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Forward Kinematics Development of a 3-RRS Parallel Manipulator for Real-Time control

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Forward Kinematics Development of a 3-RRS Parallel Manipulat...

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

作者： S. Ali A. Moosavian Omid Mahdizadeh Sina Hallajian Sara Ashrafi Mohammad Jamali Vahid Akbari 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

ISBN: (纸本)9781665454537

In control of parallel robots, forward kinematics calculation is mainly required. In some studies, obtained equations are solved numerically with massive calculations that is not acceptable for control implementations. This paper introduces a new method to extract forward kinematics for a 3- RRS robot. This method calculates the kinematics of passive joints with two separated equations, which can be solved to decrease the total computation demands. Also, a new weight compensation mechanism is proposed to improve the dynamic response by a linear pneumatic actuator placed between the ground and the end-effector. On the other hand, to use various control algorithms, the position of the rolling ball on the end- effector (moving platform) must be measured. This is performed using Convolutional Neural Networks (CNNs) as image processing algorithms. Since the system must work quickly for a high-frequency task, the proposed image processing algorithm can provide the desired fast feedback. Experimental results reveal the merits of the above-described proposed modules.

关键词： Pneumatic actuators Image processing Heuristic algorithms Kinematics Position measurement Pneumatic systems Mathematical models

iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

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arXiv 2024年

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen The Flight Dynamics & Control/Hybrid Systems Lab Purdue University West LafayetteIN47907 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Lab Department of Computer Science and Engineering University at Buffalo NY14260 United States

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents). © 2024, CC BY.

关键词： Traveling salesman problem

A ROS Implementation of the Situational Awareness and Maneuvering systems for an Autonomous Marine Vessel

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A ROS Implementation of the Situational Awareness and Maneuv...

OCEANS

作者： Matheus F. Reis Gustavo Andrade Francisco Neves Paulo Silva Rômulo T. Rodrigues A. Pedro Aguiar Cyber-Physical Control Systems and Robotics lab Faculty of Engineering - University of Porto

ISBN: (纸本)9781665427883

This paper describes a study on the development, implementation, and evaluation of situational awareness, and the maneuvering systems for an autonomous ship. The implementation and validation of these modules within the Robot Operating System (ROS) framework are motivated by the participation of the Atlantics team (UPORTO) in the Njord - The Autonomous Ship Challenge, a pilot student-driven competition that involves the participation of several universities and is organized by a team with the Norwegian University of Science and Technology. The competition consists of a set of several tasks that the participating teams are asked to solve. These tasks are indeed current challenges in the marine industry towards autonomy and involve the design and evaluation of motion control and navigation algorithms with situational awareness and collision avoidance capabilities for a standardized vessel and sensor suite. In this paper, we present the modules of object detection, object tracking, and object state estimation that constitute the situational awareness system, and the modules for planning and motion control, including a spline-based motion planner, a nonlinear path-following controller with a safety critical module for obstacle avoidance, and a model reference adaptive controller for the inner-loop (autopilot). Several simulation results using the competition simulator are shown.

关键词： Tracking loops Navigation Object detection Trajectory Safety Vehicle dynamics Task analysis

Ensuring Safety for UAVs Through Event-Triggered Predictive control

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Ensuring Safety for UAVs Through Event-Triggered Predictive ...

Mediterranean Conference on control and Automation (MED)

作者： Sotirios N. Aspragkathos George C. Karras Kostas J. Kyriakopoulos Control Systems Lab School of Mechanical Engineering National Technical University of Athens Athens Greece Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Engineering Division and Center for AI & Robotics (CAIR) New York University Abu Dhabi UAE

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

This paper presents a control system for un-manned aerial vehicles (UAVs) that track deformable objects. Using Event-Triggered (ET) principles and Image Moments-Based Nonlinear Model Predictive control (NMPC), the suggested approach improves flexibility by triggering control changes in response to specific occurrences. The incorporation of Barrier Functions (BFs) enforces safety requirements, which are critical for negotiating uncertain terrains. The framework's implementation and testing on a UAV tracking deformable coastlines are intended to demonstrate its effectiveness in adapting to dynamic situations while adhering to safety limitations.

