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376 results on '"Hua, Yongzhao"'

1. Learning-Initialized Trajectory Planning in Unknown Environments

Author

Chen, Yicheng, Li, Jinjie, Qin, Wenyuan, Hua, Yongzhao, Dong, Xiwang, and Li, Qingdong

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Autonomous flight in unknown environments requires precise planning for both the spatial and temporal profiles of trajectories, which generally involves nonconvex optimization, leading to high time costs and susceptibility to local optima. To address these limitations, we introduce the Learning-Initialized Trajectory Planner (LIT-Planner), a novel approach that guides optimization using a Neural Network (NN) Planner to provide initial values. We first leverage the spatial-temporal optimization with batch sampling to generate training cases, aiming to capture multimodality in trajectories. Based on these data, the NN-Planner maps visual and inertial observations to trajectory parameters for handling unknown environments. The network outputs are then optimized to enhance both reliability and explainability, ensuring robust performance. Furthermore, we propose a framework that supports robust online replanning with tolerance to planning latency. Comprehensive simulations validate the LIT-Planner's time efficiency without compromising trajectory quality compared to optimization-based methods. Real-world experiments further demonstrate its practical suitability for autonomous drone navigation.

Published
2023

2. Target Assignment in the Multi-UAV Combat Based on Distributed Compensation Auction Algorithm with Fixed-Time Consensus Protocol

Author

Chen, Yiyang, Hua, Yongzhao, Yu, Jianglong, Dong, Xiwang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Hua, Yongzhao, editor, Liu, Yishi, editor, and Han, Liang, editor

Published
2024
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3. Penetration trajectory optimization for the hypersonic gliding vehicle encountering two interceptors

Author

Shen, Zhipeng, Yu, Jianglong, Dong, Xiwang, Hua, Yongzhao, and Ren, Zhang

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract

The penetration trajectory optimization problem for the hypersonic gliding vehicle (HGV) encountering two interceptors is investigated. The HGV penetration trajectory optimization problem considering the terminal target area is formulated as a nonconvex optimal control problem. The nonconvex optimal control problem is transformed into a second-order cone programming (SOCP) problem, which can be solved by state-of-the-art interior-point methods. In addition, a penetration strategy that only requires the initial line-of-sight angle information of the interceptors is proposed. The convergent trajectory obtained by the proposed method allows the HGV to evade two interceptors and reach the target area successfully. Furthermore, a successive SOCP method with a variable trust region is presented, which is critical to balancing the trade-off between time consumption and optimality. Finally, the effectiveness and performance of the proposed method are verified by numerical simulations., Comment: 34 pages, 18 figures

Published
2022
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5. UAV Path Planning from Human Demonstrations Using Inverse Reinforcement Learning

Author

Xu, Zhiang, Dong, Jin, Hua, Yongzhao, Dong, Xiwang, Li, Qingdong, Ren, Zhang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor

Published
2023
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6. Distributed Time-Varying Optimization for a Class of Second-Order Multi-agent Systems with Disturbance

Author

Su, Piaoyi, Zheng, Hongwei, Yu, Jianglong, Hua, Yongzhao, Dong, Xiwang, Ren, Zhang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor

Published
2023
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7. Weapon Target Assignment Based on Compensation Auction Algorithm

Author

Li, Xuheng, Yu, Jianglong, Dong, Xiwang, Li, Qingdong, Hua, Yongzhao, Ren, Zhang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor

Published
2023
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8. SOM Neural Network Based Gaussian Mixture PHD Algorithm for Multi-Sensor Multi-Target Tracking

Author

Zhang, Yujie, Zheng, Hongwei, Zhang, Zheng, Yu, Jianglong, Hua, Yongzhao, Dong, Xiwang, Ren, Zhang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor

Published
2023
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12. Time-Varying Formation Tracking for Heterogeneous Swarm Systems Under Switching Directed Topologies with a Leader of Input

Author

Tian, Lei, Hua, YongZhao, Dong, Xiwang, Li, Qingdong, Ren, Zhang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Yu, Xiang, editor

Published
2022
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18. Introduction

Author

Hua, Yongzhao, primary, Dong, Xiwang, additional, and Zhang, Ren, additional

Published
2022
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36. Cooperative Fault-Tolerant Formation Tracking Control for Heterogeneous Air–Ground Systems Using a Learning-Based Method

