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ASME 2024 International mechanical engineering Congress and Exposition, IMECE 2024

作者： Qadeer, Abdul Akhtar, S. Sohail Abubakar, Abba A. Arif, Abul Fazal M. Mekid, Samir Al-Athel, Khaled S. Mechanical Engineering Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Advanced Materials King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia

ISBN: (纸本)9780791888605

Laser powder bed fusion (LPBF) is a pivotal additive manufacturing process characterized by selective melting and solidification of powder layers to form complex *** microstructure of the final part plays a crucial role in determining its properties, which are heavily influenced by process parameters such as laser power, scanning speed, powder layer thickness, and material *** the influence of process parameters on melt pool geometry is crucial for minimizing defects due to unmelted powder regions and optimizing hatch ***, the microstructure plays a vital role, as process parameters can lead to the formation of columnar grains, promoting anisotropy in the fabricated ***, understanding microstructural characteristics is key to ensuring the reliability of manufactured *** the microstructure and melt pool geometry in laser powder bed fusion (LPBF) processes via finite element modeling is computationally demanding and *** mitigate these challenges, we propose the development of a machine learning model capable of accurately predicting melt pool geometry and microstructure based on process parameter variation, thereby reducing time in the identification of optimal process parameters for further experimental *** study utilizes a physics-based numerical model within ANSYS Additive to predict melt pool geometry and microstructure in the LPBF *** model's accuracy was validated using single-track metal additive manufacturing (AM) experiments extracted from relevant literature.A comprehensive database was compiled by numerical simulation, including melt pool geometry, microstructure, process parameters, and material *** dataset was then employed to train a data-driven model, with process parameters and material IDs serving as inputs and melt pool width, depth, and microstructure images as ***, the images were resized to 100x200 pixels for computationa

关键词： Multilayer neural networks

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Improved Aerodynamic Performance of Ornithopter Aerial Robot Using Spatial Mechanism Compared to Planar Design

Improved Aerodynamic Performance of Ornithopter Aerial Robot...

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

作者： Sina Hallajian S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab K. N. Toosi University of Technology Tehran Iran

Ornithopter’s research can be categorized into two parts. First, aerodynamics analysis of the flying robot including designing the body and choosing the robot’s material based on the analysis. Second, design and analysis of the mechanism which makes the wings move. So far, most of the researches has been focused on the first subject, i. e., aerodynamics analysis; even some books were published on this topic. However, there is little research about the mechanisms while most of the previous works are based on the patterns verified before. This paper will propose the design of a new spatial mechanism. Besides, three common planar mechanisms will be modeled and simulated to analyze and compare to the proposed mechanism in terms of aerodynamics forces for one wing beat cycle. These results reveal that the proposed 3D mechanism works more promising than others.

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Optimizing IMU-Based Locomotion Mode Detection System Using Taguchi Method

Optimizing IMU-Based Locomotion Mode Detection System Using ...

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

作者： S. Hooman Hosseini-Zahraei Sajedeh Mazinani Iman Kardan Alireza Akbarzadeh Dept. of Mechanical Engineering Center of Advanced Rehabilitation and Robotics Research (FUM CARE) Ferdowsi University of Mashhad Mashhad Iran

Automatic detection of the locomotion mode is crucial in many applications, such as designing controller for exoskeleton robots and other assistive devices. Among the available methods and sensors for detecting locomotion type, inertial measurement units (IMU) have found increasing applications, due to their small size, ease of use, needing no special preparation, and portability. This study performs a statistical analysis to optimize the placement of IMU sensors and determine the minimum number of inputs required for the automatic classification of locomotion type. The k-nearest neighbor algorithm (kNN) is chosen as the classifier and a Taguchi U16 orthogonal experimental design is used to evaluate the effect of different factors on the accuracy of the classification. Quaternion data, acceleration magnitude, and gyroscope magnitude from thigh-mounted and shank-mounted IMUs are considered as the input factors in the Taguchi design. A simple cost function is used to incorporate both the classification error and the number of inputs. The implemented cost function includes a weighting term to identify the optimal number of inputs based on the required level of accuracy. When considering equal weights for the classification error and the number of inputs, the Taguchi analysis ranks the quaternion data, acceleration magnitude, and gyroscope magnitude as the 1st, 2nd, and 3rd order of importance, respectively. For our specific application, a weighting value of 0.6 is used to give slightly more emphasis to the accuracy of the locomotion mode detection. The optimal combination reduces the number of inputs from 12 to 6, while maintaining an acceptable accuracy level of 76.4 percent. This is only slightly lower than the 79.5 percent accuracy achieved when all of the 12 inputs are used. Furthermore, results indicated that the shank-mounted IMU had a greater impact on the classifier’s accuracy compared to the thigh-mounted IMU.

