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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jeonghyeon Yoon Junhyun Park Hyojae Park Hakyoon Lee Sangwon Lee Minho Hwang Department of Robotics and Mechatronics Engineering DGIST Daegu Republic of Korea

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon’s working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS.

关键词： Measurement Medical robotics Minimally invasive surgery Accuracy Kinematics End effectors Optimization Intelligent robots

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Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency

arXiv

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arXiv 2025年

作者： Park, Myeongbo An, Chunggil Park, Junhyun Kang, Jonghyun Hwang, Minho Department of Robotics and Mechatronics Engineering DGIST Daegu42988 Korea Republic of

Tendon-sheath mechanisms (TSMs) are widely used in minimally invasive surgical (MIS) applications, but their inherent hysteresis—caused by friction, backlash, and tendon elongation—leads to significant tracking errors. Conventional modeling and compensation methods struggle with these nonlinearities and require extensive parameter tuning. To address this, we propose a vibration-assisted hysteresis compensation approach, where controlled vibrational motion is applied along the tendon’s movement direction to mitigate friction and reduce dead zones. Experimental results demonstrate that the exerted vibration consistently reduces hysteresis across all tested frequencies, decreasing RMSE by up to 23.41% (from 2.2345 mm to 1.7113 mm) and improving correlation, leading to more accurate trajectory tracking. When combined with a Temporal Convolutional Network (TCN)-based compensation model, vibration further enhances performance, achieving an 85.2% reduction in MAE (from 1.334 mm to 0.1969 mm). Without vibration, the TCN-based approach still reduces MAE by 72.3% (from 1.334 mm to 0.370 mm) under the same parameter settings. These findings confirm that vibration effectively mitigates hysteresis, improving trajectory accuracy and enabling more efficient compensation models with fewer trainable parameters. This approach provides a scalable and practical solution for TSM-based robotic applications, particularly in MIS. Copyright © 2025, The Authors. All rights reserved.

关键词： Friction

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Comparative Analysis of Reinforcement Learning Techniques in Simulated robotics Environments 7

Comparative Analysis of Reinforcement Learning Techniques in...

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7th International Conference on Intelligent robotics and Control engineering, IRCE 2024

作者： Yoseph, Abey Tanveer, M. Hassan Voicu, Razvan Cristian Shahid, M. Farrukh Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States National University of Computer and Emerging Sciences Department of Ai and Data Science Karachi Pakistan

ISBN: (纸本)9798350352399

Machine learning is a critical component of artificial intelligence and computer science that uses data and algorithms to emulate the way that humans learn and predict. Reinforcement learning, one of the many forms of machine learning, trains an agent through trial and error, with successful outcomes being rewarded and unsuccessful outcomes being penalized. The implementation of three reinforcement learning algorithms, Qlearning, Deep Q-Learning, and Double Deep Q-Learning, was tested using a series of ROS (Robot Operating System) packages in the Gazebo simulation environment. The Python openai-ros package and Gym libraries were used to input a robot into a configurable environment and test out the performance of a specific reinforcement learning algorithm. The agent's action space was discretized into three specific actions, move forward, turn left, or turn right, that the robot could take. The reward or punishment was given after each step based on if the current action moved the robot towards the goal state or away from it. For each algorithm, the agent was first pre-trained on an obstacle free environment for 200 episodes and then was placed in an environment with two box-shaped obstacles to avoid. The algorithm/neural network parameters from the pre-training environment were loaded prior to the agent being tested in the new environment. The goal state at each episode was randomly generated and the robot's performance was evaluated using the cumulative reward achieved per episode and the number of times it reached the goal. © 2024 IEEE.

关键词： Adversarial machine learning

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Kirigami-inspired continuum soft arm with embedded sensing for non-destructive inspection and sorting

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Science China Materials 2025年第2期68卷 552-560页

作者： Jinsui Xu Boyi Xu Honghao Yue Yifan Lu Zheping Wang Zongquan Deng Fei Yang State Key Laboratory of Robotics and System School of Mechatronics EngineeringHarbin Institute of TechnologyHarbin 150001China Wu Xianming School of Intelligent Engineering South China University of TechnologyGuangzhou 510641China

