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Chen Z, Zhan G, Jiang Z, Zhang W, Rao Z, Wang H, Li J. Adaptive impedance control for docking robot via Stewart parallel mechanism. ISA TRANSACTIONS 2024:1-12. [PMID: 39368867 DOI: 10.1016/j.isatra.2024.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/19/2024] [Accepted: 09/06/2024] [Indexed: 10/07/2024]
Abstract
This paper provides an adaptive impedance control strategy about docking robot, a locking mechanism scheme based on the Stewart platform developing for the problem of excessive collision contact force caused by external environmental interference during autonomous docking tasks of ground unmanned vehicles. First, the docking robot system was introduced, and an inverse kinematics model of the docking robot was constructed. Next, to solve the problem of excessive collision contact force during docking, we have designed an adaptive impedance control algorithm, which includes a steady-state error model of contact force, an adaptive compensation controller design, and system stability analysis, thus achieving active compliance control. Finally, some simulations and experiments were conducted on the docking robot. Compared with traditional impedance control, adaptive impedance control reduces docking collision contact force and achieves compliant control. In the future, the experimental results provide a new docking approach for autonomous docking of unmanned vehicles, and also serve as a reference for the development of intelligent vehicles.
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Affiliation(s)
- Zhihua Chen
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Gan Zhan
- School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhifan Jiang
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang, 330063, China
| | - Wencai Zhang
- NORINCO GROUP LIAO SHEN INDUSTRIES GROUP CO., LTD, Shenyang 110044, China
| | - Zhibo Rao
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang, 330063, China
| | - Hua Wang
- Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang, 330063, China
| | - Jiehao Li
- College of Engineering, South China Agricultural University, Guangzhou, 510642, China
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Sharma O, Sahoo NC, Puhan NB. Kernelized convolutional transformer network based driver behavior estimation for conflict resolution at unsignalized roundabout. ISA TRANSACTIONS 2023; 133:13-28. [PMID: 35879112 DOI: 10.1016/j.isatra.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 07/06/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
The modeling of driver behavior plays an essential role in developing Advanced Driver Assistance Systems (ADAS) to support the driver in various complex driving scenarios. The behavior estimation of surrounding vehicles is crucial for an autonomous vehicle to safely navigate through an unsignalized intersection. This work proposes a novel kernelized convolutional transformer network (KCTN) with multi-head attention (MHA) mechanism to estimate driver behavior at a challenging unsignalized three-way roundabout. More emphasis has been placed on creating convolution in non-linear space by introducing a kervolution operation into the proposed network. It generalizes convolution, improves model capacity, and captures higher-order feature interactions by using Gaussian kernel function. The proposed model is validated using the real-world ACFR dataset, where it outperforms current state-of-the-art in terms of behavior prediction accuracy and provides a significant lead time before potential conflict situations.
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Affiliation(s)
- Omveer Sharma
- School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India.
| | - N C Sahoo
- School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India.
| | - Niladri B Puhan
- School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India.
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He S, Hu C, Lin S, Zhu Y, Tomizuka M. Real-time time-optimal continuous multi-axis trajectory planning using the trajectory index coordination method. ISA TRANSACTIONS 2022; 131:639-649. [PMID: 35662517 DOI: 10.1016/j.isatra.2022.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 05/16/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
Real-time time-optimal trajectory planning exists in a wide range of applications such as computer numerical control (CNC) manufacturing, robotics and autonomous vehicles. Generally, the methods to generate time-optimal trajectory can be categorized as non-real-time methods and real-time methods. Non-real-time methods such as direct optimization method tend to generate time-optimal trajectory through nonlinear or linear programming while it is computationally prohibitive for high frequency real-time applications. Current real-time methods are computationally efficient but either deal with the sparse waypoint trajectories or sacrifice the time optimality a lot. This paper innovatively proposed a time-optimal switching trajectory index coordination (TOS-TIC) framework to solve the real-time time-optimal planning problem for continuous multi-axis trajectories. The proposed method is able to generate time-optimal trajectory for continuous geometric paths while considering the axial velocity and acceleration constraints. The time-optimality of the trajectory planned by TOS-TIC is nearly the same as the offline planned optimal results. Meanwhile, the proposed method is computationally efficient for even 5kHz real-time applications. The main idea of TOS-TIC is coordinating several one-axis time-optimal switching controls to generate a modified control that decreases the state deviation from the desired trajectory. Several comparative experiments are carried out on an industrial biaxial linear motor stage. And the experimental results consistently verify that the proposed TOS-TIC real-time planner generates faster trajectory compared with the real-time lookahead method. In addition, the trajectory running time and final tracking error of the proposed method are nearly the same as the offline direct optimization method.
