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IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Ren C. Luo Ming Hsiao Tsung-Wei Lin Center for Intelligent Robotics and Automation Research National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei Taiwan 106

ISBN: (纸本)9781467363563

In this paper, an automatic erect stair climbing mobile robot is developed for indoor service applications. The design of high center of mass, tilt axis near ground, and the triangular wheel-legged structure enable the robot to climb stairs in a dynamic and self-balancing way. The wheel-legged mechanism also keeps the advantages of differential-wheeled mobile platforms when moving on flat ground, such as easy to control, saving power, and zero turning radius. Moreover, the overall mechanical design fits the requirements for a service robot to have proper height and small footprint, which can make human-robot interaction more natural and comfortable in indoor environment. Since the perceptual and control system of the robot are both well integrated, we successfully demonstrate the stair-climbing function and prove that the design and implementation of our work are feasible and efficient. As a result, the robot is expected to be a prototype of universal platform for indoor mobile service robots in the future.

关键词： Erect Wheel-Legged Stair Climbing Robot Indoor Service Applications small footprint Robot control systems INDOORS Service robots Climbing indoor environment Mobile robots ground

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Multimodal information fusion for human-robot interaction

Multimodal information fusion for human-robot interaction

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Ren C. Luo Y. C. Wu P. H. Lin International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University Electrical Engineering Department National Taiwan University Taipei Taiwan

In this paper we introduce a multimodal information fusion for human-robot interaction system These multimodal information consists of combining methods for hand sign recognition and emotion recognition of multiple. These different recognition modalities are an essential way for Human-Robot Interaction (HRI). Sign language is the most intuitive and direct way to communication for impaired or disabled people. Through the hand or body gestures, the disabled can easily let caregiver or robot know what message they want to convey. Emotional interaction with human beings is desirable for robots. In this study, we propose an integrated system which has ability to track multiple people at the same time, to recognize their facial expressions, and to identify social atmosphere. Consequently, robots can easily recognize facial expression, emotion variations of different people, and can respond properly. In this paper, we have developed algorithms to determine the hands sign via a process called combinatorial approach recognizer equation. These two recognizers are aimed to complement the ability of discrimination. In our facial expression recognition scheme, we fuse feature vectors based approach and differential-active appearance model feature based approach to obtain not only apposite positions of feature points, but also more information about texture and appearance. We have successfully demonstrated hand gesture recognition and emotion recognition experimentally with proof of concept.

关键词： Support vector machines Image color analysis Face recognition Face Service robots Emotion recognition

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Alignment and Tracking of Facial Features with Component-Based Active Appearance Models and Optical Flow

Alignment and Tracking of Facial Features with Component-Bas...

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IEEE/ASME International Conference on Advanced intelligent Mechatronics (AIM)

作者： Ren C. Luo Chun Y. Huang Pei H. Lin Center of Intelligent Robotics and Automation Research National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan

ISBN: (纸本)9781457708381

The objective of this paper is to investigate alignment and tracking of facial features with component based active appearance models and optical flow. Face tracking becomes essential issue in the area of human-computer interactions. With accurate tracking and analysis of facial features, computers or robots can response properly to user's facial emotional movements and facial expressions. We apply Active Appearance Model (AAM) and other techniques in real-time on cameras. Good AAM alignment results depend on proper selections of initial positions. However, it does take a lot of time when it applies image pyramid to get accurate results. In this paper, we introduce a brand new method to apply AAM fitting and effectively solve above problems. In our fitting scheme, we apply partial AAM separately on eyes and mouth. Thus, we can make facial features alignment much more efficient, and it becomes able to perform real-time alignment and tracking to real-world video. To make partial AAM more stable, we determine initial positions of facial feature models by multi-level optical flow. By the use of our developed algorithm, it is relative easier to get accurate positions of facial features and extract detail information of faces for further application in real world environments.

关键词： Alignment and Tracking of Facial Features Active Appearance Model (AAM) Optical Flow Human-Computer Interactions

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Brain Tumor Recognition Based on Data Augmentation and Convolutional Neural Network 4

Brain Tumor Recognition Based on Data Augmentation and Convo...

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4th International Conference on intelligent Informatics and Biomedical Sciences, ICIIBMS 2019

作者： Xu, Han Huang, Zheng Zhao, Yiwen Song, Guoli College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center Shenyang China Department of Orthopaedics Affiliated Traditional Chinese Medicine Hospital Southwest Medical University Luzhou China

ISBN: (纸本)9781728133805

The brain tumor is one of the most dangerous diseases at present. Accurate diagnosis of brain tumors can contribute to improving the prognosis conditions of patients. Existing methods have some shortcomings, such as manual extraction of features and insufficient amount of data. Since the convolutional neural network (CNN) can extract features automatically, we propose a deep Convolutional Neural Network to diagnose the brain tumors. In this paper, an automatic system based on CNN is proposed to classify three categories of brain Magnetic Resonance Images, including normal images, brain images with meningiomas and brain images with gliomas. Several steps, including image preprocessing, data augmentation and image classification, are applied to the original brain images. And the experiments show that the accuracy of the proposed system on testing set can reach 93.33%, which indicates that our model can achieve a comparable classification result. © 2019 IEEE.

