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MROS: A framework for robot self-adaptation

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MROS: A framework for robot self-adaptation

International Conference on Software engineering - Companion, ICSE, Companion

作者： Gustavo Rezende Silva Nadia Hammoudeh Garcia Darko Bozhinoski Harshavardhan Deshpande Mario Garzon Oviedo Andrzej Wasowski Mariano Ramírez Montero Carlos Hernandez Corbato Department of Cognitive Robotics Delft University of Technology Delft Netherlands Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany IRIDIA University Libre du Bruxelles Bruxelles Belgium Computer Science Department IT University Conpenhagen Denmark

Self-adaptation can be used in robotics to increase system robust- ness and reliability. This work describes the Metacontrol method for self-adaptation in robotics. Particularly, it details how the MROS (Metacontrol for ROS Systems) framework implements and pack- ages Metacontrol, and it demonstrate how MROS can be applied in a navigation scenario where a mobile robot navigates in a factory floor. Video: https://***/watchvISe9aMskJuE

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SoftGPT: Learn Goal-Oriented Soft Object Manipulation Skills by Generative Pre-Trained Heterogeneous Graph Transformer

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SoftGPT: Learn Goal-Oriented Soft Object Manipulation Skills...

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

作者： Junjia Liu Zhihao Li Wanyu Lin Sylvain Calinon Kay Chen Tan Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Idiap Research Institute Martigny Switzerland

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications, developing prior knowledge of the representation and dynamics of soft objects is necessary. In this regard, we propose a pretrained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. For each downstream task, a goal-oriented policy agent is trained to predict the subsequent actions, and SoftGPT generates the consequences of these actions. Integrating these two approaches establishes a thinking process in the robot's mind that provides rollout for facilitating policy learning. Our results demonstrate that leveraging prior knowledge through this thinking process can efficiently learn various soft object manipulation skills, with the potential for direct learning from human demonstrations.

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THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-conditioned Human Mtion Prediction

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THÖR-Magni: Comparative Analysis of Deep Learning Models fo...

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Tiago Rodrigues de Almeida Andrey Rudenko Tim Schreiter Yufei Zhu Eduardo Gutierrez Maestro Lucas Morillo-Mendez Tomasz P. Kucner Oscar Martinez Mozos Martin Magnusson Luigi Palmieri Kai O. Arras Achim J. Lilienthal Örebro University Sweden Robert Bosch GmbH Corporate Research Stuttgart Germany Department of Electrical Engineering and Automation Mobile Robotics Group Aalto University Finland FCAI Finnish Center for Artificial Intelligence Finland

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THÖR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.

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Copper Losses Studying Using a New Real Time V/f Scalar Control Approach 24

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Copper Losses Studying Using a New Real Time V/f Scalar Cont...

24th International Conference on Soft Computing and Measurements, SCM 2021

作者： Hasan, Mohammed S. Albakhait, Mohammed D. Sharaf, Al Mahturi F. Jaber, Ahmed I. University of Diyala Department of Power and Electrical Machine College of Engineering Diyala Iraq Techniques Electrical Engineering Technical College Middle Technical University Department of Electrical Power Engineering Baghdad Iraq Saint-Petersburg Electrotechnical University 'LETI' Department of Robotics and Automation of Industrial Systems Saint Petersburg Russia

ISBN: (纸本)9781665439749

The paper presents an investigation of copper losses studying of Brushless Doubly-Fed Reluctance Machine supported with Real time results based on dSPACE 1103 hardware application and its features to prove a theoretical approach of driver capabilities of Brushless Doubly Fed Reluctance machine at multi-speed in induction and synchronous modes. To overcome these challenges, we need to modernize the control algorithms strategy. Scalar control is one of control theories with certain Boost voltage that brings a significant improvement to the performances of control algorithm supported with BDFRM features. This strategy can be used to confirm BDFRM's ability with FOC to manipulate multiple control algorithms that offer the machine an optimal solution instead of induction machine, synchronous machine and permanent magnate synchronous machine. © 2021 IEEE.