关键词： Coastlines Target tracking Streaming media Aerodynamics Visual servoing Safety Vehicle dynamics

Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System

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Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzz...

2022 International Conference on Innovations and Development of Information Technologies and robotics, IDITR 2022

作者： Haider, Muhammad Husnain Ali, Hub Khan, Abdullah Aman Zheng, Hao Bhutta, M. Usman Maqbool Usman, Shaban Zhi, Pengpeng Wang, Zhonglai University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute Huzhou China Casia State Key Laboratory for Management & Control of Complex Systems Beijing China Sichuan Artificial Intelligence Research Institute Yibin China The Chinese University of Hong Kong Robotics Lab Mae Hong Kong

ISBN: (纸本)9781665409322

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path. © 2022 IEEE.

关键词： Navigation

Design and Validation of Tunable Stiffness Actuator using Soft-Rigid Combined Layer Jamming Mechanism

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Design and Validation of Tunable Stiffness Actuator using So...

IEEE International Conference on Soft robotics (RoboSoft)

作者： Seoyeon Ham Brian Byunghyun Kang Kevin Abishek Hyunglae Lee Wansoo Kim Department of Interdisciplinary Robot Engineering Systems Hanyang University Gyeonggi-do Republic of Korea Neuromuscular Control and Human Robotics Lab. Arizona States University AZ USA Department of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea Robotics Department Ansan-si Gyeonggi-do Republic of Korea

This paper presents a tunable stiffness actuator with a soft-rigid combined layer jamming mechanism. The tunable stiffness actuator is aimed to be integrated into an exosuit to prevent ankle sprain and avoid or mitigate the development of chronic ankle instability. The main purpose of the soft-rigid layer jamming mechanism is to produce large stiffness with a small volume and achieve a linear stiffness characteristic. To this end, the actuator is designed to include rigid retainer pieces within the soft silicone layers, and each soft-rigid layer is jammed to induce stiffness changes. To validate the stiffness characteristics of the proposed soft-rigid actuator, a series of experiments were performed and stiffness changes were investigated for varying jamming states from unjammed to fully jammed states. Increasing the number of jamming layer effectively increased the actuator stiffness, which was consistent with expected results from the analytical model. Soft-rigid actuator's stiffness at the fully jammed state was 212.1% and 123.1% higher than the unjammed state for one-side and both sides anchored conditions, respectively. Compared to the soft actuator without the rigid retainer, the soft-rigid actuator exhibited a more linear characteristic (Pearson correlation coefficient = 0.990 and 0.997 for one-side and both sides anchored conditions, respectively). Moreover, the soft-rigid actuator achieved significantly higher stiffness than the soft actuator in all jamming states (at least 41.3% increase in each jamming state). The results suggest a potential use of the tunable stiffness actuator to develop a soft ankle exosuit with highly variable but linear stiffness characteristics.

关键词： Correlation coefficient Actuators Analytical models Composite materials Mechanical cables Soft robotics Jamming

Dynamics Modeling using Visual Terrain Features for High-Speed Autonomous Off-Road Driving

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arXiv 2024年

作者： Gibson, Jason Alavilli, Anoushka Tevere, Erica Theodorou, Evangelos A. Spieler, Patrick Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA30332 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States

Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terrain can affect parameters such as traction, tire slip, and rolling resistance. To achieve effective planning in such environments, it is crucial to have a dynamics model that can accurately anticipate these conditions. In this work, we present a hybrid model that predicts the changing dynamics induced by the terrain as a function of visual inputs. We leverage a pre-trained visual foundation model (VFM) DINOv2, which provides rich features that encode fine-grained semantic information. To use this dynamics model for planning, we propose an end-to-end training architecture for a projection distance independent feature encoder that compresses the information from the VFM, enabling the creation of a lightweight map of the environment at runtime. We validate our architecture on an extensive dataset (hundreds of kilometers of aggressive off-road driving) collected across multiple locations as part of the DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) program. https://***/watch?v=dycTXxEosMk © 2024, CC BY.

关键词： Off road vehicles

TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting