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检索条件"任意字段=2024 International Conference on Intelligent Robotics and Automatic Control"

共 5267 条记录，以下是331-340 订阅

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Online Multi-Contact Feedback Model Predictive control for Interactive Robotic Tasks

Online Multi-Contact Feedback Model Predictive Control for I...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Han, Seo Wook Iskandar, Maged Lee, Jinoh Kim, Min Jun Korea Adv Inst Sci & Technol KAIST Intelligent Robot Syst Lab Daejeon South Korea German Aerosp Ctr DLR Inst Robot & Mechatron Wessling Germany Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon South Korea

ISBN: (纸本)9798350384581;9798350384574

In this paper, we propose a model predictive control (MPC) that accomplishes interactive robotic tasks, in which multiple contacts may occur at unknown locations. To address such scenarios, we made an explicit contact feedback loop in the MPC framework. An algorithm called Multi-Contact Particle Filter with Exploration Particle (MCP-EP) is employed to establish real-time feedback of multi-contact information. Then the interaction locations and forces are accommodated in the MPC framework via a spring contact model. Moreover, we achieved real-time control for a 7 degrees of freedom robot without any simplifying assumptions by employing a Differential-Dynamic-Programming algorithm. We achieved 6.8kHz, 1.9kHz, and 1.8kHz update rates of the MPC for 0, 1, and 2 contacts, respectively. This allows the robot to handle unexpected contacts in real time. Real-world experiments show the effectiveness of the proposed method in various scenarios.

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Learning Variable Compliance control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System

Learning Variable Compliance Control From a Few Demonstratio...

引用

2024 international conference on intelligent Robots and Systems

作者： Kamijo, Tatsuya Beltran-Hernandez, Cristian C. Hamaya, Masashi OMRON SINIC X Corp Tokyo Japan Univ Tokyo Dept Mech Engn Tokyo Japan

ISBN: (纸本)9798350377712;9798350377705

Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment, potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors, they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative, allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous, contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR) controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT (Compliance control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability and safety. Code available at https://***/omron-sinicx/CompACT.

关键词： Demonstrations

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Privacy-Preserving Lane Change Prediction using Deep Learning Models 5

Privacy-Preserving Lane Change Prediction using Deep Learnin...

引用

5th international conference on Artificial Intelligence, robotics and control (AIRC)

作者： Qasemabadi, Armin Nejadhossein Mozaffari, Saeed Ahmadi, Majid Alirezaee, Shahpour Univ Windsor Elect & Comp Engn Windsor ON Canada Univ Windsor Mech Automot & Mat Engn Windsor ON Canada

ISBN: (纸本)9798350385083;9798350385076

Lane Change Prediction (LCP) is a pivotal component of intelligent Transportation Systems (ITS) which aims to enhance road safety and optimize traffic flow. Deep learning models have been used in LCP systems, but the need for extensive data poses privacy challenges in deploying such models. This paper aims to utilize Secure Multiparty Computation (SMPC) technique that allows multiple parties to jointly compute lane change probability using their private inputs without revealing sensitive information to each other. We trained Recurrent Neural Network (RNN) models on HighD dataset. In the inference part, Secure Tanh function was utilized for privacy-preserving LCP. Experimental results show that the accuracy of the proposed LCP based on SMPC is almost the same as the traditional LCP with unsecure computation, by calculating MSE for main function in non-SMPC and SMPC scenarios in the range of 10(-7).

关键词： Lane Change Prediction Secure Multiparty Computation Deep Learning intelligent Transportation Systems Recurrent Neural Network

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A Dual Closed-Loop control Strategy for Human-Following Robots Respecting Social Space

A Dual Closed-Loop Control Strategy for Human-Following Robo...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Peng, Jianwei Liao, Zhelin Su, Zefan Yao, Hanchen Zeng, Yadan Dai, Houde Chinese Acad Sci Quanzhou Inst Equipment Mfg Fujian Inst Res Struct Matter Jinjiang 362216 Peoples R China Univ Chinese Acad Sci Fujian Coll Jinjiang 362216 Peoples R China Nanyang Technol Univ Robot Res Ctr Sch Mech & Aerosp Engn Singapore Singapore