Author

Shi, Yu, Hua, Yongzhao, Yu, Jianglong, Dong, Xiwang, Lu, Jinhu, and Ren, Zhang

Abstract

This article investigates the formation tracking control problem for heterogeneous air–ground systems with quadrotor unmanned aerial vehicles (UAVs) and wheeled unmanned ground vehicles (UGVs) of nonlinear dynamics. A hierarchical learning-based framework is proposed to address the unknown tracking leader and multiple faults. First, an adaptive observer is designed to estimate both the dynamics and state of the tracking leader via a directed network. Second, based on the data from an internal model filter and system measurements, an off-policy reinforcement learning (RL) algorithm is applied to learn a unified tracking planner for both UAVs and UGVs. The planner is coordinated with time-varying leader and formation in the RL process, while internal dynamics are learned. Third, a finite-time differential filter and a virtual reference are respectively constructed for UAVs and UGVs, and transform the planner's outputs into tracking commands. By integrating adaptive neural networks and robust compensations with backstepping designs, the fault-tolerant formation tracking is realized. Finally, the learning and control framework on air–ground systems are validated by numerical simulations.

Published
2024
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37. Fully Data-Driven Robust Output Formation Tracking Control for Heterogeneous Multiagent System With Multiple Leaders and Actuator Faults

Author

Shi, Yu, Hua, Yongzhao, Yu, Jianglong, Dong, Xiwang, and Ren, Zhang

Abstract

This article investigates a fully data-driven method to solve the robust output formation tracking control problem for the multiagent system (MAS) under actuator faults. The outputs of the followers are controlled to track those of multiple leaders with respect to a convex point while achieving an expected time-varying formation. To obviate the requirement of various system prior knowledge in typical MAS control, a hierarchical frame is developed with three learning and control stages using the online measured data. First, a distributed adaptive observer is designed to coordinate the state convex of multiple leaders while estimating unknown dynamics. The adaptive mechanism relaxes the demand for global topology. Second, by collecting and reusing the online system data, an off-policy reinforcement learning (RL) method is proposed in a continuous form to acquire nominal feedback gains from partial observations of the followers. Essential system models are learned along with the RL process, while solutions to the output regulation equations are implicitly obtained. Third, a comprehensive robust controller is further presented based on the previous learning results. To address the actuator faults with efficiency loss and bias, the adaptive neural networks and robust compensations are utilized in a model-free manner. The output formation tracking is achieved under a derived feasibility condition while stabilities of the learning and control methods are analyzed. Finally, simulation results demonstrate the validity of this fully data-driven control frame.

Published
2024
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38. Practical Optimal Formation-Containment Tracking Control of Nonlinear Multiagent Systems With Unknown Dynamics

Author

Wang, Tingting, Dong, Xiwang, Hua, Yongzhao, Wang, Danwei, and Ren, Zhang

Abstract

This article considers the practical optimal time-varying formation-containment tracking (FCT) issues of nonlinear multiagent systems (MASs) with unknown dynamics, where the quadratic regulator performance indicators related to errors and control inputs are defined for formation-leaders and followers. The optimal control inputs for minimizing performance indicators are normally obtained by solving the Hamilton–Jacobi–Bellman (HJB) equation. Nonetheless, the nonlinear MASs have nonlinear and coupling properties such that the analytical solution is hard to obtain by solving HJB equation. To address this difficulty, the neural networks are adopted to identify the unknown nonlinear dynamics, and an identifier is constructed to ensure the identification state errors converge asymptotically. Moreover, reinforcement learning with a critic–actor structure is employed to approximate the optimal performance indicators and optimal control inputs. Then, the practical optimal FCT control algorithm is presented to design the protocol parameters. The simulation results are shown to illustrate the effectiveness of the proposed theoretical method.

Published
2023
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39. Multitarget Distributed Tracking With Probability Hypothesis Density in Large-Scale Sensor Networks

Author

Su, Lingfei, Yu, Jianglong, Hua, Yongzhao, Li, Qingdong, Dong, Xiwang, and Ren, Zhang

Abstract

In this article, multitarget distributed tracking problems in large-scale sensor networks consisting of local fusion centers (LFCs) and sensor nodes (SNs) are considered. Each SN transmits measurements to its superior LFC, which runs a local probability hypothesis density filter having Gaussian mixture representation (GM-PHD) using measurements from subordinate SNs. A framework based on random finite set (RFS) is developed, incorporating an improved GM-PHD filter and an improved geometric average (GA) fusion rule. Regarding the local GM-PHD filter, a modified adaptive birth model using presegmented measurements and an SN-related clutter-based update step are proposed to handle the measurement origin uncertainty and the cardinality overestimation issues. Regarding the proposed distributed fusion algorithm that fuses posteriors among LFCs with the proposed GM-PHD filters, a hybrid fusion rule is proposed to compensate the GA miss detection by arithmetic average (AA) fusion. The boundedness of the proposed algorithm is derived. Finally, simulations illustrate the effectiveness of the proposed algorithm.

Published
2023
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