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AutoTrans: A Complete Planning and control Framework for Autonomous UAV Payload Transportation

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arXiv 2023年

作者： Li, Haojia Wang, Haokun Feng, Chen Gao, Fei Zhou, Boyu Shen, Shaojie School of Artificial Intelligence Sun Yat-Sen University Zhuhai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community. © 2023, CC BY-NC-ND.

关键词： Trajectories

PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

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arXiv 2023年

作者： Feng, Chen Li, Haojia Gao, Fei Zhou, Boyu Shen, Shaojie School of Artificial Intelligence Sun Yat-Sen University Zhuhai China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Industrail Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomously generate paths for high 3D reconstruction quality. We obtain inspiration from humans can roughly infer the complete construction structure from partial observation. Hence, we devise a surface prediction module (SPM) to predict the coarse complete surfaces of the target from the current partial reconstruction. Then, the uncovered surfaces are produced by online volumetric mapping waiting for observation by UAV. Lastly, a hierarchical planner plans motions for 3D reconstruction, which sequentially finds efficient global coverage paths, plans local paths for maximizing the performance of Multi-View Stereo (MVS), and generates smooth trajectories for image-pose pairs acquisition. We conduct benchmarks in the realistic simulator, which validates the performance of PredRecon compared with the classical and state-of-the-art methods. The open-source code is released at https://***/HKUST-Aerial-robotics/PredRecon. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

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PredRecon: A Prediction-boosted Planning Framework for Fast ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Chen Feng Haojia Li Fei Gao Boyu Zhou Shaojie Shen Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Industrail Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China School of Artificial Intelligence Sun Yat-Sen University Zhuhai China

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An all-solid-state ion-selective electrode for dopamine determination

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An all-solid-state ion-selective electrode for dopamine dete...

IET Doctoral Forum on Biomedical Engineering, Healthcare, robotics and Artificial Intelligence 2018 (BRAIN 2018)

作者： Siyuan Ma Cheng He You Wang Zhiyuan Luo Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Computer Learning Research Centre Royal Holloway University of London Egham Hill Egham Surrey TW200EX UK

An all-solid-state ion-selective electrode (ISE) was developed for potentiometric detection of dopamine. A gold disk covered by poly(3, 4-ethylenedioxythiophene) doped with poly(styrenesulfonate) (PEDOT/PSS) was used as the work electrode, a polymeric membrane containing β-cyclodextrin as an ionophore and potassium tetrakis(4-chlorophenyl) borate as ionic additive was immobilized on the conduct polymer layer as the dopamine selective membrane. The ISE was sensitive to dopamine linearly from 3×10-5 to 10-1M with a slope of 56.60+1.28mV/decade. And the detection limit was 2.2×10- 5 M. It performed stably in a pH range of 6-8 and a temperature range of 35-41°C. The dynamic response, reproducibility and stability as well as the sensitivity were investigated.

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An Efficient Trajectory Planner for Car-like Robots on Uneven Terrain

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arXiv 2023年

作者： Xu, Long Chai, Kaixin Han, Zhichao Liu, Hong Xu, Chao Cao, Yanjun Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'An710049 China School of information and electrical engineering Hangzhou City University Hangzhou310015 China

Autonomous navigation of ground robots on uneven terrain is being considered in more and more tasks. However, uneven terrain will bring two problems to motion planning: how to assess the traversability of the terrain and how to cope with the dynamics model of the robot associated with the terrain. The trajectories generated by existing methods are often too conservative or cannot be tracked well by the controller since the second problem is not well solved. In this paper, we propose terrain pose mapping to describe the impact of terrain on the robot. With this mapping, we can obtain the SE(3) state of the robot on uneven terrain for a given state in SE(2). Then, based on it, we present a trajectory optimization framework for car-like robots on uneven terrain that can consider both of the above problems. The trajectories generated by our method conform to the dynamics model of the system without being overly conservative and yet able to be tracked well by the controller. We perform simulations and real-world experiments to validate the efficiency and trajectory quality of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Least Mean p-Power Extreme Learning Machine for Obstacle Avoidance of a Mobile Robot

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Least Mean p-Power Extreme Learning Machine for Obstacle Avo...

