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EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments

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EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Sw...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Xin Zhou Jiangchao Zhu Hongyu Zhou Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China Huzhou Institute Zhejiang University Huzhou China

This paper presents a decentralized and asynchronous systematic solution for multi-robot autonomous navigation in unknown obstacle-rich scenes using merely onboard resources. The planning system is formulated under gradient-based local planning framework, where collision avoidance is achieved by formulating the collision risk as a penalty of a nonlinear optimization problem. In order to improve robustness and escape local minima, we incorporate a lightweight topological trajectory generation method. Then agents generate safe, smooth, and dynamically feasible trajectories in only several milliseconds using an unreliable trajectory sharing network. Relative localization drift among agents is corrected by using agent detection in depth images. Our method is demonstrated in both simulation and real-world experiments. The source code is released for the reference of the community.

关键词： Location awareness Systematics Codes Navigation Conferences Robustness Trajectory

VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation

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VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Acc...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Ziming Ding Tiankai Yang Kunyi Zhang Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation Ningbo Institute Ningbo China

Recently, quadrotors are gaining significant attention in aerial transportation and delivery. In these scenarios, an accurate estimation of the external force is as essential as the six degree-of-freedom (DoF) pose since it is of vital importance for planning and control of the vehicle. To this end, we propose a tightly-coupled Visual-Inertial-Dynamics (VID) system that simultaneously estimates the external force applied to the quadrotor along with the six DoF pose. Our method builds on the state-of-the-art optimization-based Visual-Inertial system [1], with a novel deduction of the dynamics and external force factor extended from VIMO [2]. Utilizing the proposed dynamics and external force factor, our estimator robustly and accurately estimates the external force even when it varies widely. Moreover, since we explicitly consider the influence of the external force, when compared with VIMO [2] and VINS-Mono [1], our method shows comparable and superior pose accuracy, even when the external force ranges from neglectable to significant. The robustness and effectiveness of the proposed method are validated by extensive real-world experiments and application scenario simulation. We will release an open-source package of this method along with datasets with ground truth force measurements for the reference of the community.

关键词： Force measurement Torque Force Dynamics Pose estimation Estimation Transportation

EVA-Planner: Environmental Adaptive Quadrotor Planning

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EVA-Planner: Environmental Adaptive Quadrotor Planning

IEEE International Conference on Robotics and Automation (ICRA)

作者： Lun Quan Zhiwei Zhang Xingguang Zhong Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages¹ .

关键词： Adaptation models Trajectory planning Heuristic algorithms Predictive models Prediction algorithms Robustness Planning

Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Xue, Shibei Zhang, Guofeng State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises. © 2022, CC BY.

关键词： Optimization

Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

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Security and Communication Networks 2023年第1期2023卷

作者： Li, Chao Fu, Yuhan Zhang, Rui Liang, Hai Wang, Chonghua Li, Junjian Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China COMAC Shanghai Aircraft Manufacturing Co. Ltd Beijing100005 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. © 2023 Chao Li et al.

关键词： Internet of things

Robust Optimization for Quantum Reinforcement Learning control using Partial Observations

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

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arXiv 2021年

作者： Wang, Qianhao He, Botao Xun, Zhiren 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 School of Automation Nanjing Institute of Technology Nanjing211112 China

Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is unfriendly to non-professional operators, as the experimental results show in Sec. VI-C. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment which the operator is interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code of our system to benefit related researches. © 2021, CC BY-NC-ND.

关键词： Remote control

Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

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Tactile-Based Object Pose Estimation Employing Extended Kalm...

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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