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Ocean Studies (ICOS), International Conference on

作者： Vladimir Filaretov Alexey Katsurin Laboratory of Robotic Systems Laboratory of Automation and Robotics Institute of Automation and Control Processes FEB RAS Vladivostok State University Vladivostok Russia Department of Automation and Robotics Laboratory of Intelligent Information Systems for Marine Robots Far Eastern Federal University M.D. Ageev Institute for Marine Technology Problems FEB RAS Vladivostok Russia

The paper discusses methods of coordinated operation of two autonomous underwater vehicles, one of which is equipped with a multi-link manipulator and a vision system, and the second - auxiliary - only a vision system. These methods ensure the joint operation of two autonomous underwater vehicles both in the mode of semi-automatic positional remote control, and in the mode of automatic execution of operations with various objects. At the same time, work is considered in situations when the vision system of the main underwater vehicle does not allow observing the object of work, which is nevertheless located in the working area of the manipulator. In this case, the auxiliary inspection underwater vehicle with its own vision system is used to monitor the object. In the semi-automatic remote control mode, the manipulator automatically takes into account the current spatial orientation of the optical axis of the vision system of the inspection underwater vehicle, as well as the spatial orientations of both underwater vehicles at yaw angles in the absolute coordinate system. And in automatic mode, an accurate transfer of the spatial location and orientation of the object, which are determined using the vision system of the inspection underwater vehicle, is additionally provided to the associated coordinate system of the main underwater vehicle. It is possible to identify and eliminate errors that occur during the operation of the vision system and navigation systems of both underwater vehicles. The results of experimental studies have confirmed the operability of the proposed methods.

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

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

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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An Improved Method for Small Target Recognition Based on Faster RCNN 17th

An Improved Method for Small Target Recognition Based on Fas...

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17th Chinese intelligent Systems Conference, CISC 2021

作者： Liu, Qun-po Wang, Qi-jing Hanajima, Naohiko Su, Bo Department of Robotics Engineering Henan Polytechnic University 2001 Century Avenue Jiaozuo Henan454003 China Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment 2001 Century Avenue Jiaozuo Henan454003 China College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran-shi Hokkaido050-8585 Japan

ISBN: (纸本)9789811663239

As one of the most challenging tasks in the field of deep learning, target detection has achieved remarkable results. The existing algorithms have achieved excellent results in the detection of large and medium-sized targets, but the detection accuracy of small targets is not ideal because of their small size and weak features. A small target recognition method based on improved Faster_Rcnn that aims at the problem of low accuracy in small target detection is proposed in this paper. Since the ROI Pooling method in Faster Rcnn might cause quantization errors in the operation process, resulting in inaccurate positioning and other problems during detection, an improved ROI Align method is adopted to eliminate the quantization errors. The cell public data set is used for the experiment, the results show that the average recognition accuracy of the recognition model with improved algorithm is 92.11%, which is an increase of 7%. The method also obtains good effect on NWPU VHR-10 data set, which indicates that it has good universality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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NeRF in robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in robotics and The Advance of NeRF in robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： robotics

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An Assist-as-Needed Variable Admittance controller for Adaptive Support of Mobile Walking Assistive Robots

An Assist-as-Needed Variable Admittance Controller for Adapt...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Xinxing Chen Yue Zheng Zongming Su Yuquan Leng Jian Huang Chenglong Fu Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Technology Planning Department Huawei Technologies Shenzhen China Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Mobile walking assistive robots have been widely used for monitoring patients’ gait patterns and providing support to prevent falls. Traditional approaches often separate the operational modes of these robots into human-following and supporting modes, which can lead to discontinuities in assistance and potentially compromise safety. This paper proposes a novel assist-as-needed control strategy that incorporates a Balance Index (BI) to dynamically adjust the robot’s support based on the user’s balance capability. The BI is calculated using the interaction force between the user and the robot along the z-axis and the gait symmetry index (GSI). The proposed variable admittance controller modifies its parameters based on the BI, ensuring seamless transitions between compliance and support modes. Experimental validation was conducted with a healthy subject walking both with and without a knee fixation brace. The results demonstrate that the robot provides minimal interference during natural gait and increased support during impaired gait, validating the effectiveness of the proposed control strategy in enhancing user safety and promoting natural walking patterns.

关键词： Legged locomotion Force Stability criteria Interference Assistive robots User experience Safety Admittance Indexes Robots

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3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion

arXiv

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3D scene flow by estimating optical flow and disparity separately. Learning 3D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this paper, the generated dense depth map is utilized to obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow from 2D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2D pixels into the 3D space. Outliers in the generated 3D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3D scene flow. The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation. © 2022, CC BY.

关键词： Three dimensional computer graphics

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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IEEE International Conference on robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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Development of a Sphere Motion Measurement System and Evaluation of the Roller Arrangement Requirement

Development of a Sphere Motion Measurement System and Evalua...

引用

International Conference on Ubiquitous Robots (UR)

作者： Shota Chikushi Kenji Kimura Jonghyun Ahn Kazuo Ishii Department of Robotics Kindai University Higashi-Hiroshima-shi Hiroshima Japan Department of Control Engineering National Institute of Technology Matsue College Matsue-shi Shimane Japan Department of Intelligent Mechanical Engineering Hiroshima Institute of Technology Hiroshima-shi Hiroshima Japan Department of Human Intelligence Systems Kyushu Institute of Technology Kitakyushu-shi Fukuoka Japan

To conserve human resources and labor, there is a demand for robots to transport objects. Compared to previous object transport methods, sphere transport by a mobile robot is efficient for transporting objects without loading them and while on the ground. However, previous research on sphere transport has not clarified the methodology for designing and controlling sphere transport based on kinematic and dynamic properties. Therefore, this study attempts to develop a rollersphere motion measurement method for focusing on the roller angular velocity and to evaluate the effectiveness of the roller arrangement requirement. In particular, this study evaluates the “roller arrangement requirement,” which is necessary for roller arrangement (position/orientation) considering kinematic characteristics in sphere transport. Experiments have shown the effectiveness of the roller arrangement requirement with a novel roller-sphere motion measurement method.

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Event- Triggered based control of Robotic Arm Under Denial of Service

Event- Triggered based Control of Robotic Arm Under Denial o...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Abdulrazaq Nafiu Abubakar Jamilu Umar Yahaya Muhammad Ma'aruf Muhammad Faizan Mysorewala Abdul-Wahid A. Saif Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum and _ Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

In this paper, we studied the finite-time stability of robotic arm systems against Denial of Service attacks, when the link between the plant and the controller is often interrupted. In order to do this, a method is suggested that relies on an event-triggered mechanism which is based on the rate of change of the Lyapunov function, that keeps the system finite-time stable while simultaneously reducing the quantity of data interchange in the network. The suggested event-triggered finite-time controller's vulnerability to Denial of Service attacks is then assessed, and sufficient limitations on the length of a Denial of Service are identified to ensure the system's stability within a specified time frame. Lastly, a simulation study is used to show how effective the suggested strategy is.

关键词： Time-frequency analysis Service robots Manipulators Sampling methods Stability analysis Logic design Lyapunov methods

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Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

arXiv

引用

arXiv 2022年

作者： Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point’s movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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