关键词： Induction machine

FBG-Based Online Learning and 3-D Shape Control of Unmodeled Continuum and Soft Robots in Unstructured Environments

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arXiv 2022年

作者： Lu, Yiang Chen, Wei Lu, Bo Zhou, Jianshu Chen, Zhi Dou, Qi Liu, Yun-Hui T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The Hong Kong Center for Logistics Robotics Hong Kong The Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China

In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and control technologies are crucial for continuum robots to perform the tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive controller by leveraging FBG shape feedback that can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study by using two continuum robots both integrated with multicore FBGs, including a robotic-assisted colonoscope and multi-section extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments as well as phantom experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models

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arXiv 2023年

作者： Zhang, Zhen Lin, Anran Wong, Chun Wai Chu, Xiangyu Dou, Qi Samuel Au, K.W. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Multiscale Medical Robotics Centre Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Computer Aided Medical Procedures Technical University of Munich Germany

This paper proposes an interactive navigation framework by using large language and vision-language models, allowing robots to navigate in environments with traversable obstacles. We utilize the large language model (GPT-3.5) and the open-set Vision-language Model (Grounding DINO) to create an action-aware costmap to perform effective path planning without fine-tuning. With the large models, we can achieve an end-to-end system from textual instructions like "Can you pass through the curtains to deliver medicines to me?", to bounding boxes (e.g., curtains) with action-aware attributes. They can be used to segment LiDAR point clouds into two parts: traversable and untraversable parts, and then an action-aware costmap is constructed for generating a feasible path. The pre-trained large models have great generalization ability and do not require additional annotated data for training, allowing fast deployment in the interactive navigation tasks. We choose to use multiple traversable objects such as curtains and grasses for verification by instructing the robot to traverse them. Besides, traversing curtains in a medical scenario was tested. All experimental results demonstrated the proposed framework’s effectiveness and adaptability to diverse environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

PV-OSIMr: A Lowest Order Complexity Algorithm for Computing the Delassus Matrix

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arXiv 2023年

作者： Sathya, Ajay Suresha Decré, Wilm Swevers, Jan Inria Département d’Informatique École Normale Supérieure PSL Research University France Division of Robotics Automation and Mechatronics The Department of Mechanical Engineering KU Leuven FlandersMake@KULeuven Leuven Belgium

We present PV-OSIMr, an efficient algorithm for computing the Delassus matrix (also known as the inverse operational space inertia matrix) for a kinematic tree, with the lowest order computational complexity known in literature. PV-OSIMr is derived by optimizing the recently proposed PV-OSIM algorithm using the compositionality of the force and motion propagators. It has a computational complexity of O(n + m2) compared to O(n + m2d) of the PV-OSIM algorithm and O(n + md + m2) of the extended force propagator algorithm (EFPA), where n is the number of joints, m is the number of constraints and d is the depth of the kinematic tree. Since the Delassus matrix is an m×m sized matrix and its computation must consider all the n joints, PV-OSIMr’s asymptotic computational complexity is optimal. We further benchmark our algorithm and find it to be often more efficient than the PV-OSIM and EFPA in practice. © 2023, CC BY-SA.

关键词： Trees (mathematics)

Multi-stage warm started optimal motion planning for over-actuated mobile platforms

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arXiv 2022年

作者： Paz-Delgado, Gonzalo Jesús Pérez-Del-Pulgar, C.J. Azkarate, M. Kirchner, F. García-Cerezo, A. The Space Robotics Laboratory Department of Systems Engineering and Automation Universidad de Málaga Andalucia Tech Málaga29070 Spain The Automation and Robotics Section European Space Agency NoordwijkAZ 2201 Netherlands The DFKI Robotics Innovation Center Bremen Robert-Hooke-Str. 1 Bremen28359 Germany

This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers non-linearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. This novel approach has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time, increasing the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of an over-actuated mobile platform. © 2022, CC BY.

关键词： Rovers

Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

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arXiv 2023年

作者： Li, Shilei Shi, Dawei Lou, Yunjiang Zou, Wulin Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Xeno Dynamics Control Department Xeno Dynamics Co. Ltd Shenzhen518055 China

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters