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Machine Learning Regression models for Real-Time Touchless Interaction Applications

Machine Learning Regression Models for Real-Time Touchless I...

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international conference on Software Engineering and Computer Systems (ICSECS)

作者： Qinyan Gong Hadyan Hafizh Muhammad Ateeq Anwar PP Abdul Majeed Matilda Isaac Bintao Hu School of Internet of Things Xi'an Jiaotong-Liverpool University Suzhou China School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China

Touchless technologies have gained significant popularity, particularly amidst the COVID-19 pandemic, as they addressed concerns related to germ transmission and hygiene during human-device interactions. this study aimed to develop an intuitive and user-friendly touchless system by combining eye gaze and hand gesture methods. Four regression machine learning models, namely Ridge regression, Lasso regression, Linear regression, and Gradient-boosting regressor, were trained and tested using standard metrics such as coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). the results indicated that Ridge regression outperformed the other models, demonstrating a high R2 value of 0.98. Leveraging this model, a simulation was conducted to evaluate the effectiveness of integrating eye gaze and hand gestures in realtime touchless interactions. the simulation demonstrated the successful integration of these modalities in the item selection process, providing users with a seamless and intuitive interaction method. this touchless interaction technology enabled effortless and accurate navigation of screens and facilitated item selections. the promising results highlighted the potential of this technology, presenting exciting opportunities for its integration with actuators. By incorporating actuators, touchless interaction systems could revolutionize various industries, including retail, healthcare, hospitality, and smart home automation.

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Integration of Slip Detection and Grip Force Control in an Autonomous Robot Assembly Task for Space Applications

Integration of Slip Detection and Grip Force Control in an A...

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IEEE international conference on automation Science and Engineering (CASE)

作者： I Ishrath S Barat Gustavo H. Diaz Shreya Santra Kazuya Yoshida Harish J. Palanthandalam-Madapusi Dept. of Mechanical Engineering IITGN Robotics Laboratory Indian Institute of Technology Gandhinagar India Depart. of Aerospace Engineering Graduate School of Engineering Space Robotics Laboratory (SRL) Tohoku University Sendai Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Establishing human habitat using in-situ materials on the Moon or Mars autonomously by robotic systems is of great interest to humans. In such autonomous assembly tasks, it is crucial to integrate a slip detection capability that can detect any slipping in the grip and take corrective actions autonomously. this will enable safe handling of in-situ materials during assembly by not unnecessarily applying excessive gripping force. We construct and evaluate sliding slip detection methods (based on both heuristic and learning methods), using only the normal force recorded by a uni-axial force senor in a robotic gripper particularly from the point of view of autonomous assembly tasks for space applications. We were able to achieve over 75% sensitivity and specificity in slip detection using LSTM (Long Short-Term Memory) based model. We also observe that training the model on simulation data alone yields very robust performance in experiments and real-time prediction, and contrary to our expectation, labeling slip in the incipient phase did not yield better performance. Further, we integrate slip detection and corrective actions with an autonomous assembly task through Robot Operating System. In an experimental demonstration of autonomous assembly task using magnetic blocks, YOLO v8 for object detection and LSTM model for slip detection, our results indicate that we are able to successfully complete with correct detection of slip and corrective actions in 9 out of the 10 demonstrations.

关键词： Training YOLO Force Predictive models Aerospace electronics Real-time systems Data models Labeling Assembly Long short term memory

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MCTS Based Dispatch of Autonomous Vehicles under Operational Constraints for Continuous Transportation

MCTS Based Dispatch of Autonomous Vehicles under Operational...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Milan Tomy Konstantin M. Seiler Andrew J. Hill University of Sydney NSW Australia University of Technology Sydney NSW Australia