The sensing capabilities of a soft arm are ofparamount importance to its overall performance as they allow precise control of the soft arm and enhance its interactionwith the surrounding environment. However, the actuationand sensing of a soft arm are not typically integrated into amonolithic structure, which would impede the arm’s movement and restrict its performance and application scope. Toaddress this limitation, this study proposes an innovativemethod for the integrated design of actuator structures andsensing. The proposed method combines the art of kirigamiwith soft robotics technology. In the proposed method, sensorsare embedded in the form of kirigami structures into actuatorsusing laser cutting technology, achieving seamless integrationwith a soft arm. Compared to the traditional amanogawakirigami and fractal-cut kirigami structures, the proposedmiddle-cut kirigami (MCK) structure does not buckle duringstretching and exhibits superior tensile performance. Based onthe MCK structure, an advanced interdigitated capacitivesensor with a high degree of linearity, which can significantlyoutperform traditional kirigami sensors, is developed. Theexperimental results validate the effectiveness of the proposedsoft arm design in actual logistics sorting tasks, demonstratingthat it is capable of accurately sorting objects based on sensorsignals. In addition, the results indicate that the developedcontinuum soft arm and its embedded kirigami sensors havegreat potential in the field of logistics automation *** work provides a promising solution for high-precisionclosed-loop feedback control and environmental interaction ofsoft arms.

关键词： kirigami embedded sensing continuous soft arm non-destructive inspection

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GA-Based Optimization of Control Strategies for a 3R Nonplanar Robotic Manipulator 12

GA-Based Optimization of Control Strategies for a 3R Nonplan...

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

作者： Ahmadpanah, Parham Charehjoo, Taha Ghanbarzadeh, Armin Najafi, Esmaeil K.N. Toosi University of Technology Faculty of Mechanical Engineering Tehran Iran Saxion University of Applied Sciences Smart Mechatronics and Robotics reserach group Enschede Netherlands

ISBN: (纸本)9798331529734

robotics and control are key to automation and industry. In this work, the model of a 3R nonplanar manipulator is simulated in MATLAB. A sinusoidal trajectory was used for the robot's movement, while Gaussian noise was added to the system to simulate real-world application. The impact of the noise was handled using a moving average filter. The effect of four different control systems was evaluated, including Proportional-Integral-Derivative, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System, and Sliding Mode Control, on each joint torque. The parameters for each controller were tuned with a Genetic Algorithm. The results showed that the most accurate trajectory tracking was provided by Sliding Mode Control, especially when noise was present, while the Adaptive Neuro-Fuzzy Inference System proved to be very flexible. The study discusses the applicability of these controllers, their optimization, and their effect on manipulators under uncertainty. © 2024 IEEE.

关键词： Genetic algorithms

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Investigating Neural Network Hyperparameter Variations in Robotic Arm Inverse Kinematics for Different Arm Lengths 3

Investigating Neural Network Hyperparameter Variations in Ro...

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3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Bouzid, Rania Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India Tunis1002 Tunisia

ISBN: (纸本)9798350349207

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. © 2024 IEEE.

关键词： Inverse kinematics

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Multi-purposeful activities for robot-assisted autism therapy: What works best for children's social outcomes? 23

Multi-purposeful activities for robot-assisted autism therap...

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18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023

作者： Zhanatkyzy, Aida Telisheva, Zhansaule Amirova, Aida Rakhymbayeva, Nazerke Sandygulova, Anara Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Graduate School of Education Nazarbayev University Astana Kazakhstan

ISBN: (纸本)9781450399647

This research designed and applied 24 multi-purposeful robot activities of varying social mediation levels in a multiple-session experiment with 34 children of diverse autistic characteristics in a rehabilitation setting. This paper explores what type of robot activities can meet individual needs to bring more socio-behavioral progress and juxtaposes child characteristics to identify behavioral outcomes in each activity. This knowledge would help us to respond to the question of what activity types suit specifc subgroups of Autism Spectrum Disorder (ASD). Our data analysis included coding 48.5 hours of video data for a total of 14 measures to fully capture children's activity-based socio-emotional outcomes. Overall, the activities on varying social mediation levels brought more or less positive social outcomes to all children. However, children showed some diferent behavioral outcomes as mediated by core autism-related and age-specifc characteristics. This study provides in-depth accounts of what might be helpful in designing and applying multi-purposeful activities responsive to the diverse needs of children. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Robots

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Obstacle Identification Based on Deep Learning for Inspection Robot of FAST Wire Rope 2

Obstacle Identification Based on Deep Learning for Inspectio...

