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Path Tracking for Intelligent Vehicle Considering Steering Delay Characteristic

Path Tracking for Intelligent Vehicle Considering Steering D...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Haiyang Huang Xiaomei Yue Guanghui Guo Mingchen Sun Guicun Guo Intelligent Driving Development Department FAW -Volkswagen Co. Ltd. Changchun China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

As a crucial process in autonomous driving systems, path tracking control determines the safety and ride comfort of intelligent vehicles. However, the response delay of the steering actuator is commonly not considered in control tracking algorithms. The steering delay may directly impact the control accuracy, particularly when the target path curvature changed rapidly. This may lead to lower control precision and system oscillations, resulting in vehicle instability. Therefore, this paper proposed a Linear Quadratic Regulator (LQR) control algorithm considering steering delay characteristic that can avoid such problems. Firstly, the response delay characteristics of steering actuator is modelled as first order delay model and the model parameter is identified. Secondly, the actuator model is integrated into the vehicle model, and the optimization objective function of the discrete LQR in the infinite time domain is constructed. Then the optimal control feedback can be solved from the Riccati equation. Next, by designing the control feedforward item, the steady lateral error can be eliminated, and realizing the path tracking control considering steering delay characteristic. Finally, in the simulation environment, the control accuracy of the proposed steering delay LQR method, is compared with the original LQR and MPC algorithm under various test conditions to validate the effectiveness of the proposed approach.

关键词： Actuators Accuracy Target tracking Service robots Optimal control Mathematical models Delays Steady-state Feedforward systems Time-domain analysis

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Adaptive Neural Network Based Fractional Order Control of Unmanned Aerial Vehicle 18th

Adaptive Neural Network Based Fractional Order Control of U...

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18th international conference on Informatics in Control, automation and robotics, ICINCO 2021

作者： Maurya, Heera Lal Singh, Padmini Yogi, Subhash Chand Behera, Laxmidhar Verma, Nishchal K. Department of Electrical Engineering Indian Institute of Technology Kanpur Uttar Pradesh Kanpur208016 India

ISBN: (纸本)9783031264733

An unmanned Aerial Vehicle (UAV) is a highly non-linear unstable system. In this work using fractional order calculus, a novel fractional order dynamics of UAV is proposed. The concept of fractional order depicts the more realistic behavior of UAVs. For proposed fractional order model, a fractional order sliding mode controller (SMC) is designed such that the desired path can be achieved by the UAV in finite-time. In addition to this an adaptive neural network (ANN) based approximation function is attached to the controller having the quality of optimal hidden nodes. The weight associated to the hidden nodes achieves the optimal values. The integration of ANN based function with fractional order SMC achieves better results compared to fractional order SMC alone. Stability analysis is given for the fractional order SMC using fractional Lyapunov method. The same Lyapunov function has been used for finding the adaptive law for estimating the unknown dynamics of the system. Simulations have been done for position and attitude tracking of UAV using ANN based fractional order SMC to demonstrate the advantage of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Lyapunov methods

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DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

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IEEE international conference on robotics and automation (ICRA)

作者： Jiawen Zhu Huayi Tang Zhi-Qi Cheng Jun-Yan He Bin Luo Shihao Qiu Shengming Li Huchuan Lu Dalian University of Technology Dalian China Carnegie Mellon University PA USA DAMO Academy Alibaba Group Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code is available at https://***/bearyi26/DCPT.

关键词： Adaptation models Visualization Codes Machine vision Logic gates Benchmark testing Autonomous aerial vehicles

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Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming

Order Matters: Generating Progressive Explanations for Plann...

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IEEE international conference on robotics and automation (ICRA)

作者： Zakershahrak, Mehrdad Marpally, Shashank Rao Sharma, Akshay Gong, Ze Zhang, Yu Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA

ISBN: (纸本)9781728190778

Prior work on generating explanations in a planning context has focused on providing the rationale behind an AI agent's decision-making. While these methods offer the right explanations, they fail to heed the cognitive requirement of understanding an explanation from the explainee or human's perspective. In this work, we set out to address this issue by considering the order for communicating information in an explanation, or the progressiveness of making explanations. Progression is the notion of building complex concepts on simpler ones, which is known to benefit learning. In this work, we investigate a similar effect when an explanation is composed of multiple parts that are communicated sequentially. The challenge here lies in determining the order for receiving different parts of an explanation that would assist in understanding. Given the sequential nature, a formulation based on goal-based MDP is presented. The reward function of this MDP is learned via inverse reinforcement learning based on training data. We evaluated our approach in an escape-room domain to demonstrate its effectiveness. Upon analyzing the results, it revealed that the desired order arises strongly from both domain-dependent and independence features. This result confirmed our expectation that the process of understanding an explanation for planning tasks was progressive and context dependent. We also showed that the explanations generated using the learned rewards achieved better task performance and simultaneously reduced cognitive load. These results shed light on designing explainable robots across various domains.