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Affiliation(s)
- Suqin He
- State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Precision/Ultra-Precision Manufacture Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Chuxiong Hu
- State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Precision/Ultra-Precision Manufacture Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Shize Lin
- State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Precision/Ultra-Precision Manufacture Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Yu Zhu
- State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Precision/Ultra-Precision Manufacture Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Masayoshi Tomizuka
- Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA.
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Wu Y, Wang Y, Fang H. Full-state constrained neural control and learning for the nonholonomic wheeled mobile robot with unknown dynamics. ISA TRANSACTIONS 2022; 125:22-30. [PMID: 34167818 DOI: 10.1016/j.isatra.2021.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 06/08/2021] [Accepted: 06/10/2021] [Indexed: 06/13/2023]
Abstract
The adaptive learning and control are proposed for the full-state(FS) constrained NWMR system with external destabilization. First, the constrained state is reformulated as the unconstrained state. Then, approximating the unknown dynamics in the closed-loop (CL) system is conducted via radial basis function (RBF) NN. Also, a sliding term is designed to deal with the external destabilization and the neural network training error. The derived adaptive neural controller can realize the asymptotic stability of a robot system without violating FS constraints. Moreover, the neural weights are converged so that the unknown dynamics are expressed by the constant weights in the CL system. It is also applicable to other similar control tasks. Lastly, the proposed algorithm is simulated and validated.
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Affiliation(s)
- Yuxiang Wu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
| | - Yu Wang
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
| | - Haoran Fang
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
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Water cycle algorithm: an approach for improvement of navigational strategy of multiple humanoid robots. ROBOTICA 2021. [DOI: 10.1017/s0263574721000837] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
AbstractThis paper presents an efficient water cycle algorithm based on the processes of water cycle with movement of streams and rivers in to the sea. This optimization algorithm is applied to obtain the optimal feasible path with minimum travel duration during motion planning of both single and multiple humanoid robots in both static and dynamic cluttered environments. This technique discards the rainfall process considering falling water droplets forming streams during raining and the process of flowing. The flowing process searches the solution space and finds the more accurate solution and represents the local search. Motion planning of humanoids is carried out in V-REP software. The performance of proposed algorithm is tested in experimental scenario under laboratory conditions and shows the developed algorithm performs well in terms of obtaining optimal path length and minimum time span of travel. Here, navigational analysis has been performed on both single as well as multiple humanoid robots. Statistical analysis of results obtained from both simulation and experimental environments is carried out for both single and multiple humanoids, along with the comparison with another existing optimization technique that indicate the strength and effectiveness of the proposed water cycle algorithm.
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Chen Z, Wang S, Wang J, Xu K, Lei T, Zhang H, Wang X, Liu D, Si J. Control strategy of stable walking for a hexapod wheel-legged robot. ISA TRANSACTIONS 2021; 108:367-380. [PMID: 32950232 DOI: 10.1016/j.isatra.2020.08.033] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 08/20/2020] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
This paper provides a legged stable walking control strategy based on multi-sensor information feedback about BIT-NAZA-II, a large load parallel hexapod wheel-legged robot developing for the problem of vertical contact impact and horizontal sliding of heavy leg robot in complex terrain environments. The BIT-NAZA-II robot has six legs and six wheels, and the wheels are installed on the foot-end. The wheels of each foot-end for the legs of the robot are locked when walking with legs. In order to realize the smooth transition between swing phase and stance phase, the leg motion is divided into different stages for control by state machine switching controller based on event (SMSCE). In the Z-direction, in order to avoid the shaking of the body caused by the contact impact at the moment of contact between the foot-end and the ground during the walking of the robot, an active compliance controller (ACC) based on impedance control (IC) is applied to solve the problem of contact impact. Moreover, in the X-direction, the swing leg retraction (SLR) based on Bezier curve (BC) is introduced to generate the foot-end trajectory of the robot, which solves the slip problem of the heavy leg robot and improves the horizontal stability. Finally, the control strategy of stable walking is respectively verified by the simulations and experiments. The results show that the ACC based on IC can effectively reduce the contact impact between the foot-end and the ground in the Z-direction and improve the stability of body. Besides, the anti-sliding ability is realized after introducing SLR based on BC in the X-direction, and we also verify that stable walking control strategy is effective, which provides a reference value for the stable walking of heavy leg robot in complex terrain.
