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Model-Free Dynamic Control of Five-bar Mechanism via Subchain Assembly and Reinforcement Learning 12

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

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

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

ISBN: (纸本)9798331529734

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. © 2024 IEEE.

关键词： Reinforcement learning

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From Bricks to Bots: Automated Collision-Aware Sequence Planning for LEGO Reconstruction with a Two-Finger Gripper 10

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

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

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

ISBN: (纸本)9798331509972

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. © 2024 IEEE.

关键词： Grippers

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Autonomous Rearrangement Planning Using Object Similarity Learning 12

Autonomous Rearrangement Planning Using Object Similarity Le...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

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

ISBN: (纸本)9798331529734

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. © 2024 IEEE.

关键词： robotic assembly

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JoTyMo: Joint Type and Movement Detection from RGB Images of before and after Interacting with Articulated Objects 11

JoTyMo: Joint Type and Movement Detection from RGB Images of...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Forootan, Ehsan Ghasemi, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Moshiri, Behzad University of Tehran Human and Robot Interaction Laboratory Electrical and Computer Engineering Department Iran

ISBN: (纸本)9798350308105

This paper presents a CNN-based network architecture aimed to classify and detect joint types within Articulated objects, specifically the Push-P joints, P-joints, R-joints and objects lacking joints. The movement modeling of the detected objects is explored by processing pre and post interaction RGB images within the SAPIEN PartNet-Mobility dataset. In order to achieve this, an architecture is proposed to leverage consecutive CNN encoders based on the VGG architecture, in order to classify joints based on pre and post-interaction images. Additionally, in order to detect the effect-point and movement vector, a convolutional encoder is applied for each joint type. Moreover, extensive evaluation is made to showcase the results, achieving 96% accuracy in joint classification and 94% accuracy in regression on the considered dataset. Elaborate details regarding network architectures, training procedures, and testing methodologies are available at source code repository. © 2023 IEEE.

关键词： Network architecture

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2D Skeleton-Based Keypoint Generation Method for Grasping Objects with Roughly Uniform Height Variation 11

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

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： robotics

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Autonomous Unmanned Aerial Vehicle Indoor Navigation Based on Deep Learning Approaches 11

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based o...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Ghasemi, Hamed Balazadeh, Elnaz Malekpour, Shayan Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran University of Tehran Human and Robot Interaction Laboratory Tehran Iran University of Tehran School of Electrical and Computer Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

This article presents two approaches based on deep learning for achieving autonomous navigation and subsequently, conducts an in-depth comparative analysis to elaborate their respective performance. Both methods leverage neural networks to facilitate navigation. The first approach employs a deep convolutional neural network to identify suitable areas for forward movement, while the second method utilizes the drone's front camera to capture images, which are then processed by a generative network to generate corresponding depth images. Importantly, both approaches rely solely on an RGB camera, resulting in cost savings for the robot and reduced energy consumption. The training data used for classifying collision-free and appropriate paths were gathered from simulated environments closely resembling real indoor settings. The proposed model calculates the probability associated with each segment of the input image, enabling the selection of the most viable area for drone flight. Incorporating generative networks as a depth estimation module enhances flight accuracy, making navigation in uncharted environments more straightforward. The accuracy of depth estimation was evaluated using standard criteria, achieving an accuracy rate of 84%. This result underscores the effectiveness of the proposed method. © 2023 IEEE.

