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IEEE International Conference on robotics and Automation (ICRA)

作者： Jian Wen Xuebo Zhang Qingchen Bi Zhangchao Pan Yanghe Feng Jing Yuan Yongchun Fang Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Systems Engineering National University of Defense Technology Changsha China

Local planning is one of the key technologies for mobile robots to achieve full autonomy and has been widely investigated. To evaluate mobile robot local planning approaches in a unified and comprehensive way, a mobile robot local planning benchmark called MRPB 1.0 is newly proposed in this paper. The benchmark facilitates both motion planning researchers who want to compare the performance of a new local planner relative to many other state-of-the-art approaches as well as end users in the mobile robotics industry who want to select a local planner that performs best on some problems of interest. We elaborately design various simulation scenarios to challenge the applicability of local planners, including large-scale, partially unknown, and dynamic complex environments. Furthermore, three types of principled evaluation metrics are carefully designed to quantitatively evaluate the performance of local planners, wherein the safety, efficiency, and smoothness of motions are comprehensively considered. We present the application of the proposed benchmark in two popular open-source local planners to show the practicality of the benchmark. In addition, some insights and guidelines about the design and selection of local planners are also provided. The benchmark website [1] contains all data of the designed simulation scenarios, detailed descriptions of these scenarios, and example code.

关键词： Measurement Industries Service robots Dynamics Benchmark testing Planning Safety

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A Modified Multi-Pedestrian Tracking System

A Modified Multi-Pedestrian Tracking System

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第三十八届中国控制会议

作者： Yuan Gong Jianning Chi Xiaosheng Yu Chengdong Wu Zixi Jia Faculty of Robot Science and Engineering Northeastern University

The intelligent vehicle driverless technology has become a very hot topic in the past two decades. To solve the road safety problem, the multi-target tracking system has attracted more and more attention in recent study since it can detect and track multiple objects in traffic scene so as to help the whole driving system plan the safe route. In this work, a novel multipedestrian tracking system based on deep neural network is proposed to improve the tracking efficiency while providing high recognition accuracy. The proposed tracking system consists of two parts: pedestrian detector and target tracker. We refer to the structure of SSD detection network to design our own detection network, replacing the traditional pooling layers by image convolution operation implemented in complex frequency domain. Then, a deep appearance model is trained to track the detected targets by their similarity between the continuous frames. To evaluate the performance of our proposed system, we implement the system on M OT16 benchmark. The detector can improve tracking accuracy by up to 9.3% and the tracker updates at a rate of 138 Hz, our extensions reducing the number of identity switches by 16.2%. The experimental results show that the proposed multi-pedestrian tracking system provides better real-time performance and accuracy than the state-of-the-art methods, which can provide more accurate object information for the following auto-driving system with higher safety.

关键词： multi-pedestrian tracking system pedestrian detector target tracker real-time performance tracking accuracy

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Design and Validation of A Novel Adaptive Cruise Control Law for a Platoon Of Vehicles

Design and Validation of A Novel Adaptive Cruise Control Law...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Amr Farag Ahmed Hussein Omar M. Shehata Elsayed I. Morgan Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University Cairo Egypt IAV GmbH Berlin Germany Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728182261

ISBN: (纸本)9781728182278

Cooperative driving has attracted the attention of many research centers around the world as a continuation towards a world with fully autonomous vehicles as full autonomy requires cooperation and coordination among vehicles as well as the ability to move automatically. One of the well-known applications of the cooperative driving is vehicle platooning, where vehicles drive in a single lane with close distance gap for fuel reduction. The formation of platooning is a matter of vehicular communication as well as vehicle control. In this paper, a decentralized novel model-free controller is proposed based on the analysis of longitudinal vehicle and platoon dynamics. The proposed controller has been tested for stability in both analytical and graphical ways and it was tested on a nonlinear model in different simulation platforms; MATLAB/SIMULINK and on CARLA simulator and compared to Sliding Mode Control (SMC) to validate its performance. The results shown that the proposed controller is asymptotically stable and has an accuracy close to SMC but outperforming in computational efficiency.

关键词： Mathematical model Vehicle dynamics Task analysis Drag Aerodynamics Force Engines

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A robust laser-inertial odometry and mapping method for large-scale highway environments

arXiv

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arXiv 2020年

作者： Zhao, Shibo Fang, Zheng Li, HaoLai Scherer, Sebastian Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

In this paper, we propose a novel laser-inertial odometry and mapping method to achieve real-time, low-drift and robust pose estimation in large-scale highway environments. The proposed method is mainly composed of four sequential modules, namely scan pre-processing module, dynamic object detection module, laser-inertial odometry module and laser mapping module. Scan pre-processing module uses inertial measurements to compensate the motion distortion of each laser scan. Then, the dynamic object detection module is used to detect and remove dynamic objects from each laser scan by applying CNN segmentation network. After obtaining the undistorted point cloud without moving objects, the laser-inertial odometry module uses an Error State Kalman Filter to fuse the data of laser and IMU and output the coarse pose estimation at high frequency. Finally, the laser mapping module performs a fine processing step and the "Frame-to-Model" scan matching strategy is used to create a static global map. We compare the performance of our method with two state-of-the-art methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset. Experiment results show that our method performs better than the state-of-the-art methods in real highway environments and achieves competitive accuracy on the KITTI dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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A New Approach for Training on EMG-based Prosthetic Hand Control

A New Approach for Training on EMG-based Prosthetic Hand Con...

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IEEE Global Conference on Life sciences and Technologies (LifeTech)

作者： Taro Shibanoki Go Nakamura Toshio Tsuji Kazuya Hashimoto Takaaki Chin The Graduate School of Science and Engineering Ibaraki University Ibaraki Japan Robot Rehabilitation Center The Hyogo Institute of Assistive Technology Kobe Japan Faculty of Engineering Hiroshima University Hiroshima Japan

ISBN: (数字)9781728170633

ISBN: (纸本)9781728170640

This paper proposes a training system for myoelectric prosthetic hand control in dual-arm operation. The system can train muscular cooperation timing of both arms. Training experiments under two conditions were conducted with an experienced participant and the results showed that the user could generate accurate EMG patterns at the specified timing.

关键词： electromyogram (EMG) training system virtual reality (VR) prosthetic hand dual-arm operation Prosthetic hand Training Functional training

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Two-DOF Speed Control of Permanent Magnet Synchronous Machine with a novel Parameter Identification Method

Two-DOF Speed Control of Permanent Magnet Synchronous Machin...

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第三十九届中国控制会议

作者： Mingjin Hu Wei Hua Xudong Wu Chengdong Wu Dazhi Wang Zhentao Liu Xiaofang Yuan Wennong Zhang School of Electrical Engineering Southeast University Faculty of Robot Science and Engineering Northeastern University College of Information Science and Engineering Northeastern University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems College of Electrical and Information Engineering Hunan University

In this paper, a two-degree-of-freedom（2-DOF） speed control scheme for permanent magnet synchronous machines is developed. Both the reference-tracking and disturbance rejection performance are taken into account and only a single parameter related to expected bandwidth determines the dynamics. To improve the performance of speed control, a novel identification method for mechanical parameters is also proposed. The effectiveness of the proposed method is verified by simulations.

关键词： Speed Control two-degree-of-freedom permanent magnet synchronous machine parameter identification

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Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

Image-guided Breast Biopsy of MRI-visible Lesions with a Han...

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International Symposium on Medical robotics (ISMR)

作者： Marta Lagomarsino Vincent Groenhuis Maura Casadio Marcel K. Welleweerd Françoise J. Siepel Stefano Stramigioli Robotics and Mechatronics (RAM) Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands University of Genoa Genoa Italy Human-Robot Interfaces and Physical Interaction (HRII) Laboratory Istituto Italiano di Tecnologia Genoa Italy Politecnico di Milano Milan Italy

ISBN: (纸本)9781665406239

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient’s skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.