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Trajectory Tracking of Robotic Arms Using Learning Modified Transpose Jacobian

Trajectory Tracking of Robotic Arms Using Learning Modified ...

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

作者： Danial Ansari S Ali A Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics & Automated Systems (ARAS) Laboratory K. N. Toosi University of Technology Tehran Iran

In this research, the issue of trajectory tracking in task space of a robotic arm with rotational joints has been solved. For this purpose, a non-model based controller the modified jacobian-transpose is used which it’s performance has been improved by reinforcement learning. Traditionally, the settings of the error thresholds for this controller will require trial and error, but in this algorithm, reinforcement learning is used to adjust these parameters online and according to the conditions. The simulations for the desired trajectory indicate that the improvement of the trajectory tracking achieved by using proposed algorithm. Also, the superiority of this controller in noisy conditions compared to the reference Jacobian transpose controller has been shown.

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Multi-aspect Optimal Sliding Mode Controller for a Mecanum Wheeled Robot

Multi-aspect Optimal Sliding Mode Controller for a Mecanum W...

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

作者： Sohrab Allahyari Hamid Rahmanei S. Ali A. Moosavian Dept. of Mechanical Engineering Center of Excellence in Robotics & Control Advanced Robotics & Automated Systems (ARAS) Laboratory K. N. Toosi University of Technology Tehran

Four-wheel drive Mecanum robots have gained attention due to their ability to move in all directions, which allows them to work in tight and complex environments. For this reason, ensuring precise control of these robots along predefined trajectories is crucial. This work presents a comparative study on the effects of parametric uncertainties, external disturbances, measurement noises, and initial deviations on the trajectory-tracking performance of a 4-wheel drive Mecanum robot. First, the kinematic and kinetic models of the Mecanum robot are introduced. Then, the integral sliding mode control is designed, and proof of stability is provided. In addition, we explore other control strategies, including PID, feedback linearization, sliding mode, and adaptive sliding mode control, that are designed for this robot. Furthermore, the particle swarm optimization algorithm is hired for the controllers with a cost function as the total sum of average error, maximum error, and final error. Optimization has been done on all controllers to compare the results fairly. To appraise the effectiveness of the proposed control optimization methods, simulations are carried out in MATLAB/Simulink. The results reveal that the proposed ISMC method performs best in both defined scenarios compared to other controllers. However, it must be noted that the ISMC controller exhibits limitations when facing significant uncertainties in the dynamic model. This study underscores the effectiveness of our proposed control method and provides valuable insights into the practical implementation controller for the Mecanum robot in real-world applications.

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Dynamics Modeling of Continuum Robots Using Screw Theory for Real-time Control

Dynamics Modeling of Continuum Robots Using Screw Theory for...

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

作者： Saeedeh Shekari S. Ali A. Moosavian Dept of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab K. N. Toosi University of Technology Tehran Iran

Modeling the dynamics of continuum robotic arms is challenging due to the highly nonlinear nature and uncertain and complicated inherent structure. This affects the application of continuum arms in various aspects, such as inverse kinematics, path generation, optimization, and control. This paper deals with the dynamics modeling of continuum robotic arms using screw theory to develop a suitable model with high accuracy, speed, and low computational load to achieve real-time control. By using the representation of the Lie group of screws in the screw theory, which simplifies the mathematics of the problem, it is possible to achieve a real-time integrated representation of the dynamics of the deformation of the body of continuum robotic arms. This provides the possibility of accurately predicting the system behavior even in the presence of external forces acting on the end-effector. The experimentally validated results for circular and infinite motion trajectories show the model effectiveness in enhancing the trajectory tracking performance and ability to estimate configuration shape changes under external loads. The integration of this model in soft robotic arms has the potential to revolutionize their application in a wide range of industries and medical applications.

