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第40届中国控制会议

作者： Zhengxin Ruan Sicong Chen Yuanlong Li Liangren Shi Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

This paper proposes an intelligent intersection management method based on the vehicle dynamic priority. By the information interaction between the autonomous vehicles（AVs） and the infrastructure equipment, the vehicle position and posture information is obtained in real time, and the priority of the vehicles passing through the conflict points in the scene is determined according to the order of the vehicles driving through the crossroads. Subsequently, to track the planned trajectory, we use the model predictive control（MPC） algorithm minimize the intersection delay with collision-free constraints. This guarantees the vehicles to drive through the intersection safely in the scene. Simulation experiments demonstrate the reliability and efficiency of the proposed algorithm.

关键词： Autonomous vehicle Intersection management system Vehicle-to-infrastructure communication Dynamic priority Model predictive control

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An Accelerated Gossip-Based Distributed Gradient Method

An Accelerated Gossip-Based Distributed Gradient Method

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第40届中国控制会议

作者： Xiaoxing Ren Dewei Li Yugeng Xi Haibin Shao Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai Engineering Research Center of Intelligent Control and Management

This paper studies distributed optimization over the multi-agent network. We develop and analyze a novel accelerated distributed gradient descent method, termed as G-DGDlm, for gossip communication protocol. G-DGDlm uses gradient tracking technique and local memory. Specifically, each agent stores two past variables, namely, an estimate of the average gradient and an estimate of the optimal solution. G-DGDlm achieves linear convergence for strongly convex and smoot√h functions when the related works, we remove the dependence of θ on global parameters and G-DGDlm is applicable to gossip communication protocol. Numerical experiments of distributed estimation in sensor networks show the faster convergence speed of G-DGDlm in comparison with the state-of-the-art methods.

关键词： Distributed convex optimization gossip communication first-order methods

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Topology Inference for Networked Dynamical Systems: A Causality and Correlation Perspective

Topology Inference for Networked Dynamical Systems: A Causal...

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IEEE Conference on Decision and control

作者： Yushan Li Jianping He Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9781665436601

Networked dynamical systems (NDSs) have gained considerable attention in recent years, where the networked agents cooperate to accomplish the common task through the interaction topology. In this paper, we focus on the topology inference problem of NDSs. Different from traditional methods, we aim to infer the internal interaction topology from the perspective of node causality and correlation, covering both directed/undirected topology structures and the asymptotic/marginal stabilities of NDSs. Specifically, we propose a causality-based method that takes the noise characteristic into account and asymptotically approaches the real interaction topology structure, with only single round observations over the dynamical process. When the observation number is small, we further design a correlation-based modification to effectively alleviate the influence of noises. We demonstrate the close relation between the proposed method and the traditional Granger estimator and ordinary least square (OLS) estimator in terms of observation rounds and horizon. We further prove the equivalence conditions of the proposed method with the two estimators from the system stability and noise characteristic. The proposed causality-correlation combined method enjoys analogous asymptotic inference performance with Granger estimator of multiple observation rounds, and outperforms OLS estimator in single observation round, verified by extensive simulations.

关键词： Degradation Asymptotic stability Correlation Network topology Conferences Stability analysis Topology

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Bearing-only Formation control of Nonholonomic Agents with Unknown Dynamic Leaders

Bearing-only Formation Control of Nonholonomic Agents with U...

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Chinese Automation Congress (CAC)

作者： Haifan Su Ziwen Yang Shanying Zhu Cailian Chen Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426480

In this paper, the problem of bearing-only formation tracking control of multiple nonholonomic agents is studied. A leader-follower structure is adopted, where the leaders are moving at an unknown constant linear velocity and time-varying orientation. To solve the problem using bearing-only measurements, a finite-time estimator is firstly proposed to estimate the leaders’ orientation information. Then, an adaptation law for estimating the leaders’ linear velocity is proposed, based on which control inputs for tracking the leaders in a predefined bearing-based formation are designed, so that formation tracking can be achieved asymptotically. Rigorous theoretical analysis of the closed-loop system is given. It is proved that both the estimation errors and formation errors converge to zero under the connectivity of the communication graph and conditions for bearing rigidity. Numerical simulations are presented to testify the effectiveness of the proposed method.

关键词： Estimation error Automation Simulation Numerical simulation Rigidity Velocity measurement Closed loop systems

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Bearing-based Formation Tracking of Nonholonomic Agents in the Leader’s Local Frame

Bearing-based Formation Tracking of Nonholonomic Agents in t...

