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IEEE International Conference on automation Science and Engineering (CASE)

作者： Rajkumar Muthusamy Ville Kyrki Praveen Kumar Muthusamy Tarek Taha Irfan Hussain Yahya Zweiri Domenico Prattichizzo Dongming Gan Robotics Lab Dubai Future Labs Dubai UAE Department of Electrical Engineering and Automation Intelligent Robotics Group Aalto University Finland Khalifa University of Science and Technology Abu Dhabi UAE Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Purdue University USA

Grasp coordination is crucial for performing cooperative object manipulation tasks. Planning cooperative grasps among a group of decentralized robots is a new line of coordination problem that requires robots to simultaneously handle complex and large shaped and sized objects, a new level of difficulty that is rarely addressed in the literature. In this paper, we propose grasp coordination approaches for a decentralized group of robots facing explicit communication and sensing limitations. In particular, a scenario where robots with incomplete knowledge about each other's embodiments further lose the ability to 1. observe others' grasps occluded by the object's shape 2. exchange direct messages due to potential communication degradation resulting from the real-time planning and execution constraints. To tackle such a scenario, we introduce two baseline and two probabilistic approaches that are specifically designed for strict decentralization. The approaches analyze cooperative grasps using traditional grasp quality metrics and estimate cooperative grasps based on the assigned robot's priority. Simulation experiments demonstrate that the probabilistic approaches exhibit superior performance over the baseline approaches, reaching performance close to optimal for both homogeneous and heterogeneous groups. These approaches provide solutions to simulated multi-robot grasp coordination scenarios that have the potential to translate to real-world environments such as logistics, manufacturing, and services.

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Analog Magnetic Sensor-Robotic System for Steel Structure Inspection

Analog Magnetic Sensor-Robotic System for Steel Structure In...

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IEEE/SICE International Symposium on System Integration

作者： Anh Quyen Pham Duc La Hung Manh La AIR-VIET Corporation in Vietnam Kahlert School of Computing University of Utah Salt Lake City UT USA Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk

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Small Sample Learning Mechanism Based on Knowledge-transferring for Fault Diagnosis

Small Sample Learning Mechanism Based on Knowledge-transferr...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Xue Ma Xinyu Wu Chenglin Wen Xiaohui Sun Department of Automation Tsinghua University Beijing China Shenzhen Institutes of Advanced Technology Guangdong Provincial Key Lab of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen China Department of Automation Guangdong University of Petrochemical Technology Maoming China

Deep learning-based methods have been applied in many fields which has received increased attention. It requires large annotated data to train a deep model with good generalization ability. However, it may be very difficult, time-consuming, and even expensive to collect and annotate practical industrial data, for example, for fault diagnosis in industrial systems. To address this issue, a novel small sample learning (SSL) mechanism based on Knowledge-transferring is proposed in this manuscript, which leverages the expert system and deep neural network simultaneously. Firstly, an annotator is constructed based on a belief rule base (BRB) expert system only with available small labeled samples. Then, the left unlabeled samples will be labeled through the annotator to form an augmented training dataset (ATD) with the available small labeled samples, which can realize the data representation of expert knowledge. Besides, a united optimization model based on CNN that combines data and knowledge is built, which has realized the transfer of expert knowledge. Finally, we demonstrate the capability of the proposed method on the dataset collected from the real experimental platform for rotating machinery fault diagnosis.

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Step-by-step Linearized Kalman Filter-Based Fault Detection of Workpiece Machining

Step-by-step Linearized Kalman Filter-Based Fault Detection ...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Xiaohui Sun Xinyu Wu Chenglin Wen Xue Ma Department of Automation Tsinghua University Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Automation Guangdong University of Petrochemical Technology Maoming China

This paper is concerned with step-by-step linearized Kalman filter to deal with multiplicative faults diagnosis problem for class of nonlinear systems. Multiplicative fault variables are defined as parameter variables to be estimated, and linear dynamic models between fault variables and all variables are established. The indirect linear state and measurement models of fault variables and the corresponding step-by-step linearized Kalman filters are established based on current estimated values of all variable and some obtained estimated values of future variables; further, combined with fault variables, a filter is designed for state variables in the linearized system. On basis of EKF, the redisual generation is obtained. The effectiveness of the novel filter is verified in workpiece machining process.

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BRNES: Enabling Security and Privacy-aware Experience Sharing in Multiagent Robotic and Autonomous Systems

arXiv

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arXiv 2023年

作者： Hossain, Md Tamjid La, Hung Manh Badsha, Shahriar Netchaev, Anton The Advanced Robotics and Automation Lab Computer Science and Engineering University of Nevada RenoNV United States Bosch Engineering North America The U.S. Army Corps United States

Although experience sharing (ES) accelerates multiagent reinforcement learning (MARL) in an advisor-advisee framework, attempts to apply ES to decentralized multiagent systems have so far relied on trusted environments and overlooked the possibility of adversarial manipulation and inference. Nevertheless, in a real-world setting, some Byzantine attackers, disguised as advisors, may provide false advice to the advisee and catastrophically degrade the overall learning performance. Also, an inference attacker, disguised as an advisee, may conduct several queries to infer the advisors’ private information and make the entire ES process questionable in terms of privacy leakage. To address and tackle these issues, we propose a novel MARL framework (BRNES) that heuristically selects a dynamic neighbor zone for each advisee at each learning step and adopts a weighted experience aggregation technique to reduce Byzantine attack impact. Furthermore, to keep the agent’s private information safe from adversarial inference attacks, we leverage the local differential privacy (LDP)-induced noise during the ES process. Our experiments show that our framework outperforms the state-of-the-art in terms of the steps to goal, obtained reward, and time to goal metrics. Particularly, our evaluation shows that the proposed framework is 8.32x faster than the current non-private frameworks and 1.41x faster than the private frameworks in an adversarial setting. © 2023, CC BY-NC-SA.

