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Chinese Control Conference (CCC)

作者： Zakarya Omar Ke-Xian Liu Chao Ren Qing-Hao Meng Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Engineering Sana'a University Sana'a Yemen

A Multi-Joint Autonomous Underwater Vehicle (MJ-AUV) is designed and equipped with multiple movable segments or joints that allow for greater maneuverability and flexibility in the water. These joints allow the MJ-AUV to change its shape, orientation, and direction of movement, so it can access tight spaces or navigate in complex underwater environments, making it useful for a variety of tasks in the deep sea, such as surveillance, exploration, and maintenance. However, the multiple joint structure of the MJ-AUV complicates the dynamics of the system. Furthermore, the joints' rotation causes misalignment of the cabins, causing deviations in the desired forces and torques. To address these issues, this study focuses on the 2-dimensional (2D) modeling as well as the trajectory tracking controller of the MJ-AUV. In this setting, the proposed controller utilizes a hierarchically structured robust nonlinear back-stepping and sliding-mode approach. Hence, this controller is simulated under various uncertainties and disturbances and the results show that it presents a global finite-time convergence to a bounded neighborhood of the origin for both position and velocity tracking errors, demonstrating its effectiveness and robustness.

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Collaborative turning and jumping control of a cyborg locust via sensory stimulation 3

Collaborative turning and jumping control of a cyborg locust...

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3rd IEEE International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2021

作者： Liu, Peng Ma, Songsong Liu, Shen Li, Yao Li, Bing Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Shenzhen518055 China

ISBN: (纸本)9781665449663

In this study, we describe the first-ever demonstration of the turning and jumping control of locusts based on electrical stimulation, which can realize a cyborg jumper. In terms of perception, walking distance, speed, payload, maneuverability, and adaptability to complex environments, cyborgs can reveal unmatchable advantages to insect-scale artificial robots. Herein, live locusts (Locusta migratoria) were selected as the study platform. Antennae and cercus stimulations using electrical pulses were applied to elicit the insect to turn and jump. We examined both the turning angle and the angular velocity by tuning the stimulus amplitude or frequency. It is found the turning rate induced by antenna stimulation can be graded. Besides, the stimulation of the cercus at the distal abdomen triggered the jump reliably. Moreover, the directional jumping control of the locust was achieved by the collaboration of turning and jumping control. Such results provide the potential to establish a more precise and more efficient model to develop a direction-controllable cyborg jumper. © 2021 IEEE.

关键词： Antennas

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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International Conference on Mechatronics (ICM)

作者： Zhenyu Li Ruixin Wu Jin Wang Chuanqi Hu Zhi Zheng Jun Meng Guodong Lu School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China Polytechnic Institute Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture.

关键词： Point cloud compression Visualization Three-dimensional displays Trajectory planning Heuristic algorithms Collaboration Real-time systems Safety Optimization Quadrotors

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Investigating Neural Network Hyperparameter Variations in Robotic Arm Inverse Kinematics for Different Arm Lengths

Investigating Neural Network Hyperparameter Variations in Ro...

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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis EI Manar Le Belvédère Tunis Tunisia

This research delves into the evaluation of Artificial Neural Networks (ANNs) in tackling the intricate domain of inverse kinematics problems within the context of robotic systems. Compared to direct kinematics, inverse kinematics presents significant challenges, especially when dealing with different arm lengths, which can impact the system's overall functionality. In this study, we conduct a comprehensive comparative analysis to assess the performance of ANNs across various robotic arm lengths. Following the forward kinematics of a simple 2-DoF robotic manipulator, three distinct datasets (fixed step size, random step size, and sinusoidal step size) are generated for training, testing, and validation phases. For every dataset, three optimizers, namely Levenberg Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient, are considered with different hyperparameters to investigate the performance of the ANN models. Based on the validation of MSE values, ANN models with different hyperparameters are found to be a promising approach in estimating the inverse kinematic solutions for different arm lengths with data complexities. Such solutions can be further extended to higher DoF robotic solutions and save the computational burden of evaluating analytical solutions.

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KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove

KNTU-RoboGlove: Design of a Pneumatic Soft Robotic Glove

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Tanin Boka M. Navid Nikkhah S. Ali A. Moosavian Dept of Mechanical Engineering K. N. Center of Excellence in Robotics and Control Advanced Robotics & Automated Systems (ARAS) Laboratory Toosi University of Technology Tehran Tehran Iran Dept of Mechanical Engineering K. N. Toosi University of Technology Tehran Tehran Iran

This paper introduces an innovative advancement in the field of wearable assistive technology by presenting a revolutionary portable soft robotic glove designed to assist individuals with hand impairments during rehabilitation exercises and daily tasks. The glove offers both active finger flexion and passive extension to aid in hand support and rehabilitative training. It achieves this by utilizing integrated soft actuators, including a variable stiffness layer and a pneumatic-driven layer, which guide specific bending, twisting, and extending movements when air pressure is applied. Notably, the variable stiffness layer takes inspiration from pangolins, whose scales are pliable during daily activities but become rigid when facing threats. To provide increased power with enhanced stiffness, a toothed pneumatic actuator is specially designed. The three-finger soft glove is manufactured by molding super-elastic material, creating the actuator mold using a 3D printer. The glove’s grasping abilities have been thoroughly tested, and experimental results demonstrate that, when compared to previously developed elastomeric-based actuators, these actuators can generate substantial force and torque to facilitate finger flexion even at lower air pressure levels. This capability allows the glove to grasp a wide range of objects, assisting individuals in their rehabilitative training. Furthermore, a control system for grasping has been developed, which includes motion processing unit sensors for collecting feedback signals aligned with the actuators, along with a closed-loop controller for regulating the pressure. In contrast to existing devices, this glove, thanks to its portable design, has the potential to significantly enhance user autonomy and independence.