关键词： automation conferences Decision making Buildings Training data Reinforcement learning Planning

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Self-Data-Driven Modeling for Fault Diagnosis of RV Reducers in Industrial Robots

Self-Data-Driven Modeling for Fault Diagnosis of RV Reducers...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Naipeng Li Huitong Li Yang Tan Huan Liu Yaguo Lei Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing Systemn Xi 'an Jiaotong University Xi'an China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The health state of RV reducers plays a significant role in reliability and productivity of industrial robots. Due to the complicated structure and nonlinear dynamic behavior, it is difficult to construct a precise dynamic model of RV reducers. Aiming at the wear degradation monitoring of RV reducers, this paper develops a self-data-driven modeling method based on only proprioceptive sensor signals of robotic joints, including the torque, position and velocity. The baseline model of the ob-servation traj ectories is constructed by statistically analyzing the monitoring data under the healthy stage. A nonparametric modeling technique, i.e., functional principal component analy-sis (FPCA), is used to generate the principal components of the model automatically driven by the monitoring data. With the wear degradation of RV reducers, the observation trajectories will deviate far from the baseline model. Based on this phenom-enon, the real-time observation trajectories are reconstructed on the principal components. The trajectory reconstruction er-ror (TRE) signal is calculated to visualize the wear degradation of RV reducers. Through an experimental demonstration in a single-degree-of-freedom (SDOF) robot manipulator, it is found that the proposed method is more effective than commonly used momentum-based residual signals in the wear degradation mon-itoring of RV reducers.

关键词： Degradation Analytical models Torque Service robots Propioception Industrial robots Manipulators Real-time systems Trajectory Monitoring

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Deep Reinforcement Learning for Prefab Assembly Planning in Robot-based Prefabricated Construction 17

Deep Reinforcement Learning for Prefab Assembly Planning in ...

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17th IEEE international conference on automation Science and Engineering (CASE)

作者： Zhu, Aiyu Xu, Gangyan Pauwels, Pieter de Vries, Bauke Fang, Meng Eindhoven Univ Technol Dept Built Environm Informat Syst Built Environm NL-5600 MB Eindhoven Netherlands Harbin Inst Technol Shenzhen HITsz Sch Architecture Shenzhen 518055 Peoples R China Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands

ISBN: (纸本)9781665418737

Smart construction has raised higher automation requirements of construction processes. The traditional construction planning does not match the demands of integrating smart construction with other technologies such as robotics, building information modelling (BIM), and internet of things (IoT). Therefore, more precise and meticulous construction planning is necessary. In this paper, leveraging recent advances in deep Reinforcement Learning (DRL), we design simulated construction environments for deep reinforcement learning and integrate these environments with deep Q-learning methods. We develop reliable controllers for assembly planning for prefabricated construction. For this, we first show that hand-designed rewards work well for these tasks;then we show deep neural policies can achieve good performance for some simple tasks.

关键词： Prefabricated construction automation Computer aided software engineering conferences Reinforcement learning Planning Internet of Things

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YOLOv8-GDI: A lightweight YOLOv8 for real-time Pedestrian Detection

YOLOv8-GDI: A lightweight YOLOv8 for real-time Pedestrian De...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Jianbin Sang Mengwen Zhang Huicheng Yang Ke Xu Yaocong Hu Haiting Wang Mengbo Jia Jun Yuan Anhui Polytechnic University Wuhu China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Aiming at the current challenges of low detection efficiency in real-time pedestrian detection algorithms, complex model structures, and difficulties in implementing them on mobile devices, we proposed YOLOv8-GDI, a lightweight pedestrian detection model designed based on the backbone of YOLOv8n. Firstly, the C2f-Ghost module is constructed by incorporating the Ghost Convolution in the model, in order to decrease the number of floating-point operations during feature channel fusion and enhance feature expression capability. In addition, Dynamic Task Align Detection Head(DTADH) was designed to facilitate the interaction between the model classification and localization to further enhance the model's detection performance. Finally, the Inner-CIoU loss function was introduced into the YOLOv8n to improve the ability to localize features of different sizes. The experimental results show that compared with YOLOv8n, YOLOv8-GDI reduced the number of parameters by 50%, decreased the computational complexity by 25%, and maintained a high detection accuracy on the Wider person dataset. This suggested that the improved model is more lightweight and has better application value in the direction of real-time pedestrian detection.