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Tomato Fruit Maturity Detection Method Based on YOLOV4 and Statistical Color Model 11

Tomato Fruit Maturity Detection Method Based on YOLOV4 and S...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Zhou, Xiaoliang Wang, Pengbo Dai, Guanglin Yan, Jiawen Yang, Zhan School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215123 China

ISBN: (纸本)9781665425278

The ripening information of tomato fruit in greenhouse environment is closely related to production operation. Currently, greenhouse tomato ripening information is mainly carried out by manual inspection. In this paper, we present a new real-Time method of tomato fruit detection and its maturity measurement in natural greenhouse. More than 25,000 tomato fruits were tested in 1005 images by Yolov4 and the identification accuracy was 95%. After construction of single fruit data set, the color proportion analysis of RGB color features was analyzed. Combined with normalization and noise reduction processing of K-means clustering, we proposed the maturity detection method based on the proportion of R component in RGB space. Experiment results of scouting robot showed that the recognition and detection speed is 5-6 frames/s. © 2021 IEEE.

关键词： Greenhouses

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Identification and Localization of Optimal Picking Point for Truss Tomato Based on Mask R-CNN and Depth Threshold Segmentation 11

Identification and Localization of Optimal Picking Point for...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Yan, Jiawen Wang, Pengbo Wang, Tianjian Zhu, Guofeng Zhou, Xiaoliang Yang, Zhan School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215123 China

ISBN: (纸本)9781665425278

Aiming at the problems of variable illumination, different fruit postures and the interference of main stem and fruit stem in natural greenhouse environment, this paper presents a new identification and localization method for picking point of truss tomato based on the combination of Mask R-CNN and depth threshold segmentation. 679 tomato clusters were detected in greenhouse. The results showed that the overall segmentation and detection success rate of Mask R-CNN ripe truss tomatoes reached 80.5%. On the basis of successfully obtaining the optimal picking point area, Mask R-CNN excluded the main stem and background fruit stem after depth threshold processing. The success rate of segmentation and recognition is 100%, and the success rate of fruit stem depth information positioning is 87.3%. The average detection time is 2.34s, which can be used to localize the optimal picking point in greenhouse. © 2021 IEEE.

关键词： Fruits

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Compliance Control and Simulation of a Next-Generation Chewing Robot 42

Compliance Control and Simulation of a Next-Generation Chewi...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Xudong Xu, Weiliang Dhupia, Jaspreet Bronlund, John Han, Jianda The University of Auckland Department of Mechanical & Mechatronics Engineering Auckland New Zealand School of Food and Advanced Technology Massey University Palmerston North New Zealand Reddit Institute Palmerston North New Zealand Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China

ISBN: (纸本)9789887581543

The aim of the development of the chewing robots is to study the biomechanics of chewing process or produce the bolus for further research by in vitro experiments. Some chewing robots are open loop controlled and couldn't behave like a human. However, to make the chewing robots more bin-mimic, force feedback based closed loop control is required. To improve the efficiency of the in-vitro chewing experiments, a next generation chewing robot with cam-follower based multi-chambers was developed previously, which can be used test the effect of different chewing parameters on the chewing performance and food texture changes simultanously. In this paper, modified admittance control based on the force feedback was applied to the chewing robot. At first, to improve food particle size reduction to a distribution which closely matches that produced by human and protect the components of the robot from breakage or impact damage, a spring mass based system, called remote center compliance (RCC), was developed and applied to the robot to control the chewing force. Then, based on the knowledge of human chewing process, power-based algorithm, and modified admittance control method were used as the feedback to adjust the angular velocity of the motor and corresponding chewing velocity of the teeth. The performance of the control model was also simulated, results showed that the model is reliable to control the robot and make it bio-mimic. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Compliance control

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Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

arXiv

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arXiv 2024年

作者： Liu, Junjia Li, Zhuo Yu, Minghao Dong, Zhipeng Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious process. Given the rapid introduction of new humanoid platforms, a cross-embodiment framework that allows generalizable skill transfer is becoming increasingly critical. To address this, we propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for re-training on every new robot platform. The model learns behavior primitives from human demonstrations through adversarial imitation, and the complex robot structures are decomposed into functional components, each trained independently and dynamically coordinated. Task generalization is achieved through a human-object interaction graph, and skills are transferred to different robots via embodimentspecific kinematic motion retargeting and dynamic fine-tuning. Our framework is validated on five humanoid robots with diverse configurations, demonstrating stable loco-manipulation and highlighting its effectiveness in reducing data requirements and increasing the efficiency of skill transfer across platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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