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Continuous transportation of material in the mining industry is achieved by the dispatch of autonomous haul-trucks with discrete haulage capacities. Recently, Monte Carlo Tree Search (MCTS) was successfully deployed in tackling challenges of long-run optimality, scalability and adaptability in haul-truck dispatch. Typically, operational constraints imposed on the mine site are satisfied by heuristic controllers or human operators independent of the dispatch planning. this article incorporates operational constraint satisfaction into the dispatch planning by utilising the MCTS based dispatch planner Flow-Achieving Scheduling Tree (FAST). Operational constraint violation and satisfaction are modelled as opportunity costs in the combinatorial optimisation problem of dispatch. Explicit cost formulations are avoided by utilising MCTS generator models to derive opportunity costs. Experimental studies with four types of operational constraints demonstrate the success of utilising opportunity costs for constraint satisfaction, and the effectiveness of integrating constraints into dispatch planning.

关键词： Costs Monte Carlo methods Scalability Transportation Production Linear programming Generators Planning Mining industry Optimization

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Prediction of Aviation Spare Parts Consumption Based on Kepler-NeuralProphet Modeling

Prediction of Aviation Spare Parts Consumption Based on Kepl...

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Computer, Control and robotics (ICCCR), international conference on

作者： Jiaheng Lu Haiyan Zhu Jieru Zheng Ningyun Lu College of Chang Kong Nanjing University of Aeronautics and Astronautics Nanjing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Aviation spare parts are essential resources for the aviation industry, playing a crucial role in ensuring the safety and reliability of aircraft flights. Nevertheless, because of their high cost and specific requirements, it is necessary to develop precise procurement plans. this paper addresses the challenges associated with poor prediction accuracy and limited explanatory capability of current methods when dealing with small-sample aviation spare parts data. It introduces a small-sample aviation spare parts consumption prediction method based on the Kepler-N euralProphet model. the NeuralProphet model has a strong combinatorial interpretation capability, which allows it to maintain interpretability while achieving accurate predictions. It utilizes trend, seasonal, holiday, and autoregressive terms to extract the features of time series data related to aviation spare parts consumption. In order to enhance the feature extraction capability of NeuralProphet model, the hyperparameters of NeuralProphet model were optimized using improved Kepler algorithm, which is based on the optimization of the Logistic-Tent-Cosine chaotic operator. In the empirical tests, the Kepler-NeuralProphet model yielded MAE, MAPE, and RMSE results of 3.31, 3.39%, and 5.59, respectively, outperforming the NeuralProphet, Prophet, and NARX models.

关键词： Machine learning algorithms Accuracy Time series analysis Machine learning Predictive models Prediction algorithms Feature extraction

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Hybridizing Long Short-Term Memory Network and Inverse Kinematics for Human Manipulation Prediction in Smart Manufacturing

Hybridizing Long Short-Term Memory Network and Inverse Kinem...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Jiarong Yao Chongshan He Kaixu Li Rong Su Keck-Voon Ling School of Electrical and Electronic Engineering Nanyang Technological University Singapore Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Human-Robot Collaboration (HRC) is essential for enhancing productivity and flexibility in smart manufacturing, which poses requirements on accurately predicting the future movements of human operators, especially the trajectories of their upper limbs. However, existing model-based studies on human manipulation prediction lacks consideration of stochasticity and variability while the emerging deep learning-based methods are demanding on data size, which yet makes real-time deployment challenging. therefore, combining the advantages of both model-based and deep learning-based methods, a method for predicting human arm motion, specifically, the position of a worker's wrist in less than 0.5 second, is proposed by hybridizing a Long Short-Term Memory (LSTM) network with an Inverse Kinematics (IK) model. Using historical coordinate sequences of the wrist joint in three-dimensional space in the past multiple frames as input, a neural network is trained to output the predicted coordinates of the wrist joint for the next frame. then IK (Inverse Kinematics) is used to calculate the arm's motion trajectory based on the predicted wrist coordinates. As the predicted wrist coordinates are sequentially used as the input for the next prediction cycle, the prediction is realized over a sliding time window. Evaluation was conducted using both proprietary and open datasets, results demonstrated that our LSTM-IK method achieved high prediction accuracy, with an average distance error of approximately 5 cm, and can adapt to various task scenarios and individual differences. Additionally, comparison with ground truth illustrated the model's ability to handle complex motion patterns, even with partial occlusions or rapid movements.

