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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Optimal control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks

arXiv

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arXiv 2024年

作者： Chen, Yu Yin, Xuanyuan Li, Shaoyuan Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University 62 Nanyang Drive Singapore

We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to fulfill a qualitative surveillance task in the sense that a specific region of interest can be visited infinitely often with probability one. Furthermore, to quantify the performance of the system, we consider the concept of efficiency, which is defined as the ratio between rewards and costs. This measure is more general than the standard long-run average reward metric as it aims to maximize the reward obtained per unit cost. Our objective is to synthesize a control policy that ensures the surveillance task while maximizes the efficiency. We provide an effective approach to synthesize a stationary control policy achieving ϵ-optimality by integrating state classifications of MDPs and perturbation analysis in a novel manner. Our results generalize existing works on efficiency-optimal control synthesis for MDP by incorporating qualitative surveillance tasks. A robot motion planning case study is provided to illustrate the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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A High-Quality View Synthesis system for Sparse Rgb-D Input

SSRN

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SSRN 2024年

作者： Zhao, Ziyu Lu, Xiaobo Zhou, Sifan Chiang, Patrick Y.I.N. School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Key Laboratory of ASIC and System Fudan University Shanghai China

Recent research in neural rendering has achieved impressive results in the field of novel view synthesis. However, high-quality view synthesis with sparse source views remains a challenging task, as the reduction in the number of source views significantly degrades the synthesis quality. In this paper, we present a novel view synthesis system that exploits RGB-D pairs from sparse source views. First, we introduce a two-branch feature extraction network to embed the geometric clues from depth maps into the texture information of color images and fuse it with regularized Fourier mapping features. Second, a distance-aware feature query method, which constructs multi-level feature maps, is proposed to ensure high-fidelity details across different distance views. Then, we apply an occupancy network to perform occupancy prediction on raw point cloud. By integrating occupancy probabilities and extracted features, we use a prediction network with geometric information as an additional input to obtain the color and density of sampled points, and synthesize the target view using neural volume rendering. To prevent model degradation and enhance efficiency, we design an efficient sampling strategy based on Gaussian Mixture Model. Moreover, we introduce pixel-wise local regularization and full-image global geometric regularization to optimize the model. Extensive experiments show that the proposed method can achieve the state-of-the-art results for novel view synthesis with sparse inputs, and the model performance has been significantly improved in different scenarios. © 2024, The Authors. All rights reserved.

关键词： Geometry

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Arbitrary State Transition of Open Qubit system Based on Switching control

arXiv

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arXiv 2024年

作者： Wu, Guangpu Xue, Shibei Ma, Shan Kuang, Sen Dong, Daoyi Petersen, Ian R. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Automation Central South University Changsha410083 China Department of Automation University of Science and Technology of China Hefei230027 China School of Engineering Australian National University CanberraACT2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. With coherent vector representation, we propose a switching control strategy, which can prevent the state of the qubit from entering invariant sets and singular value sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states. In comparison to existing works, this control strategy relaxes the strict constraints on system models imposed by special target states. Furthermore, we identify conditions under which the open qubit system achieves finite-time stability (FTS) and finite-time contractive stability (FTCS), respectively. This represents a critical improvement in quantum state transitions, especially considering the asymptotic stability of arbitrary target states is unattainable in open quantum systems. The effectiveness of our proposed method is convincingly demonstrated through its application in a qubit system affected by various types of decoherence, including amplitude, dephasing and polarization decoherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic stability

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A Method for Evading Penetration and Guidance of the Hypersonic Missile

A Method for Evading Penetration and Guidance of the Hyperso...