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Online Optimal Energy Management for Building Heating with Thermal Energy Storage

Online Optimal Energy Management for Building Heating with T...

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IEEE Conference on Decision and Control

作者： Mengfan Cao Shibo Chen Haoyu Miao Zaiyue Yang Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper studies the online optimal energy management problem for the building heating system. This system is driven by electric heat pumps, and equipped with the thermal energy storage (TES) that provides heat flexibility. The temporal coupling introduced by building temperature dynamics and TES energy level dynamics, together with the uncertainties of real-time electricity price and outdoor temperature, pose the optimal energy management problem a computationally challenging one. Hence, based on the Lyapunov optimization method, an online algorithm is proposed to minimize the long-term time average cost of heating system. Our algorithm can ensure the building temperature constraints and TES energy level constraints are satisfied all the time. The maximum optimality gap of our algorithm is also rigorously derived. Simulation results show that this algorithm can achieve user comfort and reduce energy cost effectively.

关键词： Costs Uncertainty Simulation Heuristic algorithms Buildings Optimization methods Energy management

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Motion Planning for Nonlinear Robotic System based on ADMM and Convex Feasible Set Algorithm

Motion Planning for Nonlinear Robotic System based on ADMM a...

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IEEE Conference on Decision and Control

作者： Ruishuang Chen Zaiyue Yang Jie Cheng Zhihui Liang Department of Mechanical and Energy Engineering and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In the motion planning of robot, nonlinear dynamic constraints and obstacle avoidance constraints make the problem highly non-convex and difficult to solve. In this paper, a general algorithmic framework for trajectory optimization is proposed based on alternating direction method of multipliers (ADMM) and convex feasible set algorithm (CFS). The ADMM framework decomposes the original problem into an optimal control sub-problem with only dynamic constraints and a subproblem with only obstacle avoidance constraints, while the sub-problem with obstacle avoidance constraints is solved via CFS based on the geometric properties of the feasible region. The convergence of proposed algorithm is proven, and the simulation results in different cases verify the feasibility and effectiveness of proposed algorithm.

关键词： Heuristic algorithms Simulation Dynamics Convex functions Planning Nonlinear dynamical systems Collision avoidance

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Artificial Neural Networks for the Forward Kinematics of a SCARA Manipulator: A Comparative Study with Two Datasets

Artificial Neural Networks for the Forward Kinematics of a S...

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Emerging Technologies for Sustainability and Intelligent systems (ICETSIS), ASU International Conference in

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

In the field of robotics and automation, the precise determination of manipulator positions is a fundamental aspect with widespread applications. Despite the significance of analytical methods, solving the Forward Kinematics (FK) for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can be complex and computationally intensive. Our research introduces a method to address the FK problem for the four degrees of freedom (4-DoF) SCARA manipulator through Artificial Neural Networks (ANNs). We utilize two different generated datasets, one with a fixed step size and another one based on a sinusoidal signal, for training the ANNs. This choice of datasets allows us to assess the ANN s' generalization capabilities across a range of operating conditions. Thereafter, the study is planned to investigate the ANN's performance while employing three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a comprehensive comparison of the performance of different ANN models, different training algorithms, and the two adopted generated datasets, this study provides valuable insights into selecting the most optimal training configurations for the SCARA robot's FK solutions. We employ the Mean Squared Error (MSE) and the error histogram as two performance metrics to assess the accuracy of various ANNs. The results reveal that optimal MSE outcomes were obtained when utilizing a balanced architecture with three hidden layers in the analysis of the sinusoidal-signal-based datasets.

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechtronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ($\sim 91 \%$) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ($\sim 77.7 \%$). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion.

关键词： Training Support vector machines Radio frequency Musculoskeletal system Logistic regression Machine learning algorithms Nearest neighbor methods Sampling methods Random forests Testing

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Cooperative Motion Control Using Hybrid Acoustic-Optical Communication Networks ⁎

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IFAC-PapersOnLine 2021年第16期54卷 232-237页

作者： Rodrigo Rego Nguyen Hung Antonio Pascoal Laboratory of Robotics and Systems in Engineering and Science(LARSyS) ISR/IST University of Lisbon Lisbon Portugal

There is currently widespread interest in the development of groups of autonomous underwater vehicles for ocean exploration. This calls for the deployment of robots that can act in cooperation by exchanging data over communication networks. For this purpose, acoustic networks have been so far the choice par excellence. However, for operations involving vehicles operating at close range, there is currently a flurry of activity on the use of optical-based communication systems, capable of higher transmission rates. As a result, the goal of this work is to make optical communications viable and to use both communication technologies during underwater cooperative missions. Motivated by this goal, the first part of this work is dedicated to design a coordination controller with an event-based communication strategy for the coordination of the vehicles over the acoustic network. The second part of this work proposes an algorithm to make optical communications viable, which ultimately boils down to achieving optical beam alignment between a pair of cooperative agents. The proposed algorithm is illustrated by simulation results that show optical beam alignment being reached for a pair of vehicles in a cooperative formation.

关键词： Motion Control Cooperative Path Following Autonomous Underwater Vehicles Acoustic Optical Communication Event-Triggered Communications

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