关键词： Wrist Learning systems Robot kinematics Kinematics Predictive models Manipulators Real-time systems Trajectory Long short term memory Smart manufacturing

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Automatic Robot Path Planning for Active Visual Inspection on Free-Form Surfaces

Automatic Robot Path Planning for Active Visual Inspection o...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Osama Tasneem Roel Pieters Cognitive Robotics Group Unit of Automation Technology and Mechanical Engineering Tampere University Tampere Finland

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Visual inspection is a crucial yet time-consuming task across various industries. Numerous established methods employ machine learning in inspection tasks, necessitating specific training data that includes predefined inspection poses and training images essential for the training of models. the acquisition of such data and their integration into an inspection framework is challenging due to the variety in objects and scenes involved and due to additional bottlenecks caused by the manual collection of training data by humans, thereby hindering the automation of visual inspection across diverse domains. this work proposes a solution for automatic path planning using a single depth camera mounted on a robot manipulator. Point clouds obtained from the depth images are processed and filtered to extract object profiles and transformed to inspection target paths for the robot end-effector. the approach relies on the geometry of the object and generates an inspection path that follows the shape normal to the surface. Depending on the object size and shape, inspection paths can be defined as single or multi-path plans. Results are demonstrated in both simulated and real-world environments, yielding promising inspection paths for objects with varying sizes and shapes. Code and video are open-source available at: https://***/CuriousLad1000/Auto-Path-Planner

关键词： Point cloud compression Training Visualization Shape Robot vision systems Training data Inspection Cameras Path planning Hardware

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Servo Drive with PWM Controller Simulation

Servo Drive with PWM Controller Simulation

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Vladislav D. Popov Yuriy V. Shornikov Dmitry N. Dostovalov Department of Automated Control Systems Novosibirsk State Technical University Novosibirsk Russia Department of Automated Control Systems Novosibirsk State Technical University Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences Novosibirsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

the simulating of a servo drive with symmetrical pulse width controlling is implemented by the ISMA platform. this task is classified as an event-continuous or hybrid system. Since the dynamic processes can be event-driven, the finite-state machine notation by D. Harel (statechart as formalism) is applied to build a mathematical model for the problem. the statechart with three states complying the Cauchy problem is designed, predicates for directed transitions, as well as states equations are defined. the original LISMA language, combining both block and textual coding, which allows to form compact models that correspond bijectively to statechart formalism used for event-driven continuous systems, is applied. A fragment of the LISMA language grammar for determining the states and transitions of hybrid systems is presented. the designed ISMA software is resolved by original explicit and semi-explicit numerical methods with the asymptotic algorithm for event detection, included to the ISMA library. Computer experiments demonstrate good results with sufficient accuracy.

关键词： Analytical models Computational modeling Software algorithms Pulse width modulation Mathematical models Software Libraries

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Motion Error Analysis of High-speed Parallel Robots

Motion Error Analysis of High-speed Parallel Robots

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robotics, Control and automation (ICRCA), international conference on

作者： Liangliang Zhu Sergey S. Gavriushin Jingzhong Zheng Guannan Chi Depart. Automated Production Computer Systems Bauman Moscow State Technical University Moscow Russian Depart. Instrumentation Technologies Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

