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Kana S, Gurnani J, Ramanathan V, Ariffin MZ, Turlapati SH, Campolo D. Learning Compliant Box-in-Box Insertion through Haptic-Based Robotic Teleoperation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8721. [PMID: 37960421 PMCID: PMC10648443 DOI: 10.3390/s23218721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/30/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023]
Abstract
In modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are frequently used in robotics to facilitate skill transfer from humans to robots, can be one solution for complex tasks that are difficult to mathematically model. In order to automate the box-in-box insertion task for packaging applications, this study makes use of LfD techniques. The proposed framework has three phases. Firstly, a master-slave teleoperated robot system is used in the initial phase to haptically demonstrate the insertion task. Then, the learning phase involves identifying trends in the demonstrated trajectories using probabilistic methods, in this case, Gaussian Mixture Regression. In the third phase, the insertion task is generalised, and the robot adjusts to any object position using barycentric interpolation. This method is novel because it tackles tight insertion by taking advantage of the boxes' natural compliance, making it possible to complete the task even with a position-controlled robot. To determine whether the strategy is generalisable and repeatable, experimental validation was carried out.
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Affiliation(s)
| | | | | | | | | | - Domenico Campolo
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore; (S.K.); (J.G.); (V.R.); (M.Z.A.); (S.H.T.)
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2
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DMPs-based skill learning for redundant dual-arm robotic synchronized cooperative manipulation. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00429-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractDual-arm robot manipulation is applicable to many domains, such as industrial, medical, and home service scenes. Learning from demonstrations is a highly effective paradigm for robotic learning, where a robot learns from human actions directly and can be used autonomously for new tasks, avoiding the complicated analytical calculation for motion programming. However, the learned skills are not easy to generalize to new cases where special constraints such as varying relative distance limitation of robotic end effectors for human-like cooperative manipulations exist. In this paper, we propose a dynamic movement primitives (DMPs) based skills learning framework for redundant dual-arm robots. The method, with a coupling acceleration term to the DMPs function, is inspired by the transient performance control of Barrier Lyapunov Functions. The additional coupling acceleration term is calculated based on the constant joint distance and varying relative distance limitations of end effectors for object-approaching actions. In addition, we integrate the generated actions in joint space and the solution for a redundant dual-arm robot to complete a human-like manipulation. Simulations undertaken in Matlab and Gazebo environments certify the effectiveness of the proposed method.
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Chi M, Liu Y, Yao Y, Liu Y, Li S, Zeng C, Zhong M. Development and evaluation of demonstration information recording approach for wheelchair mounted robotic arm. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00350-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractTo offer simple and convenient assistance for the elderly and disabled, researchers focus on programming by demonstration approach to improve the intelligence and adaptability of wheelchair mounted robotic arm assistive robot. But how to easily and quickly obtain the demonstration information is still an urgent problem to be solved. Based on the systematic analysis of the daily living tasks in need of robot assistance, this paper proposes the key-point-based programming by demonstration recording approach to quickly obtain the demonstration information and develops a specified demonstration interface to simplify the operation process. A corresponding evaluation approach is also proposed from the demonstration trajectories and demonstration process two aspects. Additionally, tasks of “holding water glass task”, “eating task”, and “opening door task” are carried out and experimental results, as well as comparative evaluations confirm the validity of the proposed approach with high efficiency. This study can not only offer a convenient and feasible way to obtain the demonstration information of daily living tasks, but also lay a good foundation for the assistive robot to learn relative motion skills, especially for the demonstrated dexterous manipulation skills, and semi-autonomously accomplish complex, multi-step tasks following the user’s instructions in the daily home environment.
