csy2.12036
摘要( 35 )     PDF(0KB)( 13 )
Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance. However, prior work that seeks to find the optimal shared control ratio needs an accurate human model, which is usually challenging to obtain. In this study, the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient (DDPG) (TD3X)-based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally. The robot's states, shared control ratio in the previous time step, and human's control input is used as inputs to the reinforcement learning (RL) agent, which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model. Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio. Furthermore, to accelerate the training process and protect the robot, a navigation demonstration policy and a safety guard are developed. A neural network (NN) structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed. In addition, an extended DAGGER (DAGGERX) human agent is developed for training the RL agent to reduce human workload. Robot simulations and experiments with humans in the loop are conducted. The results show that the DAGGERX human agent can simulate real human inputs in the worst-case scenarios with a mean square error of 0.0039. Compared to the original TD3 agent, the TD3X-based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment. The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment, while the performance is equally good in the complex environment because of the use of an advanced human agent. In the human subject tests, participants' average perceived workload was significantly lower in shared control than that in exclusively manual control (26.90 vs. 40.07, p = 0.013).
Predictive-based optimal automatic formation control of mobile vehicles   收藏
Tadanao Zanma, Shunta Haga, Kenta Koiwa, Kang-Zhi Liu
IET Cyber-Systems and Robotics. 2021 (4): 331-342.   DOI: https://doi.org/10.1049/csy2.12034
摘要( 55 )     PDF(0KB)( 17 )
In recent years, formation control has received a great deal of attention as one of the most interesting issues in multiple mobile robots systems. In variable formation control systems, multiple mobile vehicles can form an appropriate formation from a list of possible formations, such that all mobile vehicles can pass even when the width of the course is narrow, and simultaneously the vehicles maintain a set distance between the course and each vehicle. This paper presents a predictive-based automatic formation control. In our formation control system, a unique formation is determined from feasible formations at any given sampling point. This control is formulated by its corresponding mixed-integer quadratic programing by introducing binary variables that are used to specify the formation. The effectiveness of the proposed methods is verified by applying two-wheeled mobile vehicles through simulations and experiments.
Technical report: PID design of second-order non-linear uncertain systems with fractional order operations   收藏
Song Chen, Tehuan Chen, Chao Xu, Jian Chu
IET Cyber-Systems and Robotics. 2021 (4): 343-346.   DOI: https://doi.org/10.1049/csy2.12027
摘要( 50 )     PDF(0KB)( 15 )
This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers (PIλDμ). The stability result of PIλDμ-based control systems is provided, such that the closed-loop systems can accomplish global stabilisation under some suitable conditions related to the system uncertainties. Finally, a simulation is demonstrated to verify the results.
Control of upper limb rehabilitation robot based on active disturbance rejection control   收藏
Junchen Li, Wenzhao Zhang, Yu Zheng, Aimin An, Wenda Pan
IET Cyber-Systems and Robotics. 2021 (4): 347-362.   DOI: https://doi.org/10.1049/csy2.12037
摘要( 38 )     PDF(0KB)( 17 )
The upper limb rehabilitation robot technology integrates rehabilitation medicine, human anatomy, mechanics, computer science, robotics, and many other disciplines. Its main function is to drive the affected limb to carry out rehabilitation training to restore the condition of patients with upper limb dyskinesia, which plays a great role in improving the quality of life. In this study, to resolve the problems of slow convergence speed and poor tracking accuracy due to the interference of patient spasms with the trajectory-tracking control of the upper limb rehabilitation robot, a novel algorithm based on active disturbance rejection control (ADRC) is adopted, and the convergence of its main structure is proved by the time-domain analysis method. First, this ADRC algorithm can obtain better trajectory-tracking performance due to its non-linear extended observer and non-linear feedback mechanism, even if the model suffers a strong disturbance or receives inaccurate information. Second, the non-linear tracking differentiator can guarantee to gain quick convergence speed. To validate this algorithm, a model of three degrees of freedom upper limb rehabilitation robot is established using MATLAB R2019b and three situations including strong spasm and weak spasm are carried out to prove the effectiveness and reliability of the control algorithm designed.