关键词： Location awareness Adaptation models Pedestrians Accuracy Convolution Computational modeling Heuristic algorithms Feature extraction Real-time systems Computational complexity

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Stretch with Stretch: Physical Therapy Exercise Games Led by a Mobile Manipulator

Stretch with Stretch: Physical Therapy Exercise Games Led by...

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IEEE international conference on robotics and automation (ICRA)

作者： Matthew Lamsey You Liang Tan Meredith D. Wells Madeline Beatty Zexuan Liu Arjun Majumdar Kendra Washington Jerry Feldman Naveen Kuppuswamy Elizabeth Nguyen Arielle Wallenstein Madeleine E. Hackney Charles C. Kemp The Institute for Robotics and Intelligent Machines Georgia Institute of Technology (GT) Emory University School of Medicine The Parkinson’s Foundation Toyota Research Institute (TRI) University of Texas Long School of Medicine

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Physical therapy (PT) is a key component of many rehabilitation regimens, such as treatments for Parkinson’s disease (PD). However, there are shortages of physical therapists and adherence to self-guided PT is low. Robots have the potential to support physical therapists and increase adherence to self-guided PT, but prior robotic systems have been large and immobile, which can be a barrier to use in homes and clinics. We present Stretch with Stretch (SWS), a novel robotic system for leading stretching exercise games for older adults with PD. SWS consists of a compact and lightweight mobile manipulator (Hello Robot Stretch RE1) that visually and verbally guides users through PT exercises. The robot’s soft end effector serves as a target that users repetitively reach towards and press with a hand, foot, or knee. For each exercise, target locations are customized for the individual via a visually estimated kinematic model, a haptically estimated range of motion, and the person’s exercise performance. The system includes sound effects and verbal feedback from the robot to keep users engaged throughout a session and augment physical exercise with cognitive exercise. We conducted a user study for which people with PD (n = 10) performed 6 exercises with the system. Participants perceived the SWS to be useful and easy to use. They also reported mild to moderate perceived exertion (RPE).

关键词： Knee Presses Adaptation models Medical treatment Kinematics Games End effectors

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Design and Construction of a Spherical Magnet Array for Multi-dimensional Tactile Perception

Design and Construction of a Spherical Magnet Array for Mult...

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2023 IEEE international conference on Real-Time Computing and robotics, RCAR 2023

作者： Gui, Mei-Jiang Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Li, Hao Xiang, Tian-Yu Huang, De-Xing Yao, Bo-Xian Hou, Zeng-Guang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Macau University of Science and Technology Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering 999078 China

ISBN: (纸本)9798350327182

Soft magnetic sensors provide considerable merits for tactile perception, such as high precision, simple wiring, and easy expansion to large sensing areas. However, current magnetic sensors basically rely on data-driven methods, difficult to establish the relationship for the two-stage conversion (i.e., from magnetic signals to spatial displacements, then to force signals). In this study, a spherical magnet array is designed with magnetic powder and hyper-elastic rubber for multi-dimensional tactile perception. Besides, a highly interpretable and calculable method is proposed to analyze the two-stage conversion according to magnetic dipole model and dynamic Young's modulus. A nine-spherical-magnet array is constructed, and extensive analysis in terms of different magnet numbers are conducted in COMSOL environments. The results demonstrate the proposed method's high accuracy with mean square errors of 0.02 mT and 9.90 mN for the two stages, respectively. This fact also indicates the great potential of our method to various applications of tactile perception. © 2023 IEEE.

关键词： Mean square error

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First Principle Modeling of a Morphable Unmanned Aerial-Aquatic Vehicle: Mirs-Alioth

First Principle Modeling of a Morphable Unmanned Aerial-Aqua...

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international conference on Control and automation (ICCA)

作者： Dongyue Huang Minghao Dou Xuchen Liu Ruixin Yan Songqun Gao Zixuan Liu Biao Wang Lihua Dou Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper establishes a hybrid system model for a morphable unmanned aerial-aquatic vehicle (UAAV), named Mirs-Alioth, incorporating hydrodynamic parameters through the first principle. Unlike other current UAAVs, variable thrust tilt angles make Mirs-Alioth's model mutable. Thus, this paper directly employs geometric features of the vehicle, integrating a morphing model into a comprehensive dynamics of the vehicle and switching models between different media triggered by depth. The comprehensive model parameters including the rigid body part and the hydrodynamic part is then identified through experiments and computational fluid dynamic (CFD) methods.

关键词： Adaptation models automation Accuracy Computational modeling Computational fluid dynamics Switches Media

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