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Affiliation(s)
- Zhihua Chen
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Shoukun Wang
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China.
| | - Junzheng Wang
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Kang Xu
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Tao Lei
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Hao Zhang
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Xiuwen Wang
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Daohe Liu
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Jinge Si
- Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Servo Motion System Drive and Control, Ministry of Industry and Information Technology, School of Automation, Beijing Institute of Technology, Beijing 100081, China
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Moreno-Valenzuela J, Montoya-Villegas L, Pérez-Alcocer R, Sandoval J. A family of saturated controllers for UWMRs. ISA TRANSACTIONS 2020; 100:495-509. [PMID: 31980208 DOI: 10.1016/j.isatra.2020.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 12/09/2019] [Accepted: 01/03/2020] [Indexed: 06/10/2023]
Abstract
Input saturation appears in a physical system when a large power dissipation is requested. In this situation, and specifically for unicycle-type wheeled mobile robots, actuators only can deliver a finite amount of power. Thus, in practice the linear and angular velocity input of this class of mobile robots is limited and this should be considered in the control design. In this paper, a family of controllers that produce saturated velocity input for unicycle-type wheeled mobile robots is presented. The proposed family of controllers is designed to satisfy the trajectory tracking control goal. Sufficient conditions to prove the closed-loop system global asymptotic stability are established by using Lyapunov's theory. Already reported schemes and original designs are shown to satisfy the properties of the given family of controllers. By using two different motion tasks, experimental tests in real-time with five saturated control schemes are presented in order to validate the proposed theory. In order to show the ability of the family of controllers to produce limited control action, experiments have also been carried out with an unsaturated algorithm. Better tracking accuracy is obtained with the original design derived from the proposed class of algorithms.
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Affiliation(s)
- Javier Moreno-Valenzuela
- Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Colonia Nueva Tijuana, Tijuana, Baja California 22435, México.
| | - Luis Montoya-Villegas
- Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Colonia Nueva Tijuana, Tijuana, Baja California 22435, México.
| | - Ricardo Pérez-Alcocer
- CONACYT-Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Colonia Nueva Tijuana, Tijuana, Baja California 22435, México.
| | - Jesús Sandoval
- Tecnológico Nacional de México/Instituto Tecnológico de la Paz, Boulevard Forjadores de Baja California Sur No. 4720, La Paz, Baja California Sur, 23080, México.
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Amirkhani A, Shirzadeh M, Shojaeefard MH, Abraham A. Controlling wheeled mobile robot considering the effects of uncertainty with neuro-fuzzy cognitive map. ISA TRANSACTIONS 2020; 100:454-468. [PMID: 31916988 DOI: 10.1016/j.isatra.2019.12.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/19/2019] [Accepted: 12/20/2019] [Indexed: 06/10/2023]
Abstract
In this paper, we present neuro-fuzzy cognitive map (NFCM) to control a non-holonomic wheeled mobile robot, for both the kinematic control and the dynamic control. For this purpose, the rules for updating the parameters of NFCM used in online training have been extracted. Also, the convergence of the presented approach has been confirmed by means of Lyapunov method. To evaluate the strength and robustness of the proposed model, it has been tested in tracking different circular and square paths. Experimental results indicate that despite the presence of disturbances, the changes of system parameters, and the existence of non-holonomic constraints, our robot has been able to follow challenging paths (e.g. square-shape trajectories) successfully.
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Affiliation(s)
- Abdollah Amirkhani
- School of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.
| | - Masoud Shirzadeh
- Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15875-4413, Iran
| | - Mohammad H Shojaeefard
- Department of Mechanical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
| | - Ajith Abraham
- Machine Intelligence Research Laboratories, Washington, DC 98071-2259, USA
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