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2nd International Conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Yang, Chengxu Sun, Xiangyu Liu, Gangfeng School of Mechatronics Engineering Harbin Institute of Technology Harbin China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China

ISBN: (纸本)9798350308341

In this paper, for the requirements of image recognition and localization of the obstacles existing on the moving path in the inspection task of FAST cable inspection robot, the deep learning method is used to realize the recognition of multi-kind and multi-scale obstacles. The obstacles on FAST wire rope have not been identified before. By enhancing the data set and improving the network model, the recognition accuracy and speed are both improved, which meets the efficiency and accuracy requirements of obstacle detection on the wire rope, and provides help for the subsequent robot pace planning and overhaul tasks. © 2023 IEEE.

关键词： Inspection

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Design and Analysis of Extensible Cable-Driven Continuum Robot with Variable Stiffness

Design and Analysis of Extensible Cable-Driven Continuum Rob...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Seleem, Ibrahim A. Naeem, Mohamed A. Ishii, Hiroyuki Waseda University Department of Modern Mechanical Engineering Japan Menoufia University Faculty of Electronic Engineering Department of Industrial Electronics and Control Engineering Egypt Harbin Institute of Technology School of Mechanical and Electrical Engineering Department of Mechatronics Engineering China South Valley University Faculty of Engineering Department of Mechatronics and Robotics Engineering Egypt

ISBN: (纸本)9798350325706

Extensible Soft Robots (ESRs) have increasingly attracted attention, especially in tight and complex environments, owing to their dexterity, and wide reachable workspaces relative to their volume. The existing actuation methods of ESR still suffer from challenges including large dimensions, expensive, complex modeling and control, and limited payload capacity. In this paper, we introduce a novel design for a lightweight, extensible and variable stiffness soft robot-based cable-driven actuation without backbone. The design composes of one section with three segments based on semi-octagon honeycomb structure, wherein the middle segment acts like a soft spring to provide extension feature. Additionally, the top and bottom segments have identical structure with two honeycomb patterns embedded within each other to improve the stiffness of the design. A design analysis is conducted to optimize the proposed structure with respect to stress and displacement. Additionally, a shape estimation approach is utilized to get accurate inference of the prototype's shape based on data acquired from a low-cost Inertial Measurement Unit (IMU) and Motion Capture System (MCS), and constant curvature assumption. A series of experiments including workspace reach-ability, repeatability, shape approximation and payload analysis are carried out to validate the proposed design. The results show that the prototype exhibits good compression/extension ratio up to 42% and 50% relative to its normal length, respectively. Moreover, its payload capability reaches to 565 grams. © 2023 IEEE.

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Target Tracking Control of Space-Manipulators on Lie Groups

Target Tracking Control of Space-Manipulators on Lie Groups

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Moghaddam, Borna Monazzah Chhabra, Robin Carleton University Autonomous Space Robotics and Mechatronics Laboratory Mechanical and Aerospace Engineering Department OttawaONKIS 5B6 Canada

ISBN: (纸本)9798350337020

We present a full-pose end-effector control approach on Lie groups for free-floating space manipulators with non-zero momentum while tracking a moving target during proximity operations. We model space-manipulators as open-chain multi-body systems with 1-degree-of-freedom joints where the configuration space of the spacecraft is isomorphic to the Special Euclidean group SE(3). We formulate the dynamics of the spacecraft-manipulator via the Lagrange-Poincare equations and the dynamics of its target via Euler-Poincare equations to avoid kinematic singularities associated with parametrization of their poses. This model reduces the phase space of the space-manipulator by exploiting its inherent independence of the spacecraft's pose. We consider the full pose of the end-effector relative to the target as the system output, which transforms the output-tracking control problem into an output-regulation problem. To avoid parametrization singularities of this output, we perform feedback linearization on the matrix Lie group SE (3) in the reduced phase space of the space-manipulator. We then propose a feedback/feedforward proportional-integral-derivative workspace controller, based on coordinate-free pose and velocity error functions defined on the matrix Lie group associated with the target's relative pose. We provide analytical proof of the almost-global stability of the presented controller when regulating the end-effector's pose relative to the target towards identity. © 2023 IEEE.

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