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Liu M, Peng B, Shang M. Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00341-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractFor the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient’s movement intention is a challenging research work, which needs to be studied from the shallow to the deep. Specifically, it is necessary to ensure that the movement intention of the normal person can be accurately recognized, and then improve the model to realize the recognition of the movement intention of the patients. Therefore, before studying the patient’s movement intention, it is essential to consider the normal person first, which is also for safety considerations. In recent years, a new Hill-based muscle model has been demonstrated to be capable of directly estimating the joint angle intention in an open-loop form. On this basis, by introducing a recurrent neural network (RNN), the whole prediction process can achieve more accuracy in a closed-loop form. However, for the traditional RNN algorithms, the activation function must be convex, which brings some limitations to the solution of practical problems. Especially, when the convergence speed of the traditional RNN model is limited in the practical applications, as the error continues to decrease, the convergence performance of the traditional RNN model will be greatly affected. To this end, a projected recurrent neural network (PRNN) model is proposed, which relaxes the condition of the convex function and can be used in the saturation constraint case. In addition, the corresponding theoretical proof is given, and the PRNN method with saturation constraint has been successfully applied in the experiment of intention recognition of lower limb movement compared with the traditional RNN model.
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Si W, Wang N, Yang C. A review on manipulation skill acquisition through teleoperation‐based learning from demonstration. COGNITIVE COMPUTATION AND SYSTEMS 2021. [DOI: 10.1049/ccs2.12005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Weiyong Si
- Bristol Robotics Laboratory University of the West of England Bristol UK
| | - Ning Wang
- Bristol Robotics Laboratory University of the West of England Bristol UK
| | - Chenguang Yang
- Bristol Robotics Laboratory University of the West of England Bristol UK
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Luo J, He W, Yang C. Combined perception, control, and learning for teleoperation: key technologies, applications, and challenges. COGNITIVE COMPUTATION AND SYSTEMS 2020. [DOI: 10.1049/ccs.2020.0005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Jing Luo
- Key Laboratory of Autonomous Systems and Networked ControlSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou510640People's Republic of China
| | - Wei He
- School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083People's Republic of China
| | - Chenguang Yang
- Key Laboratory of Autonomous Systems and Networked ControlSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou510640People's Republic of China
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7
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Affiliation(s)
- Jing Luo
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China
- Department of Bioengineering, Imperial College of Science Technology and Medicine, London, UK
| | - Chao Liu
- Department of Robotics, LIRMM, UMR5506, University of Montpellier-CNRS, Montpellier, France
| | - Ying Feng
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, People's Republic of China
| | - Chenguang Yang
- Bristol Robotics Laboratory, University of the West of England, Bristol, UK
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9
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Aragón-Martínez A, Arias-Montiel M, Lugo-González E, Tapia-Herrera R. Two-finger exoskeleton with force feedback for a mobile robot teleoperation. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881419895648] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this work, the design, manufacturing, instrumentation, and application of a two-finger exoskeleton with force feedback are presented. The exoskeleton is based on remote center of motion mechanisms in order to avoid mechanical interference with the user’s fingers and is manufactured by three-dimensional printing. The developed exoskeleton is applied in a mobile robot teleoperation by mapping the finger movements in forward and turning commands for the robot. The presence of obstacles detected by the robot is sensed by the user by means of a feedback force. The problem of simultaneously communicating a data acquisition card and the robot hardware by MATLAB ® Simulink® was solved by using an external Wi-Fi module. The result is a lightweight exoskeleton which is able to communicate bidirectionally with a mobile robot by a personal computer for teleoperation tasks. The success of the system implementation is proven by a set of experiments presented in the final part of the article.
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Affiliation(s)
- Aldo Aragón-Martínez
- Institute of Electronics and Mechatronics, Universidad Tecnológica de la Mixteca, Oaxaca, México
| | - Manuel Arias-Montiel
- Institute of Electronics and Mechatronics, Universidad Tecnológica de la Mixteca, Oaxaca, México
| | - Esther Lugo-González
- Institute of Electronics and Mechatronics, Universidad Tecnológica de la Mixteca, Oaxaca, México
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Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9142865] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).
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