引用

第43届中国控制会议

作者： Zhi Lin Jianglong Yu Hong Jiang Xiwang Dong Zhang Ren Qingke Tan Rui Lv Zhuo Liang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Institute of Unmanned System Beihang University Institute of Artificial Intelligence Beihang University China Academy of Launch Vehicle Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the problem of evading penetration of the hypersonic missile,this paper proposes a method to control the overload and maneuver time of the hypersonic missile for evasive ***,the kinematic model,the kinetic model,and the attack-defense confrontation model of the hypersonic missile and interceptor are ***,the evaluation function model of the hypersonic missile penetration effect is established,and a method to determine the penetration parameters with the maximum value of evaluation function is ***,the critical parameters of penetration,including the optimal overload and maneuver time,are obtained through online ***,the error conditions of environmental noise and time delay are introduced into the critical parameters,and the Monte Carlo simulation verifies that the evasive penetration method can improve the success rate of penetration of a single hypersonic missile against two interceptors.

关键词： Hypersonic missile attack-defense confrontation overload maneuver time Monte Carlo Simulation

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Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

arXiv

引用

arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information system), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Exact Noise-Robust Distributed Gradient-Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Exact Noise-Robust Distributed Gradient-Tracking Algorithm f...

引用

IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource allocation method with good robustness is urgently needed under complex industrial environments. This paper considers the distributed constraint- coupled resource allocation problem with noisy information exchange over an undirected network, where each agent holds a private cost function and obtains the solution via only local communications. Communication noise poses a challenge to gradient-tracking based algorithm as the impact of noise will accumulate and its variance tends to infinity when the noise is persistent. Adopting noise-tracing scheme, we propose an exact noise-robust distributed gradient-tracking algorithm to achieve cost-optimal distribution of resources, which can avoid noise-accumulation in the tracking step. Moreover, noise suppression parameters are introduced to further attenuate the impact of noise. With diminishing suppression parameters, it is theoretically proved that the proposed algorithm is able to achieve exact convergence to the optimal solution. Finally, a numerical example is provided for verification.

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent systems

作者： Ting Cheng Shuai Liu Qimin Xu Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources.

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An Intelligent Robot Assisting Medical Practitioners to Aid Potential Covid-19 Patients 6

An Intelligent Robot Assisting Medical Practitioners to Aid ...

引用

6th International Conference on Image Information Processing, ICIIP 2021

作者： Sowrabh, S. Rameshkumar, Ashith Athira, P.V. Jishnu, T.B. Anish, M.N. UPES MTech in Robotics and Automation School of Engineering Dehradun India NSSCE Bachelor of Technology in Instrumentation Control Engineering Department Palakkad India Assistant System Engineer-trainee TCS Limited Kochi India NSSCE Instrumentation Control Engineering Department Palakkad India

ISBN: (纸本)9781665433617

The purpose of this paper is to develop a robot-nurse capable of assisting human health professionals in a COVID-19 isolation unit such that health care professionals may discontinue wearing the personal protective kit that they previously wore when dealing with patients at isolation units. The robot can perform most of the tasks carried out by human nurses in a hospital. It can take a patient's temperature without touching them, administer medicines at the appropriate time, assist in sanitizing the individual and their hands, and offer a section for sterilizing medical equipment. [14] © 2021 IEEE.

关键词： Nursing

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Optimal control in Both Steady State and Transient Process with Unknown Disturbances

arXiv

引用

arXiv 2024年

作者： Li, Ming Wang, Zhaojian Liu, Feng Cao, Ming Yang, Bo The Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Power System The Department of Electrical Engineering Tsinghua University Beijing100084 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands

The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem. Such methods focus on regulating the physical states to the optimal solution in the steady state, without considering the performance during the transient process. In this paper, we simultaneously consider the performance in both the steady state and the transient process of a linear time-invariant system with unknown disturbances. The performance of the transient process is illustrated by the concept of overtaking optimality. An overtaking optimal controller with known disturbances is derived to achieve the transient overtaking optimality while guaranteeing steady-state performance. Then, we propose a disturbance independent near-optimal controller, which can achieve optimal steady-state performance and approach the overtaking optimal performance in the transient process. The system performance gap between the overtaking optimal controller and the proposed controller proves to be inversely proportional to the control gains. A case study on a power system with four buses is used to validate the effectiveness of the two controllers. © 2024, CC BY.

关键词： Electromagnetic transients

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