International Joint Conference on Neural Networks

作者： Jing Yang Pengpeng Chen Hai-Jun Rong Badong Chen Institute of Control Engineering School of Electronic and Information Engineering Xi'an Jiaotong University China 710049 State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Xi'an Jiaotong University China 710049 Institute of Artificial Intelligence and Robotics School of Electronic and Information Engineering Xi'an Jiaotong University China 710049

ISBN: (纸本)9781509006212

This paper proposes an obstacle avoidance method for navigation of a mobile robot in uncertain environments based on a novel neural learning algorithm, namely least mean p-norm extreme learning machine (LMP-ELM) and Q-learning. The proposed obstacle avoidance method comprises of two behavior modules, Viz., an avoidance behavior and goal-seeking behavior. At the learning phase, the two modules are independently designed using the proposed LMP-ELM and Q-Learning. And then they are combined to navigate the mobile to the goal position without colliding with obstacles based on a switching function at the running phase. The LMP-ELM is used to realize the state-action mapping of the Q-learning. In the novel LMP-ELM, the computationally simple extreme learning machine architecture is maintained but a novel error criterion, namely the least mean p-power (LMP) error criterion provides a mechanism to update the output weights sequentially. The LMP error criterion aims to minimize the mean p-power of the error that is the generalization of the mean square error criterion used in the ELM. The effectiveness of the proposed method is verified by a series of simulations.

关键词： Mobile robots Robot kinematics Collision avoidance Robot sensing systems Force

Fully Robotized 3D Ultrasound Image Acquisition for Artery

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Fully Robotized 3D Ultrasound Image Acquisition for Artery

IEEE International Conference on robotics and Automation (ICRA)

作者： Mingcong Chen Yuanrui Huang Jian Chen Tongxi Zhou Jiuan Chen Hongbin Liu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Biomedical Engineering and Imaging Sciences King's College London London UK

Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.

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Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer

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arXiv 2022年

作者： Shi, Hao Jiang, Qi Yang, Kailun Yin, Xiaoting Wang, Ze Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation the National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd Shanghai201210 China

Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide sufficient coverage. Nevertheless, from a spatiotemporal perspective, it is possible to obtain information beyond the camera’s physical FoV from past video streams. In this paper, we propose the concept of online video inpainting for autonomous vehicles to expand the field of view, thereby enhancing scene visibility, perception, and system safety. To achieve this, we introduce the FlowLens architecture, which explicitly employs optical flow and implicitly incorporates a novel clip-recurrent transformer for feature propagation. FlowLens offers two key features: 1) FlowLens includes a newly designed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated over time. 2) It integrates a multi-branch Mix Fusion Feed Forward Network (MixF3N) to enhance the precise spatial flow of local features. To facilitate training and evaluation, we derive the KITTI360 dataset with various FoV mask, which covers both outer- and inner FoV expansion scenarios. We also conduct both quantitative assessments and qualitative comparisons of beyond-FoV semantics and beyond-FoV object detection across different models. We illustrate that employing FlowLens to reconstruct unseen scenes even enhances perception within the field of view by providing reliable semantic context. Extensive experiments and user studies involving offline and online video inpainting, as well as beyond-FoV perception tasks, demonstrate that FlowLens achieves state-of-the-art performance. The source code and dataset are made publicly available at https://github. com/MasterHow/FlowLens. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

Exploring Event-based Human Pose Estimation with 3D Event Representations

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arXiv 2023年

作者： Yin, Xiaoting Shi, Hao Chen, Jiaan Wang, Ze Ye, Yaozu Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics Hunan University Changsha410012 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd. Shanghai201210 China

Human pose estimation is a fundamental and appealing task in computer vision. Although traditional cameras are commonly applied, their reliability decreases in scenarios under high dynamic range or heavy motion blur, where event cameras offer a robust solution. Predominant event-based methods accumulate events into frames, ignoring the asynchronous and high temporal resolution that is crucial for distinguishing distinct actions. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC aggregates events within concise temporal slices at identical positions, preserving their 3D attributes along with statistical information, thereby significantly reducing memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. Our methods are tested on the DHP19 public dataset, MMHPSD dataset, and our EV-3DPW dataset, with further qualitative validation via a derived driving scene dataset EV–JAAD and an outdoor collection vehicle. Our code and dataset have been made publicly available at https://***/MasterHow/EventPointPose. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D modeling