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Experimental Study on Reducing the Oscillations of a Cable-Suspended Parallel robot for Video Capturing Purposes using Simulated Annealing and Path Planning 7

Experimental Study on Reducing the Oscillations of a Cable-S...

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7th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2021

作者： Ghanatian, Mohammad Yazdi, Mohammad Reza Hairi Masouleh, Mehdi Tale School of Mechanical Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9781665409384

This paper aims to smoothen the movements of an under-constrained cable-suspended parallel robot which carries a camera for video capturing purposes, especially for video capturing of football games. This goal is achieved by means of an accurate while simple PID controller optimized by the Simulated Annealing algorithm and implemented on the joint space. Moreover, a planning strategy is considered for the joint space trajectory of the robot which guarantees the zero jerk, acceleration, and velocity at the start and the end of motions. On this regard, a septic function for each joint of the robot is considered and the corresponding boundary conditions are applied to the function to make the end-effector movements less oscillatory. This method is implemented and tested on an experimental setup while the end-effector oscillation data is recorded using an IMU sensor attached to the end-effector of the robot. Applying frequency analysis on the oscillatory data of the end-effector reveals that this simple method, on average, resulted in a 33.8% reduction in the average amplitude of the end-effector oscillations. Moreover, the maximum joint space error was decreased by 76.68% when using septic joint profile compared to the ordinary linear Cartesian trajectory planning approach. Upon applying the proposed strategy, the error of the controller has been reduced by 92.26% with respect to the previous research performed on this experimental setup. Without requiring any knowledge on the dynamic model of the robot or the natural frequencies of the end-effector or using any complex controller, this method significantly increased the smoothness and accuracy of the robot movements. The proposed method can be regarded as a definitive asset when this robot is used for video capturing purposes. © 2021 IEEE.

关键词： Simulated annealing

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Experimental Study on Automatically Assembling Custom Catering Packages With a 3-DOF Delta robot Using Deep Learning Methods

Experimental Study on Automatically Assembling Custom Cateri...

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Iranian Conference on Electrical engineering (ICEE)

作者： Reihaneh Yourdkhani Arash Tavoosian Navid Asadi Khomami Mehdi Tale Masouleh Electrical and Computer Engineering Department Human and Robot Interaction Laboratory University of Tehran Mechanical Engineering Department University of Tehran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper introduces a pioneering experimental study on the automated packing of a catering package using a two-fingered gripper affixed to a 3-degree-of-freedom Delta parallel robot. A distinctive contribution lies in the application of a deep learning approach to tackle this challenge. A custom dataset, comprising 1,500 images, is meticulously curated for this endeavor, representing a noteworthy initiative as the first dataset focusing on Persian-manufactured products. The study employs the YOLOV5 model for object detection, followed by segmentation using the FastSAM model. Subsequently, rotation angle calculation is facilitated with segmentation masks, and a rotated rectangle encapsulating the object is generated. This rectangle forms the basis for calculating two grasp points using a novel geometrical approach involving eigenvectors. An extensive experimental study validates the proposed model, where all pertinent information is seamlessly transmitted to the 3-DOF Delta parallel robot. The proposed algorithm ensures real-time detection, calibration, and the fully autonomous packing process of a catering package, boasting an impressive over 80% success rate in automatic grasping. This study marks a significant stride in advancing the capabilities of robotic systems for practical applications in packaging automation.

