检索结果-内蒙古大学图书馆

Neural Potential Field for Obstacle-Aware Local Motion Planning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Alhaddad, Muhammad Mironov, Konstantin Staroverov, Aleksey Panov, Aleksandr Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny141701 Russia The Artificial Intelligence Research Institute Moscow105064 Russia The Federal Research Center "Computer Science and Control" Moscow117312 Russia

— Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning. The code for our approach is presented at https://***/cog-isa/NPField together with demo video. © 2023, CC BY.

关键词： Model predictive control

Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

学校读者我要写书评

暂无评论

Decoupling While Coupling: Towards More Accurate Stereo Imag...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingcai Wei Hui Liu Chuang Qian Yi Jia Wangyu Wu Zhishan Li School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool UK School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns.

关键词： Couplings Uncertainty Neural networks Estimation Signal processing Robustness Stereo vision Image restoration Speech processing Autonomous vehicles

Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2023年第2期56卷 270-275页

作者： Zishuo Li Muhammad Umar B. Niazi Changxin Liu Yilin Mo Karl H. Johansson Department of Automation Tsinghua University Beijing 100084 China Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm SE-100 44 Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.

关键词： Estimation and filtering Design of fault tolerant/reliable systems Estimation and fault detection

Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Smaili, Lyes Tang, Zhiqi Berkane, Soulaimane Hamel, Tarek Department of Computer Science and Engineering University of Quebec in Outaouais GatineauQC Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden Department of Electrical Engineering Lakehead University ON Canada France

This paper addresses the problem of autonomous robot navigation in unknown, obstacle-filled environments with second-order dynamics by proposing a Dissipative Avoidance Feedback (DAF). Compared to the Artificial Potential Field (APF), which primarily uses repulsive forces based on position, DAF employs a dissipative feedback mechanism that accounts for both position and velocity, contributing to smoother and more natural obstacle avoidance. The proposed continuously differentiable controller solves the motion-to-goal problem while guaranteeing collision-free navigation by using the robot’s state and local obstacle distance information. We show that the controller guarantees safe navigation in generic n-dimensional environments and that all undesired ω-limit points are unstable under certain controlled curvature conditions. Designed for real-time implementation, DAF requires only locally measured data from limited-range sensors (e.g., LiDAR, depth cameras), making it particularly effective for robots navigating unknown workspaces. Simulations in 2D and 3D environments are conducted to validate the theoretical results and to showcase the effectiveness of our approach. © 2024, CC BY.

关键词： Feedback

Design and Stability Analysis of control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS)

学校读者我要写书评

暂无评论

Design and Stability Analysis of Control System in Multiport...

IEEE Workshop on computers in Power Electronics

作者： Qianxue Xia Suman Debnath Phani R V Marthi Maryam Saeedifard Energy Systems Integration & Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system.

关键词：

Diagnosing and Augmenting Feature Representations in Correctional Inverse Reinforcement Learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lourenço, Inês Bobu, Andreea Rojas, Cristian R. Wahlberg, Bo The Division of Decision and Control Systems Kth Royal Institute of Technology Stockholm Sweden The Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States

Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned features. When the features the robot needs to learn to perform its task are missing or do not generalize well to new settings, the robot will not be able to learn the task the human wants and, even worse, may learn a completely different and undesired behavior. Prior work shows how the robot can detect when its representation is missing some feature and can, thus, ask the human to be taught about the new feature;however, these works do not differentiate between features that are completely missing and those that exist but do not generalize to new environments. In the latter case, the robot would detect misalignment and simply learn a new feature, leading to an arbitrarily growing feature representation that can, in turn, lead to spurious correlations and incorrect learning down the line. In this work, we propose separating the two sources of misalignment: we propose a framework for determining whether a feature the robot needs is incorrectly learned and does not generalize to new environment setups vs. is entirely missing from the robot's representation. Once we detect the source of error, we show how the human can initiate the realignment process for the model: if the feature is missing, we follow prior work for learning new features;however, if the feature exists but does not generalize, we use data augmentation to expand its training and, thus, complete the correction. We demonstrate the proposed approach in experiments with a simulated 7DoF robot manipulator and physical human corrections. Copyright © 2023, The Authors. All rights reserved.

关键词： Manipulators

Zero- Touch Privacy Preserving Provisioning in an Edge-, Fog-, and Cloud Environment

学校读者我要写书评

暂无评论

Zero- Touch Privacy Preserving Provisioning in an Edge-, Fog...

International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Raphael Schermann Simone Bussa Rainer Urian Christian Steger Institute of Technical Informatics Graz University of Technology Graz Austria Department of Control and Computer Engineering Politecnico di Torino Turin Italy Infineon Technologies AG Augsburg Germany

IoT device onboarding, especially in the context of an edge-fog-cloud architecture, still has many challenges to solve. FIDO has already specified a zero-touch onboarding process called FIDO Device Onboarding (FDO) specification. In this paper, we present improvements to the FDO specification regarding performance and privacy. For privacy and security reasons, we show how the URL of the Owner Fog can be hidden from the Rendezvous Server. Further, we replaced the EPID protocol with a promising privacy-preserving protocol called AACKA. We also modified the last phase in the FDO protocol to create a performance improvement.

关键词：

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

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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