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Chinese Automation Congress (CAC)

作者： Ke Lv Ziwen Yang Shanying Zhu Lin Wang Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9781665426480

This paper investigates the formation tracking control problem of nonholonomic agents in the leader’s local frame, based on bearing measurements. Most of existing works on formation tracking are aimed at tracking a given formation in the global frame, which cannot meet the needs of many applications such as a fleet of cars driving on the highway. In this case, if the cars cannot keep a fixed formation in the leader’s local frame, they may drive off the highway. Thus we propose an integrated approach consisting of a bearing-based estimator and a tracking controller, to track a given formation in the leader’s local frame. In order to eliminate the estimation error in formation tracking, a persistently exciting leader is characterized by conditions. The stability of the integrated method is proved rigorously. Simulation results prove the effectiveness of the proposed approach.

关键词： Road transportation Estimation error Automation Simulation Stability analysis Robustness Trajectory

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Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

Research Square

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Research Square 2021年

作者： Xie, Jialiang Zhang, Shanli Wang, Honghui Wu, Dongrui School of Science Jimei University Xiamen361021 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control China

Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on fifteen test problems were conducted to verify its effectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. © 2021, CC BY.

关键词： Self organizing maps

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Event-Triggered Consensus on Matrix-weighted Networks

Event-Triggered Consensus on Matrix-weighted Networks

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Chinese Automation Congress (CAC)

作者： Lulu Pan Chongzhi Wang Haibin Shao Dewei Li Yugeng Xi Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426480

In this work, we consider the consensus problem of the multi-agent system over matrix-weighted networks using an event-triggered mechanism. An event-triggered coordination strategy is proposed to steer this generalized category of multi-agent systems towards the bipartite consensus state. The triggering condition is periodically checked and distributed in the sense that the event detector only uses neighbors’ information and local computation at discrete sampling instants. Moreover, it is shown that the triggering condition is closely related to the spectrum of matrix-valued weights corresponding to the edges in the matrix-weighted network. In the meantime, the Zeno phenomenon can be avoided automatically under the proposed event trigger approach.

关键词： Automation image edge detection Detectors Eigenvalues and eigenfunctions Multi-agent systems

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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Attacking End-to-End Visual Navigation Model: How Weak Existing Learning-Based Approaches Can Be?

Attacking End-to-End Visual Navigation Model: How Weak Exist...

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IEEE International Conference on Robotics and Biomimetics

作者： Hongye Wang Kefan Jin Hesheng Wang Shanghai Jiao Tong University China MOE Key Laboratory of Marine Intelligent Equipment and System State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665405362

Learning-based self-driving techniques and vehicle navigation approaches are the most hot topics in recent years. However, existing approaches typically assume the attack-free sensor data, the safety issue under large disturbance or external attack have not been well-solved. In this article, we build a simple FGSM-based attack method designed by minimizing the maximum value of the extracted visual features in order to greatly decrease the performance of the popular learning model for vehicle navigation and even make it totally fail. The proposed min-max operation based feature space attack method solves the problems of branch activation uncertainties and the lack of labels. Furthermore, we also provide a general adversarial training framework which can be used to overcome the proposed feature space attack. Simulational experiments in CARLA platform demonstrate the effectiveness and practical applicability of our attacking approach and defence strategy.

关键词： Training Space vehicles Visualization Uncertainty Navigation Feature extraction Robustness

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An Integrated Neural Unmanned System for Multi-Domain Perception and Decision-Making

An Integrated Neural Unmanned System for Multi-Domain Percep...

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IEEE International Conference on Robotics and Biomimetics

作者： Qiming Liu Neng Xu Jinpeng Zhang Hesheng Wang Department of Automation Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Second Academy of China Aerospace Science and Industry Corporation (CASIC) Beijing China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665481106

Most recent intelligent systems study the perception or policy modules independently but ignore the inter-connection between them, which results in low information efficiency and high network redundancy, in the end, leads to the incomplete utilization of network potential and poor system-level end-to-end intelligence. This paper proposes an integrated end-to-end network that adapts to multi-domain inputs and multi-function outputs. “Integration” is defined in this paper in mainly three aspects: network structure level, optimization level, and functional level. The integrated network architecture is capable of fusing multiple raw sensory inputs and outputting multiple perceptions and decisions simultaneously. We first implement the integrated system in a virtual simulation environ-ment and investigate the domain transformation methods to real world. Comprehensive experiment results in both virtual and real environments show that our proposed system can behave normally under exploration, target-driven navigation and target shooting tasks.

关键词： Training Point cloud compression Target tracking Navigation Robot kinematics Redundancy Robot sensing systems

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