关键词： Drones

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Autonomous robotic Assembly and Sequence Planning Based on YOLOv8 11

Autonomous Robotic Assembly and Sequence Planning Based on Y...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Babi, Zeynab Ezzati Khomami, Navid Asadi Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran School of Mechanical Engineering Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798350308105

This study contributes to autonomous assembly by introducing a robotic building system designed to observe its workspace, plan building sequences, determine appropriate grasps and manipulator motions, and operate hardware in real-time. This study involves the creation of a wooden brick dataset and employs a YOLOv8 deep learning model for object segmentation in two implemented real-time algorithms, namely, Desired Construction Object Detection (DCOD) and robot Current Frame Object Detection (RCFOD). The DCOD algorithm is designed for the desired structure, while the RCFOD algorithm is utilized for detecting objects within the operational environment of the robot. By comparing the outputs of these two algorithms, sequences of objects for assembly are formulated, and the appropriate grasp positions are determined. Subsequently, the robot's motion planning for both grasping and construction tasks is executed. Due to the limitations of using a single RGB gripper-mounted camera, various issues, such as failing to recognize items with the same cross-section but varying heights, have been addressed in this study. The results of this study indicate the effective learning of the YOLOv8 model, as shown by a loss value below 0.2 and an accuracy over 90% for 17 out of the total 18 classes. Practical implementation is done by a common industrial Delta parallel robot, a 2-finger gripper, and a simple RGB camera. Overview of practical implementation system is shown in Fig. 1. The proposed robotic assembly method demonstrates its effectiveness in autonomously constructing structures utilizing a reduced and cost-efficient configuration. © 2023 IEEE.

关键词： Object detection

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HUGGA: human-like Grasp Generation with Gripper's Approach State Using Deep Learning 11

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

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

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

ISBN: (纸本)9798350308105

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

关键词： Grippers

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On Graph Reduction in Consensus Control of Multi-agent Systems: A Preliminary Study

On Graph Reduction in Consensus Control of Multi-agent Sys...

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27th RoboCup International Symposium, 2024

作者： Setayeshi, Amirali Babi, Zeynab Ezzati Saadati, Ali Sadeghnejad, Soroush Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Tehran Iran

ISBN: (纸本)9783031858581

Since the use of multi-agent systems has significantly increased over the past few decades, it is crucial to study the methods used in this area and take implementation-related practical issues into consideration. For instance, in the formulation of the consensus problem, the communication between the agents is depicted using a graph, where each edge represents a communication link between the agents. In practical terms, there will be significant operational challenges and costs associated with implementing each of these communication channels in various forms. Therefore, in this study, the graph reduction problem has been presented as an optimization problem, accompanied by six distinct cost functions. The objective is to reduce the size of a graph without compromising its fundamental characteristics. Hence, the cost functions provided are formulated to minimize the number of edges in the resulting graph while ensuring that the consensus error remains bounded. Ultimately, simulations were performed to demonstrate the effectiveness of the proposed approach. The outcomes derived from the specified set of cost functions were then compared to several conventional topologies in multi-agent systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost functions

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Dynamic Modeling of Double Segment Redundant Gough-Stewart Hybrid Manipulator based on the Principle of Virtual Work 12

Dynamic Modeling of Double Segment Redundant Gough-Stewart H...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Ardakani, Alireza Kamali Akbari, Hossein Namazian, Parsa Masouleh, Mehdi Tale Bahrami, Arash University of Tehran Human and Robot Interaction Laboratory School of Mechanical Engineering Tehran Iran University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331529734

This paper presents the dynamic modeling and analysis of a hyper-redundant manipulator with two Gough-Stewart platforms. Initially, the kinematic model of the manipulator is reviewed and used, since it acts as a base component for the subsequent dynamic modeling. Then, the virtual work approach is introduced to establish a foundation for the dynamic modeling of a single Gough-Stewart platform. This modeling for a single segment is used to form the equation of motion for both segments. However, the external forces applied to the lower Gough-Stewart robot are different from the upper one, since the force and masses in the upper platform act on the lower one. By considering these effects on the lower platform, the dynamic equations for the whole have been formed and presented. The accuracy of the proposed model is validated through a detailed comparison with simulations performed using a Simscape environment. Notable challenges in aligning the results between the model and simulations are discussed, highlighting the complexities involved in ensuring consistency across different platforms. The comparisons show an average error of 1.537 percent per actuator. © 2024 IEEE.

关键词： Digital elevation model

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