关键词： Deep learning YOLO Parallel robots Image segmentation 3-DOF Focusing Grasping

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Developing a Reinforcement Learning-Based Actor-Critic Neural Network Controller for General Form of Serial Chain robots with Torque Constraints

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SSRN 2024年

作者： Bahrami, Vahid Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

This paper uses reinforcement learning techniques to introduce a novel approach to the controller design for a wide range of serial chain robots and cable-driven serial chain robots. The method addresses the regulation problem by designing a regulator and steady-state torques without requiring knowledge of the Coriolis vector, a complex part of the robot's dynamic model. The approach relies on Denavit-Hartenberg parameters to determine regulator and desired steady-state torques, using an actor-critic neural network for adaptation and a multi-layer perceptron neural network for desired steady-state torque. Stability is ensured through Lyapunov functions, with only the mass matrix and gravity vector required. The proposed control scheme offers adaptability, robustness, scalability, and optimization features compared to traditional controllers. Simulations on a 4-degrees-of-freedom cable-driven serial chain robot validate the proposed control method's effectiveness. A comparative benchmark is performed, demonstrating that the reinforcement learning controller outperforms other controllers in terms of regulation error, reducing it by at least 17.14\% compared to deep deterministic policy gradient and robust integral of the sign of the error methods. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Deep Learning-Based Imitation of human Actions for Autonomous Pick-and-Place Tasks

Deep Learning-Based Imitation of Human Actions for Autonomou...

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Iranian Conference on Electrical engineering (ICEE)

作者： Anushe Saadati Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In robotics, attempts have been made to imitate movements through learning from demonstrations. There are two types of demonstration-based learning. Expert-based learning is expensive and time-consuming since it necessitates a paired observation-action procedure. Conversely, visual-based learning is more practical to collect and makes robots more adaptable to many kinds of tasks. But this problem still needs to be answered completely, and it's still a difficult assignment. This paper presents a comprehensive approach to replicating human actions in robots for object manipulation. As the main contribution of this paper, This research employs a deep learning methodology to allow robots to imitate human actions in grasping objects, utilizing human movement as a reference. A real dataset with 718 images and 142 videos has been collected in this paper. then The model is trained for object detection and object segmentation. To imitate human actions, a video of a person completing a task is recorded first. Following the subject's movements in the video allows for the extraction of the motion path. The robot then proceeds along the path that was extracted. The DPR impressively demonstrates the practical application, showcasing its capacity to imitate and execute human actions.

关键词： Deep learning Accuracy Tracking Video sequences Object detection Grasping Real-time systems

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From Bricks to Bots: Automated Collision-Aware Sequence Planning for LEGO Reconstruction with a Two-Finger Gripper

From Bricks to Bots: Automated Collision-Aware Sequence Plan...

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International Conference on Control, Instrumentation, and Automation

作者： Arman Barghi Navid Pasiar Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

An automated, vision-guided assembly method is proposed for reconstructing LEGO arrangements using a 3 DOF Delta Parallel robot with a two-finger gripper. Assembly sequence planning, an NP-complete problem, focuses on determining the optimal sequence of bricks for constructing a target structure. The complexity increases when robotic con-straints-such as avoiding collisions between the gripper and the partially constructed structure-are integrated into the planning. To address this, the Arrangement Planning Process is introduced, combining target arrangement identification with collision-aware sequence planning. This process includes precise detection techniques that merge instance segmentation with stud detection. Notable innovations include an assembly-by-disassembly algorithm for efficient planning and experimental techniques such as the Safe Approach and the Wiggling Technique for accurate brick placement. By implementing a homing method, positioning accuracy improved approximately twofold compared to previous work. The results demonstrate the feasibility and precision of the proposed method in automated LEGO reconstruction.

关键词： Instance segmentation Parallel robots Technological innovation Accuracy Three-dimensional displays Service robots Robustness Planning Grippers Assembly

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Autonomous Rearrangement Planning Using Object Similarity Learning

Autonomous Rearrangement Planning Using Object Similarity Le...

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

作者： Parmida Jabari Zeynab Ezzati Babi Hamed Ghasemi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This study investigates the integration of artificial intelligence in robotics assembly systems, focusing on enhancing flexibility and adaptability in dynamic environments where the robot might not be given the precise objects of the intended construction. To this end, a developed approach is presented which is based on object detection, similarity assessment, and sequence planning for autonomous construction tasks. The system utilizes a YOLOv8 model for object detection and a Siamese network architecture, which facilitates object replacement. It processes two input images, one representing the target structure and another showing the available objects, extracting attributes such as bounding boxes, centers, and orientations. An exact object-matching step is performed before a two-stage similarity assessment. The two-stage similarity assessment prioritizes single-object matches and employs a multi-object similarity network only when necessary. This multi-stage approach is designed to maintain high similarity to the intended design while minimizing possible object shortcomings. During the construction phase, motion planning is performed using a 3-degrees-of-freedom Delta parallel robot, and a 2-finger gripper is utilized for robotics grasping. The results of this study, conducted on wooden bricks of varying shapes and sizes, demonstrate the effectiveness of the YOLOv8 model for object detection, achieving over 80% accuracy across 18 classes with a classification loss of approximately 0.25. Additionally, the Siamese network performed robustly, with a loss of less than 0.01 for both single and multiple object comparisons.

关键词： robotic assembly Parallel robots Automation Autonomous systems robot vision systems Warehousing Object detection Cameras Planning Grippers

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Designing and Developing a 6-DOF Calibration Setup Based on the Gough-Stewart Platform Equipped by Potentiometer Sensors

Designing and Developing a 6-DOF Calibration Setup Based on ...

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

作者： Mohammad Behdad Mohammadi Damnab Mehdi Tale Masouleh Mohammad Reza Haeri Yazdi Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

This paper investigates the design and development of a novel 6 Degree-Of-Freedom parallel mechanism displacement sensor based on the Gough-Stewart platform with potentiometer sensors for calibration purposes. This platform is equipped by six potentiometer sensors which connect the fixed base to a moving plate, called the End-Effector (EE), in order to measure the displacement and rotation of its EE along the three Cartesian axes X, Y, and Z. Since the precision of the potentiometer is considerably high, thus this device can be used as a calibration setup to the end of calibrating other robots. The main objective of this paper consists in the design of a reliable calibration setup by investigating different architectures of the Gough-Stewart platform, workspace calculation, the manufacturing process, and the data acquisition circuit. In order to ensure the precision of the proposed setup, first a calibration procedure is carried out on one potentiometer which is done by a laser sensor which has a higher order of precision than the precision of the potentiometer. Then the EE of the setup is attached to a 3-DOF decoupled parallel robot, the so-called Tripteron robot, in order to actuate accurately the setup. Since three independent ball screws are used to actuate the Tripteron robot, thus the latter laser sensor is used separately to measure the displacement of each ball screw. Experimental results reveal that the mean Euclidean distance pose error in the EE of the designed calibration setup is 0.3886 mm, and the max Euclidean distance pose error became 0.6498 mm. Finally, it can be inferred that this setup is highly accurate in measuring displacement and can be attached to other robots to identify their kinematic parameters which is a definite asset in practice.

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Model-Free Dynamic Control of Five-bar Mechanism via Subchain Assembly and Reinforcement Learning

Model-Free Dynamic Control of Five-bar Mechanism via Subchai...

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

作者： Soheil Sedghi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Parallel robots are versatile but challenging to control due to their complex dynamics and the need for precise modeling. This research presents a novel approach to modular control of parallel robots, utilizing reinforcement learning to dynamically assemble and control complex arbitrary systems. By treating each sub chain of a parallel robot as an individual agent, the proposed method offers a flexible and adaptable framework for various control tasks. The study compares the learning performance of different RL algorithms and proposed decision modeling on the tracking control of a five-bar robot with a 55 cm link length, aiming to achieve an error of less than 5 cm in the PyBullet simulator. In single-agent reinforcement learning, three algorithms (DDPG, PPO, and SAC) were employed, with PPO and SAC effectively solving the problem. In multi-agent reinforcement learning, two algorithms (FACMAC and MAPPO) were utilized. While MAPPO successfully learned the problem, FACMAC exhibited difficulties in grasping the problem. The results obtained from comparing the proposed RL algorithms indicated that single-agent RL learns the control problem better and faster than multi-agent reinforcement learning. In short, the paper focuses on formulating an RL solution which can effectively control parallel robots and can potentially be used for pre-training subchains.

关键词： Training Parallel robots Adaptation models Sensitivity Mechatronics Heuristic algorithms Transfer learning Neural networks Training data Reinforcement learning

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HUGGA: human-like Grasp Generation with Gripper’s Approach State Using Deep Learning

HUGGA: Human-like Grasp Generation with Gripper’s Approach ...

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

作者： Elnaz Balazadeh Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this study, an attempt has been made to develop a new method for producing a successful grasp based on human behavior, by considering the gripper’s approach state in the final grasp of an object. The dataset collected for the purpose of this paper consists of 14 different daily usage objects with different orientations (In total 2526 images) which a human approaches and grasps them. Data collection has been done based on a protocol shown in TableI to cover all achievable states. From Fig1, it can be seen that the dataset includes one image from a single object without the user’s hand. The second image contains the human hand approach and its configuration regarding the object which is subject to be grasped and the third image represents the object which is grasped by the human hand and thus evidently contains both object and human hand grasp configuration. It is important to acknowledge that providing a static hand image does not suffice to facilitate the acquisition of human-like grasping capabilities by robot grippers, so, a mapping from hand fingers to a two-finger gripper should be implemented to fill this gap. In the collected dataset, all grasps are performed by the index finger and thumb to make it possible to map the coordinates of each finger on a gripper finger. Using MediaPipe, which operates a single shot detection network, with only an RGB image containing a human hand the index finger and thumb landmarks were extracted. Finally, a detailed human approach and grasp information were available, and with the collected data, a two-input and one-output deep neural network was trained. The designed model produced a grasp with a human pattern by receiving only the RGB image of the object and the approach vector to the object. Ultimately, through testing and evaluation based on existing criteria, the trained network successfully produced a grasp with 61% accuracy in grasp rectangle overlap, 97% in grasp angle and overall 60.5% met two established grasp crite

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2D Skeleton-Based Keypoint Generation Method for Grasping Objects with Roughly Uniform Height Variation

2D Skeleton-Based Keypoint Generation Method for Grasping Ob...

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

作者： Ali Sabzejou Mehdi Tale Masouleh Ahmad Kalhor School of Electrical and Computer Engineering Department Human and Robot Interaction Laboratory University of Tehran

robotic grasping poses a fundamental challenge in robotics, particularly when dealing with unknown objects. This skill has always been a focal point in robotics research due to its fundamental importance and inherent complexity. Recent advances utilize state-of-the-art learning techniques, including deep learning and deep reinforcement learning, presenting their unique set of challenges. Training these models requires extensive data and computational resources, with the generalization to unknown objects presenting a significant obstacle for robots. This paper presents a comprehensive approach to unknown object robotic grasping, with a specific focus on top-down grasping actions. The process encompasses image preprocessing, the application of the Straight Skeleton (StSkel) method, and the systematic generation of grasp keypoints when applied to a selection of objects from the Dex-Net dataset. During the evaluation, the analysis incorporated crucial metrics, including the number of detected grasp keypoints for each object, the count of successfully generated grasp pairs, the overall success rate, and the success rate when considering the top 5 ranked grasp pairs. One of the notable strengths of this approach lies in its adaptability to a wide array of objects, ranging from simple shapes to complex ones. The proposed approach in this paper paves the way for automatically labeling grasping datasets, providing a valuable asset for developing auto-generating Deep-RL models. The StSkel method effectively captures essential structural information, making it suitable for real-world applications involving diverse objects. The experimental outcomes affirm the robustness and adaptability of the approach, as it most often achieved a high success rate in generating viable pairs of grasping points for most tested objects. Even complex objects yield impressive results, demonstrating the method’s potential for real-world applications in grasping unknown objects.

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