关键词： Multi agent systems

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6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation 12

6DoF-ICTC: A Deep Image Feature Extraction Network Combining...

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12th International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhang, Liming Zhou, Xin Zhu, Baixian Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness. © 2022 IEEE.

关键词： Feature extraction

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A Distributed Deep Learning Approach for A Team of Unmanned Aerial Vehicles for Wildfire Tracking and Coverage

A Distributed Deep Learning Approach for A Team of Unmanned ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Kripash Shrestha Hung Manh La Hyung-Jin Yoon Amazon Robotics Research Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno USA Department of Mechanical Engineering University of Nevada Reno USA

ISBN: (纸本)9781665472616

Recent large wildfires in the United States and the subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there need to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) have been previously used in this problem domain to track and monitor wildfires with approaches such as artificial potential fields and reinforcement learning. Our work aims to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments. We proposed and implemented the Deep Q-Network (DQN) with a state estimator (auto-encoder), then compared it to existing methods including a Q-learning, a Q-learning with experience replay, and a DQN. The proposed DQN with a state estimator outperformed existing deep learning methods in terms of reward maximization and convergence.

关键词： Deep learning Q-learning Computational modeling Fires Autonomous aerial vehicles Vehicle dynamics Task analysis

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A novel method for predicting action switching in continuous motion based on sEMG signals

A novel method for predicting action switching in continuous...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Shi, Xin Zhu, Jiaqing Qin, Pengjie Cui, Haoyang School of Automation Chongqing University Chongqing400044 China Shenzhen Institudes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System ShenZhen518055 China

ISBN: (纸本)9781665436786

As one of the main signals of human movement intention recognition, surface electromyography (sEMG) has received more and more attention. However, due to the non-stationary, non-periodic and chaotic characteristics of the sEMG signal itself, it is difficult to recognize intentions in continuous actions. In practical applications, if the state switching position in continuous motion can be accurately captured, the accuracy of motion intention recognition will be greatly improved. Therefore, this paper proposes a predictive classification method based on multi-channel sEMG signals, and conducts preliminary exploratory experiments, aiming to provide a new idea for the study of action switching in continuous motion. Combining the predictive model with the diagnostic model, and testing the data of 8 objects through online simulation, the average switching delay is 145.5ms. The experiment confirmed the feasibility of the research idea and laid the foundation for the future research of continuous multi-action switching. © 2021 IEEE.

关键词： Switching

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A Multi-model Fusion of LiDAR-inertial Odometry via Localization and Mapping

A Multi-model Fusion of LiDAR-inertial Odometry via Localiza...

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

作者： An Duy Nguyen Chuong Phuoc Le Pratik Walunj Anton Netchaev Thanh Nho Do Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA Graduate School of Biomedical Engineering UNSW Sydney Kensington Campus NSW Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a comprehensive LiDAR-inertial odometry framework featuring robust smoothing and mapping capabilities, effectively correcting LiDAR feature point skewness using an inertial measurement unit (IMU). While the Extended Kalman Filter (EKF) is a common choice for nonlinear motion estimation, its complexity grows when handling maneuvering targets. To overcome this challenge, a new framework that incorporates the Iterated Interactive Multiple Models of Kalman Filter (IMMKF) is given, providing a solution for reliable navigation in dynamic motion and noisy conditions. To ensure map consistency, an ikd-tree that facilitates continuous updates and adaptive rebalance is employed, preserving the map’s integrity. To guarantee the robustness of our approach, it undergoes extensive testing across diverse scales of indoor and outdoor environments. This testing scenario simulates absolute GPS denial. In terms of estimated motion, the new algorithm demonstrates superior accuracy compared to existing approaches. The implementation is openly accessible on GitHub 4 for further exploration.

关键词： Location awareness Technological innovation Laser radar Accuracy Smoothing methods Computational modeling Feature extraction Odometry Kalman filters Testing

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Single Frame Lidar and Stereo Camera Calibration Using Registration of 3D Planes

Single Frame Lidar and Stereo Camera Calibration Using Regis...

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IEEE International Conference on Robotic Computing (IRC)

作者： Ashutosh Singandhupe Hung Manh La Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV 89557 USA

ISBN: (纸本)9781665434171

This work focuses on finding the extrinsic parameters (rotation and translation) between the lidar and the stereo camera setups. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both the sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The planes extracted from the sensor’s data are used as reference data sets to find the relative transformation between the two sensors. We use our proposed method Correntropy Similarity Matrix Iterative Closest Point (CoSM-ICP) algorithm to estimate the relative transformation. In this work, we use a single frame of the point cloud data acquired from the lidar sensor and a single frame from the calibrated Stereo camera point cloud to perform this operation. We evaluate our approach on a simulated dataset since it has the freedom to evaluate under multiple configurations. Through results, we verify our approach under various configurations.

关键词： Point cloud compression Laser radar Three-dimensional displays Transmission line matrix methods Conferences Robot vision systems Cameras

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