关键词： Diagnosis

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Effective online trajectory generation of waist and arm for enhancing humanoid robot walking

Effective online trajectory generation of waist and arm for ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ren C. Luo Chien-An Chen Anne Spalanzani International Center of Excellence in Intelligent Robotics and Automation Research Electrical Engineering Department National Taiwan University Taipei INRIA Grenoble Rhone Alpes France

ISBN: (纸本)9781509008742

Inspired by the human torso, humanoid robots usually equipped with active waist joints. For arbitrary walking patterns or whole body motions, the well-designed waist trajectories can greatly enhance the motion of robot legs and allow the robot to perform larger walking step length or crossing higher stair gaps. In this paper, we develop an effective online algorithm for generating waist and arm trajectories for humanoid robots having a waist joint. The waist trajectory generation problem is formulated into a Quadratic Programming problem. The Receding Horizon Control technique is used to make the algorithm relative faster to integrate it into online control systems and have the ability to confront perturbations. The arm swinging motion is then generated to eliminate the angular momentum generated by the swinging leg and the waist motion. Finally, we apply our algorithm on RENBO, which is the humanoid robot developed in our lab. The experimental result and analysis demonstrate that the algorithm can generate continuous and smooth waist and arm trajectory, making the resulting leg motion much smoother.

关键词： Legged locomotion Trajectory Humanoid robots Hip Generators Torso

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Repulsive reaction vector generator for whole-arm collision avoidance of 7-DoF redundant robot manipulator

Repulsive reaction vector generator for whole-arm collision ...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Ren C. Luo Meng-Chu Ko Yi-Ting Chung Raja Chatila Center for Intelligent Robotics and Automation Research National Taiwan University Taipei Taiwan UPMC/ISIR 4 place Jussieu Paris cedex 05 France

ISBN: (纸本)9781479957378

In this paper, we propose a repulsive reaction vector generator (RVG) for whole-arm collision avoidance of 7-DoF redundant robot manipulator, and elucidate the case of spherical-revolute-spherical (S-R-S) type manipulator. Furthermore, this article discusses the obstacle-avoiding function of RVG, in which the generated reaction vector can aid the manipulator to achieve collision avoidance in an intuitive way by taking arm angle as the redundant parameter. First of all, we use six representative points to describe the manipulator, and generate the repulsive vectors according to the relationship between obstacle and the tip, wrist, and arm body of the manipulator, respectively. Next, after the pose of the manipulator, including position, orientation, and arm angle, is rectified by the reaction vector, the command would be sent to proposed vOTG to complete the reactive motion. As a result, the manipulator can achieve intuitive repulsive reaction, active whole-arm collision avoidance, and meanwhile avoiding the minor-risk obstacle by rotating the arm plane. Experimental results with NTU-iCeiRA arm developed in our lab and Microsoft Kinect depth sensor are presented.

关键词： Manipulators Vectors Collision avoidance Wrist Trajectory Generators

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research on the seamless positioning method of an unmanned system based on UWB/IμGNSS fusion for indoor and outdoor

Research on the seamless positioning method of an unmanned s...

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2023 International Conference on Advanced Mechatronic Systems, ICAMechS 2023

作者： Jiang, Tao Wang, Qian Zhang, Linshuai Gu, Shuoxin Xu, Lin Tang, Xianggui Chengdu Univ. of Traditional Chinese Med. Unmanned Syst. Intelligent Percept. Contr. Technol. Eng. Laboratory of Sichuan Province Chengdu University of Information Technology School of Intelligent Medicine Chengdu China Chengdu University of Information Technology School of Automation Chengdu China Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province School of Automation Sichuan Chengdu China

ISBN: (纸本)9798350307474

This paper studies the multi-sensor fusion positioning methods of Ultra-wideband (UWB), Global Navigation Satellite System (GNSS) and Inertial measurement unit (IMU), and constructs a seamless positioning system based on UWB/IμGNSS multi-sensor fusion. In this paper, the indoor location information provided by UWB is fused with IMU location data through extended Kalman filter, which solves the problems of signal loss, track interruption and jump in positioning information when unmanned systems enter indoor environment, and also improves the positioning accuracy of the overall system. In the experiment of the indoor and outdoor scenes of the unmanned vehicle system, the trajectory diagram of each sensor combination positioning mode is counted, and the error of positioning information is analyzed and studied. The results show that the overall positioning error of this method is small, it can provide stable and reliable whole-time positioning information, and achieve the high-precision seamless positioning required by unmanned systems. © 2023 IEEE.

关键词： Extended Kalman filters

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

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Chinese Control and Decision Conference, CCDC

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang

ISBN: (纸本)9781665478977

UAVs have great potential when applied to persistent monitoring, but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path information. Therefore, it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring tasks. In response to the above problems, this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path security. Through the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy, a mathematical model for UAV path planning is established. An improved ant colony algorithm based on the monitoring overdue time (Overdue-aware Ant Colony Optimization, OACO) is designed, and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed, improve the security of the UAV monitoring path, which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： Time-frequency analysis Simulation Mathematical models Entropy Trajectory Safety Security

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Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization

arXiv

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arXiv 2018年

作者： Lundell, Jens Krug, Robert Schaffernicht, Erik Stoyanov, Todor Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Finland AASS Research Center Örebro University Sweden Robotics Learning and Perception lab KTH Royal Institute of Technology Sweden

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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