ISBN: (纸本)9798350384581;9798350384574

Human following for mobile robots has emerged as a promising technique with widespread applications. To ensure psychological comfort while collaborating, coexisting, and interacting with humans, robots need to respect the social space of the target person. In this study, we propose a dual closed-loop human-following control strategy that combines model predictive control (MPC) and impedance control. The outer-loop MPC ensures precise control of the robot's posture while tracking the target person's velocity and direction to coordinate the motion between them. The inner-loop impedance controller is employed to regulate the robot's motion and interaction force with the target person, enabling the robot to maintain a respectful and comfortable distance from the target person. Concretely, the social interaction dynamics characteristics between the robot and the target person are described by human-robot interaction dynamics, which considers the rules of social space. Furthermore, an obstacle avoidance component constructed using behavioral dynamics is integrated into the impedance controller. Experimental results demonstrate the effectiveness of the proposed method in achieving human following and obstacle avoidance without intruding into the intimate zone of the target person.

关键词： Predictive control systems

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Nonlinear Dynamic Modeling and LQR Optimal control of Wheel-Bipedal Robots 6

Nonlinear Dynamic Modeling and LQR Optimal Control of Wheel-...

引用

6th international conference on robotics, intelligent control and Artificial Intelligence, RICAI 2024

作者： Hu, Minchao Mao, Jianliang Zhou, Xin Zhang, Chuanlin College of Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9798331541699

The wheel-bipedal robot integrates the advantages of both wheeled and legged systems, enabling efficient locomotion. However, many control approaches simplify this system to a wheeled linear inverted pendulum (WLIP), treating the legs and body as a unified entity and overlooking the distinct dynamics and effects of the legs. This paper derives a simplified model of the leg mechanism that captures the interactions between the robot’s leg and body dynamics. A linear quadratic regulator (LQR) controller is further designed to maintain overall balance, while a dedicated leg controller governs leg-specific movements, establishing a dynamic composite control framework. Additionally, this paper details the system architecture and development of controllers for various scenarios. Experimental results from prototype testing in real-world environments demonstrate the effectiveness and robustness of the proposed control strategies. © 2024 IEEE.

关键词： Wheels

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Pulsating Fluidic Sensor for Sensing of Location, Pressure and Contact Area

Pulsating Fluidic Sensor for Sensing of Location, Pressure a...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Jones, Joanna Pontin, Marco Damian, Dana D. Univ Sheffield Dept Automat Control & Syst Engn Sheffield Biomed Robot Lab Sheffield S1 3JD S Yorkshire England Univ Sheffield Sheffield Robot Sheffield S Yorkshire England Univ Sheffield Insigne Inst In Silico Med Sheffield S Yorkshire England

ISBN: (纸本)9798350384581;9798350384574

Designing information-rich and space-efficient sensors is a key challenge for soft robotics, and crucial for the development of safe soft robots. Sensing and understanding the environmental interactions with a minimal footprint is especially important in the medical context, where portability and unhindered patient/user movement is a priority, to move towards personalized and decentralized healthcare solutions. In this work, a pulsating fluidic soft sensor (PFS) capable of determining location, pressure and contact area of press events is shown. The sensor relies on spatio-temporal resistance changes driven by a pulsating conductive fluid. The sensor demonstrates good repeatability and distinction of single and multiple press events, detecting single indents of sizes greater than 1 cm, forces larger than 2 N, and various locations across the sensor, as well as multiple indents spaced 2 cm apart. Furthermore, the sensor is demonstrated in two applications to detect foot placement and grip location. Overall, the sensor represents an improvement towards minimizing electronic hardware, and cost of the sensing solution, without sacrificing the richness of the sensing information in the field of soft fluidic sensors.

关键词： Fluidic logic devices

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Fuzzy Shared control Method and automatic Assistance for Aircraft Under Pilot-Induced Oscillations

Fuzzy Shared Control Method and Automatic Assistance for Air...

