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IEEE International Conference on Robotics and automation (ICRA)

作者： Yinghan Wang Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by point clouds, MNP first efficiently compresses point clouds into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

关键词： Point cloud compression Learning systems Training Simulation Neurons Training data Encoding

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Multi-Agent Path Planning for Finite Horizon Tasks with Counting Time Temporal Logics

Multi-Agent Path Planning for Finite Horizon Tasks with Coun...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Peng Lv Shaoyuan Li Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we consider the multi-agent path planning problem for high-level tasks with finite horizons. In many situations, there is the need to count how many times a sub-task is satisfied in order to achieve the overall task. However, existing temporal logic languages, such as linear temporal logic, may not be efficient in describing such requirements. To address this issue, we propose a new temporal logic language called Counting Time Temporal Logic (CTTL) that extends linear temporal logic by explicitly counting the number of times that some tasks are satisfied. To solve the CTTL path planning problem, we use integer linear programming to encode the satisfaction of the task. We demonstrate that our approach is both sound and complete. To validate our results, we present numerical experiments to show the scalability of the proposed approach. Furthermore, we provide a simulation case study of a team of autonomous robots to illustrate the synthesis procedure.

关键词： Computer aided software engineering automation Scalability Integer linear programming Path planning Planning Delays Logic Standards Autonomous robots

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Jointly Learning Time-Structure-History Graph Embedding for Temporal Knowledge Graph Reasoning 19

Jointly Learning Time-Structure-History Graph Embedding for ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Duan, Hao Jin, Haoyu Chen, Kang Du, Shaochong Fang, Tao Huo, Hong Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350346558

The Temporal Knowledge Graph(TKG) reasoning is an imperative task for various applications of TKG. However, reasoning over TKGs which aims to predict future facts has been less explored. Intuitively, entities and relations in TKG change differently over time, and the variations are different. It also can be observed that what happens in the past may affect the future. Apart from these, structure information that exists in the neighborhood of a given entity in a KG has been proven to be important. Inspired by these, a novel graph embedding method for TKG reasoning, called time-structure-history graph embedding (TSHGE), is proposed in this paper. TSHGE consists of three progressive learning units. The first time information learning unit employs a novel time-aware mechanism to encode the time influence on entities and relations. Then a relation-aware GAT (Graph Attention Network) is used in the structure information learning unit to model the 1-hop neighborhoods of all entities in a KG at each timestamp in TKG. Finally, the history information learning unit utilizes GRU (Gated Recurrent Unit) to learn history information from various facts. Then the ultimate embedding representations of entities and relations can be obtained for the prediction of future facts. The evaluations on three public TKG datasets have demonstrated the effectiveness and superiority of TSHGE for predicting future facts. © 2022 IEEE.

关键词： Forecasting

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Joint Design of control and Transmission for Industrial CPS under Time Sensitive Networking 48

Joint Design of Control and Transmission for Industrial CPS ...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Lu, Xuanzhao Xu, Qimin Zhang, Jinglong Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

To improve the performance of industrial cyber-physical systems (ICPS), the joint design of control and transmission has been shown to be an effective mechanism. However, the existing joint design work lack completeness and accuracy, which brings challenges to improving the stability and network resource utilization of ICPS. In this paper, we propose a control and transmission joint design architecture for integrated multi-subsystem ICPS for multi-hop networks. We consider the timeliness of information through state delays, input delays, and event-triggered states in ICPS. To avoid the instability caused by input delay, we design an LQR-based controller and derive the feasible region of delay to guarantee the stability of the control system. Finally, we propose an event-triggered strategy that represents control and transmission costs to minimize the amount of data transfer while ensuring system stability. The evaluation results show that our strategy improves the stability of the control system while reducing the network burden compared with the traditional joint design method. © 2022 IEEE.

关键词： Cyber Physical system

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Curvature Adaptive control Based Path Following for Automatic Driving Vehicles in Private Area

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 690-698页

作者： SHI Qiang ZHANG Jianlin YANG Ming Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

Path following refers to traveling along the desired path with automatic steering control,which is a crucial technology for automatic driving *** in private areas are highly irregular,resulting in a large curvature variation,which reduces the control accuracy of the path following.A curvature adaptive control(CAC)based path-following method was proposed to solve the problem mentioned ***,CAC takes advantage of the complementary characteristics in response to the path curvature fluctuation of pure pursuit and front-wheel feedback and by combining the two methods further enhances the immunity of the control accuracy in response to a curvature *** CAC,the quantitative indices of the path curvature fluctuation and control accuracy were *** model between the path curvature fluctuation and a dynamic parameter was identified using the quantitative index of the control accuracy as the optimization *** experimental results of a real vehicle indicate that the control accuracy of path following is further enhanced by its immunity in response to curvature fluctuation improved by the *** addition,CAC is easy to deploy and requires low demand for hardware resources.

关键词： automatic driving vehicles path following adaptive control

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Collision-Free Path Planning with Kinematic Constraints in Urban Scenarios

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 731-738页

作者： WANG Liang WANG Bing WANG Chunxiang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

In urban driving scenarios,owing to the presence of multiple static obstacles such as parked cars and roadblocks,planning a collision-free and smooth path remains a challenging *** addition,the path-planning problem is mostly non-convex,and contains multiple local ***,a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban *** sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor,and the optimization-based method constructs a quadratic programming problem considering the desired state constraints,continuity constraints,driving corridor constraints,and kinematic constraints to perform path *** experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.