To enhance the operational speed of robots and minimize energy expenditure, a majority of Delta parallel robots employed in the industry have a lightweight design. At high operating speeds, the lightweight construction of Delta robots often causes elastic deformation and vibration. Traditional analysis and control methods for rigid robots do not ensure positioning accuracy of the Delta robot. To address this problem, in this paper we have chosen the WSC-600DJ Delta robot as the subject of our research. Our focus is on analyzing the robot's rigid-flexible coupling dynamics. Firstly, the active and passive arms of the Delta robot are made flexible using Hypermesh software. And then, the rigid-flexible coupled dynamics model of the Delta robot is established in Adams program. Finally, the end effector trajectory is planned in MATLAB using the modified sinusoidal correcting keystone acceleration curve. the driving angle of each active arm is determined via inverse kinematics solutions, which then is imported into Adams for simulation. By comparing the results of modeling the rigid-flexible coupled dynamics of the Delta robot with the theoretical trajectories of the rigid model, we are able to estimate the positioning errors of the robot's actuators caused by the elastic deformation. the results of this study establish a foundation for designing a high-accuracy control system for this particular robot model.

关键词： Parallel robots Service robots Deformation Dynamics Mathematical models Software Trajectory

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RBF Neural Networks Model-Free Feedforward Control of Piezoelectric Actuator

RBF Neural Networks Model-Free Feedforward Control of Piezoe...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Tao He Yinbing Wang Zhicheng Lin Kai Guo Duo Li Tao Huang College of Mechanical and Vehicle Engineering Chongqing University Chongqing China Department of Mechanical Engineering Shandong University Jinan China School of Mechanical Engineering Shanghai Jiaotong University Shanghai China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

Piezoelectric actuator is widely utilized in ultra-precision position fields. However, it is challenged by difficulty modeling dynamic properties, such as hysteresis, creep, vibration, etc. therefore, this paper presents a model-free feedforward control strategy of Radial Basis Functions Neural Network (RBFNN) for piezoelectric actuator. RBFNN is used to estimate the inverse dynamic model of the piezoelectric actuator, and avoiding the difficult modeling and impossible to invert problems. this feedforward controller has model-free advantage that it does not depend on the specific physics-based model and is only data-driven by references and control signals. the parameters of RBFNN are training by offline methods and online learning strategy especially. the offline learning method can compensate the trained trajectory with high precision, but for the untrained trajectory, the compensation accuracy is insufficient. For unknown target trajectories, the online learning strategy is proposed, which utilizes current data for parameter iteration and updating to improve control accuracy. Finally, simulation verify that the proposed model-free feedforward control strategy is effective. Especially the online learning strategy has the adaptive ability and generalization ability for unknown trajectory targets.

关键词： Vibrations Training Adaptation models Accuracy Creep Piezoelectric actuators Trajectory

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Embodied Runtime Monitoring of Learning-enabled Robot Perception Components

Embodied Runtime Monitoring of Learning-enabled Robot Percep...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Deebul Nair Sathwik Panchangam Miguel A. Olivares-Mendez Nico Hochgeschwender Institute for Artificial Intelligence and Autonomous Systems Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Space Robotics Research Group (SpaceR) Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg Department of Mathematics and Computer Science University of Bremen Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Monitoring during runtime, the estimated uncertainty from learning-enabled robot perception components, is a vital tool for assuring and evaluating model correctness. By utilizing different uncertainty estimation methods, monitors exploit the perceived sensor data and predictions generated by the perception model to make informed decisions, such as in safety-critical situations. In robotics, informed decisions should be aligned with embodied information regarding space and time. We argue and show that runtime monitors of learning-enabled components can be made more robust by including the embodiment information of the robot. However, it remains challenging to build uncertainty-based runtime monitors with embodiment information using only real-world datasets, which are tedious to collect. To this end, we propose a solution that leverages simulation-based datasets to characterize expected uncertainty estimates with robot embodiment. By constructing a nominal runtime monitor model based on simulated datasets, we establish a reference for the expected uncertainty along with the embodiment information that can be subsequently used to monitor real-world deployed perception components. We demonstrate the zero-shot sim-to-real transfer of our approach on a Kinova Gen3 manipulator picking task, for which the runtime monitor reliability model was trained using simulation-based data containing embodiment information of distance from the object being selected.

关键词： Runtime Uncertainty Computational modeling Estimation Predictive models Robot sensing systems Manipulators Reliability engineering Data models Monitoring

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