引用

2024 IEEE international conference on control Science and Systems Engineering, ICCSSE 2024

作者： Fan, Xue Zou, Quan Song, Wanghang Xie, Rong Navigation and Control Northwestern Polytechnical University Department of Guidance Xi'an China Chinese Flight Test Research Institute Xi'an China

ISBN: (纸本)9798331517199

This paper presents a new human-machine shared control method to improve the control performance of the aircraft in abnormal situations. To solve the problem of Pilot-Induced Oscillations(PIO) caused by elevator rate limitation, a shared control method based on autopilot assistance is designed by adjusting the gain of the pilot's control command. This method takes advantage of the precision control of the autopilot and corrects the pilot's excessive control behavior, thereby enhancing the stability of the aircraft in complex environments. The key feature of the proposed shared control is that it utilizes the control command of the autopilot to achieve the process of assisting the pilot in flying, which helps reduce the pilot's high control gain while keeping the elevator away from saturation. At the same time, considering the fact that the pilot's instantaneous judgment of triggering a fault has certain ambiguity, the pilot's adaptive logic trigger module is utilized with fuzzy logic. Finally, the effectiveness of the shared controller is verified by the experimental simulation of F-16 longitudinal dynamics under abnormal conditions. © 2024 IEEE.

关键词： Fuzzy control

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Robust control Methods for Vehicle Trajectory Tracking Amid Multi-Channel Network Attacks 6

Robust Control Methods for Vehicle Trajectory Tracking Amid ...

引用

6th international conference on robotics, intelligent control and Artificial Intelligence, RICAI 2024

作者： Kang, Zigui Li, Tao Fan, Xiaofei Nanjing University of Information Science and Technology Nan Jing210044 China

ISBN: (纸本)9798331541699

Network attacks present a critical threat to vehicle trajectory tracking systems, especially when multi-channel attacks can severely compromise system stability and tracking accuracy. Addressing these issues is challenging due to the complexity of vehicle dynamics, attack signal suppression, and reliable tracking on roads with varying curvature. This paper proposes an advanced control strategy based on a fuzzy extended observer to mitigate multi-channel network attacks. A hybrid model integrating fuzzy inference with Takagi-Sugeno (T-S) techniques is developed to capture trajectory tracking dynamics. A fuzzy extended observer estimates attack impacts on vehicle dynamics, and a fuzzy controller is designed to counteract these attacks while ensuring stable tracking. The stability analysis confirms the stability of the system. Finally, co-simulations in CarSim and Simulink demonstrate the practical effectiveness of the proposed control strategy. © 2024 IEEE.

关键词： Robust control

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Research on Motion control Technology of Quadruped Robot Arm Based on Visual Analysis 6

Research on Motion Control Technology of Quadruped Robot Arm...

引用

6th international conference on robotics, intelligent control and Artificial Intelligence, RICAI 2024

作者： Yao, Shuaibing Li, Danyong Zhang, Tong Ma, Mingxuan School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (纸本)9798331541699

To address the issues of limited working range, weak interactivity, and low safety in robotic arm, a collaborative quadruped robot with an attached robotic arm is designed and implemented. Using the door-opening task as an example, this system covers target recognition and measurement, kinematic modeling and path planning, collision detection, and collaborative control. The Mask-RCNN algorithm is employed for precise identification of the door handle, and a depth camera is used to measure the spatial distance to the door handle. A torque observer based on generalized momentum is designed for collision detection of the robotic arm. Based on the coordinates of the target object and the observed external torque, a motion control strategy for the robotic arm to open the door is planned. Finally, a collaborative control logic is established between the quadruped robot and the robotic arm, enabling them to work together to complete the door-opening task while ensuring safety. © 2024 IEEE.

关键词： Multipurpose robots

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Research on Density control of Coal Preparation Based on Fast Decoupling Predictive control 7

Research on Density Control of Coal Preparation Based on Fas...

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7th international conference on intelligent robotics and control Engineering, IRCE 2024

作者： Wang, Jing China Coal Technology and Engineering Group Nanjing Design and Research Institute Nanjing China Nanjing Yehengda Intelligent System Co. Ltd. Nanjing China

ISBN: (纸本)9798350352399

This article presents a split-range control strategy based on fast decoupling to address the issue of extensive online computation in multivariable predictive control for coal preparation's suspension density. By employing split-range control, the predictive model's operating variables are reduced by comparing the suspension density with the set value, thereby retaining the crucial operating variables that significantly influence the controlled variable. Less significant operating variables are treated as measurable disturbances for swift compensation, effectively decoupling the original multivariable control model into a combination of multiple single-variable control models. Consequently, a decentralized rolling optimization strategy is devised using these multiple single-variable control models. Simulation experiments have demonstrated the algorithm's ability to substantially reduce the computational burden of online optimization and shorten calculation time from 1.147s to 0.253s once, thereby meeting the rapid response requirements of industrial applications. © 2024 IEEE.

关键词： Predictive control systems

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