关键词： autonomous vehicle path planning dynamic searching quadratic programming

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Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicing in Time-Sensitive Networking

Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicin...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Shouliang Xu, Qimin Zhang, Yanzhou Xu, Lei Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai China 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781728119489

The development of the Industrial Internet of Things (IIoT) is increasingly relying on the low-latency and low-jitter information exchange. Time-Sensitive Networking (TSN) guarantees the deterministic transmission by a set of mechanisms, such as Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). However, the deterministic scheduling of hybrid traffic with different Quality of Service (QoS) is still unresolved since traffic performance is hard to meet by a single mechanism or an integrated mechanism with fixed time slot. In this paper, to realize the low-latency and low-jitter hybrid traffic scheduling, an adaptive time slot slicing (ATSS) method is proposed integrating TAS and CQF. With dynamically slicing the time slot to centralize time-triggered (TT) traffic, ATSS eliminates the irregular distribution of TT flow to support multi-hop networks. A resource calculation method based on slot occupancy judgment is devised to reduce the scheduling complexity, which raises the analysis objects into flows instead of frames. To reduce the complexity further, an incremental algorithm with the search boundary of resources is designed. The evaluation results show that ATSS reduces the jitter by 12 times and time cost by 200 times when handling over 125 mixed flows in different network topologies compared to previous work. © 2022 IEEE.

关键词： Jitter

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Integrated Framework for Test and Evaluation of Autonomous Vehicles

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 699-712页

作者： SU Yimin WANG Lin Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

Autonomous vehicles must pass effective standard tests to verify their reliability and ***-ingly,it is very important to establish a complete scientific test and evaluation system for autonomous vehicles.A comprehensive framework incorporating the design of test scenarios,selection of evaluation indexes,and estab-lishment of an evaluation system is proposed in this *** aims of the system are to obtain an objective and quantitative score regarding the intelligence of autonomous vehicles,and to form an automated process in the future *** proposed framework is built on a simulation platform to ensure the feasibility of the design and implementation of the test *** design principle for the test scenarios is also *** reduce subjective influences,the proposed framework selects objective indexes from four aspects:safety,comfort,driving performance,and standard *** order relation analysis method is adopted to formulate the index weights,and fuzzy comprehensive evaluation is used to quantify the ***,a numerical example is provided to visually demonstrate the evaluation results for the autonomous vehicles scored by the proposed framework.

关键词： test and evaluation scenario construction evaluation index autonomous vehicles

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Real-Time Trajectory Planning for On-road Autonomous Tractor-Trailer Vehicles

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 722-730页

作者： SHEN Qiyue WANG Bing WANG Chunxiang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

Tractor-trailer vehicles,which are composed of a car-like tractor towing a passive trailer,have been widely deployed in the transportation industry,and trajectory planning is a critical step in enabling such a system to drive *** to the properties of being highly nonlinear and nonholonomic with complex dynamics,the tractor-trailer system poses great challenges to the development of motion-planning *** this study,an indirect trajectory planning framework for a tractor-trailer vehicle under on-road driving is presented to deal with the problem that the traditional planning framework cannot consider the feasibility and quality simultaneously in real-time trajectory generation of the tractor-trailer *** indirect planning framework can easily handle complicated tractor-trailer dynamics and generate high-quality,obstacle-free trajectory using quintic polynomial spline,speed profile optimization,forward simulation,and properly designed cost *** under different driving scenarios and trajectories with different driving requirements are conducted to validate the performance of the proposed framework.

关键词： trajectory planning tractor-trailer vehicle forward simulation on-road driving

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LiDAR Point Cloud Map Coding Framework Based on Quadric Surface Fitting and Scene Construction 5

LiDAR Point Cloud Map Coding Framework Based on Quadric Surf...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Xuebin Wang, Jinxi Yang, Xiaofei School of Electrical Automation and Information Engineering Tianjin University Tianjin China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Shanghai Institute of Optics and Fine Mechanics Department of Aerospace Laser Technology and System Shanghai China

ISBN: (纸本)9798350355925

Due to rapid 3D acquisition advancements, LiDAR point clouds are prevalent in emerging applications like heritage preservation, mobile robotics, and remote sensing. Yet, the sheer data volume poses challenges for transmission and storage, especially in autonomous driving. This paper introduces an efficient coding framework for compressing large dense LiDAR point cloud maps. By recording timestamps and quaternion matrices during map construction, we reduce map compression to sequence compression. The scans are segregated into intra-frames and inter-frames. For intra-frames, a quadric surface fitting intra-prediction technique eliminates spatial redundancy. For inter-frames, by constructing the 3D scene using encoded intra-frames, the inter-frame can be simulated according to its posture and LiDAR intrinsic parameters. Coding focuses on the difference between actual and predicted point cloud data. Decoding reverses this process, reconstructing the point cloud map from the decoded sequence and quaternion matrices. KITTI dataset experiments show the point cloud map can be averagely compressed to 3.43% of its original size while maintaining centimeter-level precision. This outperforms both octree and Google Draco methods. © 2024 IEEE.

关键词： Mobile robots

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