关键词： Target tracking Biopsy Phantoms Breast Needles Real-time systems Skin

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Fast-dynamic-vision: Detection and tracking dynamic objects with event and depth sensing

arXiv

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arXiv 2021年

作者： He, Botao Li, Haojia Wu, Siyuan Wang, Dong Zhang, Zhiwei Dong, Qianli Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Ningbo315000 China School of Automation Nanjing Institute of Technology Nanjing211112 China Faculty of Robot Science and Engineering Northeastern University Shenyang110207 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

The development of aerial autonomy has enabled aerial robots to fly agilely in complex environments. However, dodging fast-moving objects in flight remains a challenge, limiting the further application of unmanned aerial vehicles (UAVs). The bottleneck of solving this problem is the accurate perception of rapid dynamic objects. Recently, event cameras have shown great potential in solving this problem. This paper presents a complete perception system including ego-motion compensation, object detection, and trajectory prediction for fast-moving dynamic objects with low latency and high precision. Firstly, we propose an accurate ego-motion compensation algorithm by considering both rotational and translational motion for more robust object detection. Then, for dynamic object detection, an event camera-based efficient regression algorithm is designed. Finally, we propose an optimization-based approach that asynchronously fuses event and depth cameras for trajectory prediction. Extensive real-world experiments and benchmarks are performed to validate our framework. Moreover, our code will be released to benefit related researches. © 2021, CC BY-NC-ND.

关键词： Object recognition

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Single-path Bit Sharing for Automatic Loss-aware Model Compression

arXiv

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arXiv 2021年

作者： Liu, Jing Zhuang, Bohan Chen, Peng Shen, Chunhua Cai, Jianfei Tan, Mingkui The School of Software Engineering South China University of Technology China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China The School of Computer Science the University of Adelaide Australia The Faculty of Information Technology Monash University Australia The College of Computer Science and Technology Zhejiang University Hangzhou China

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance. To address this, performing pruning and quantization jointly is essential. Nevertheless, how to make a trade-off between pruning and quantization is non-trivial. Moreover, existing compression methods often rely on some pre-defined compression configurations (i.e., pruning rates or bitwidths). Some attempts have been made to search for optimal configurations, which however may take unbearable optimization cost. To address these issues, we devise a simple yet effective method named Single-path Bit Sharing (SBS) for automatic loss-aware model compression. To this end, we consider the network pruning as a special case of quantization and provide a unified view for model pruning and quantization. We then introduce a single-path model to encode all candidate compression configurations, where a high bitwidth value will be decomposed into the sum of a lowest bitwidth value and a series of re-assignment offsets. Relying on the single-path model, we introduce learnable binary gates to encode the choice of configurations and learn the binary gates and model parameters jointly. More importantly, the configuration search problem can be transformed into a subset selection problem, which helps to significantly reduce the optimization difficulty and computation cost. In this way, the compression configurations of each layer and the trade-off between pruning and quantization can be automatically determined. Extensive experiments on CIFAR-100 and ImageNet show that SBS significantly reduces computation cost while achieving promising performance. For example, our SBS compressed MobileNetV2 achieves

关键词： Encoding (symbols)

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A Bi-directional Interactive System of Sign Language and Visual Speech Based on Portable Devices

A Bi-directional Interactive System of Sign Language and Vis...

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IEEE International Conference on robotics and Biomimetics

作者： Fei Wang Shizhuo Sun Yanjun Liu Faculty of Robot Science and Engineering Northeastern University Shenyang China School of Computer Science and Engineering Faulty of Robot Science and Engineering Northeastern University Shenyang China

At present, the natural communication between deaf and normal people is a major challenge both in theoretical research and application. In addition, in the field of Human-Machine Interaction, there is little work on bi-directional communication based on sign language and visual speech. In this paper, a portable bi-directional interactive system of sign language and visual speech was proposed to help deaf people communicate naturally under certain circumstances. In the section of modeling, Network in Network model was employed to classify sign language words, while the method using a network combined DenseNet and LSTM together was used for lip reading. By wearing the portable device, people can achieve bi-directional translation and communication of visual speech and sign language. We built our own Chinese sign language database containing more than 100 categories of words and conducted experiments in the context of the airport for experimental verification. Eventually, the average time of each round of conversation is within 15 seconds, faster than normal communication without system assistance, which verified the effectiveness and advantage of the purposed system.

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