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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang 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 School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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Edge-Assisted Flexible Platooning of Connected and Automated Vehicles Under Traffic Anomaly

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IEEE Transactions on Vehicular Technology 2025年

作者： Gao, Fengkun Yang, Bo Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

The platooning of connected and automated vehicles (CAVs) has the great potential to significantly improve travel experience in terms of safety, comfortableness, and energy efficiency. However, constrained by sensing range and prediction accuracy, the performance of CAV platoon can not be guaranteed in the scenario of mixed traffic consisting of CAVs and human-driven vehicles (HDVs). This is not only due to the uncertainty of HDVs but also related to the occurrence of various traffic anomalies, such as emergency braking and accidents. In this paper, we aim to investigate an efficient control strategy to guarantee the operation of the platoon in mixed traffic, especially in the case of traffic anomalies. To tackle the problem, an edge-assisted platoon control strategy, called E-CACC, is proposed. First, an error-aware flexible tracking policy is designed to adapt to HDV uncertainties and speed prediction errors. Based on the tracking policy, a zone model predictive control (ZMPC) method is then designed to guarantee travel safety and avoid undesired acceleration. Further, a coordination strategy of CAV platoon and roadside unit (RSU) is proposed to prevent the platoon from potential collision in the case of traffic anomalies, where an event-based communication trigger is designed to determine whether and when the platoon establishes communication links with RSU to acquire exogenous sensing data of anomaly segment. Finally, the evaluation results illustrate the effectiveness of the proposed method in improving travel safety and comfortableness. © 1967-2012 IEEE.

关键词： Braking performance

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DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscopic Scenes

DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscop...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Lujia Yang Qiyu Feng Lijun Han Hesheng Wang 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 School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Estimating camera motion and continuously reconstructing dense scenes in deformable environments presents a complex and open challenge. Many existing approaches tend to rely on assumptions about the scene’s topology or the nature of deformable motion. However, these assumptions do not hold true in medical endoscopy applications. To address these challenges, we introduce DDS-SLAM, a novel dense deformable semantic neural SLAM that achieves accurate camera tracking, continuous dense scene reconstruction, and high-quality image rendering in deformable scenes. First, we propose a novel hybrid neural scene representation method capable of capturing both natural and artificial deformations. Additionally, by leveraging the 2D semantic information of the scene, we introduce a semantic loss function based on semantic distance fields. This approach guides network optimization at a higher level, thereby enhancing system performance. Furthermore, we validate our method through a series of experiments conducted on several representative medical datasets, demonstrating its superiority over other state-of-the-art approaches. The code is available at: https://***/IRMVLab/DDS-SLAM.

关键词： Simultaneous localization and mapping Tracking system performance Semantics Cameras Rendering (computer graphics) Topology Image reconstruction Optimization Biomedical imaging

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A Communication-Efficient Distributed Optimization Algorithm with Linear Convergence over Directed Graphs

A Communication-Efficient Distributed Optimization Algorithm...

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Asian control Conference

作者： Yuzhu Duan Ziwen Yang Shanying Zhu Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education of China Shanghai P. R. China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The Industrial Internet of Things (IIoT) is increasingly emerging as a novel paradigm for information exchange within industrial production environments. To facilitate the efficient operation of IIoT, there is an urgent requirement for communication-efficient methods to transmit extensive production data. This paper proposes a communication-efficient distributed optimization algorithm tailored for the strongly convex and smooth convex optimization problems over a directed graph. To encompass a broader class of compressors, the assumption of compressor is relaxed to contain both relative and absolute errors. To mitigate compression errors, we adopt differential compression technique and dynamic scaling factor in the algorithm. In particular, under this compression assumption, a non-uniform quantizer is introduced to enable finite-bit transmission over communication channels and an upper bound of the required bits with this quantizer is provided. Moreover, we prove the linear convergence of the proposed algorithm with compression, and numerical examples are provided to verify the performance of the proposed algorithm.

关键词： Upper bound Heuristic algorithms Directed graphs Production Compressors Convex functions Optimization

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

arXiv

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arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie W., Liu Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

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ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description

arXiv

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arXiv 2024年

作者： Guo, Xiao-Yu Li, Yi-Fan Liu, Yuan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Protein design has become a critical method in advancing significant potential for various applications such as drug development and enzyme engineering. However, protein design methods utilizing large language models with solely pretraining and fine-tuning struggle to capture relationships in multi-modal protein data. To address this, we propose ProtDAT, a de novo fine-grained framework capable of designing proteins from any descriptive protein text input. ProtDAT builds upon the inherent characteristics of protein data to unify sequences and text as a cohesive whole rather than separate entities. It leverages an innovative multi-modal cross-attention, integrating protein sequences and textual information for a foundational level and seamless integration. Experimental results demonstrate that ProtDAT achieves the state-of-the-art performance in protein sequence generation, excelling in rationality, functionality, structural similarity, and validity. On 20,000 text-sequence pairs from Swiss-Prot, it improves pLDDT by 6%, TM-score by 0.26, and reduces RMSD by 1.2 Å, highlighting its potential to advance protein design. © 2024, CC BY-NC-SA.

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons Under DoS Attacks

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

引用

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

作者： Qiaoni Han Guowen Cheng Zhenning Tang Chang Liu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security.

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Model Predictive control for T-S Fuzzy Markovian Jump systems Using Dynamic Prediction Optimization

arXiv

引用

arXiv 2024年

作者： Zhang, Bin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

In this paper, the model predictive control (MPC) problem is investigated for the constrained discrete-time Takagi-Sugeno fuzzy Markovian jump systems (FMJSs) under imperfect premise matching rules. To strike a balance between initial feasible region, control performance, and online computation burden, a set of mode-dependent state feedback fuzzy controllers within the frame of dynamic prediction optimizing (DPO)-MPC is delicately designed with the perturbation variables produced by the predictive dynamics. The DPO-MPC controllers are implemented via two stages: at the first stage, terminal constraints sets companied with feedback gain are obtained by solving a "min-max" problem;at the second stage, and a set of perturbations is designed felicitously to enlarge the feasible region. Here, dynamic feedback gains are designed for off-line using matrix factorization technique, while the dynamic controller state is determined for online over a moving horizon to gradually guide the system state from the initial feasible region to the terminal constraint set. Sufficient conditions are provided to rigorously ensure the recursive feasibility of the proposed DPO-MPC scheme and the mean-square stability of the underlying FMJS. Finally, the efficacy of the proposed methods is demonstrated through a robot arm system example. © 2024, CC BY.

关键词： Predictive control systems

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SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds

arXiv

引用

arXiv 2024年

作者： Wang, Yanbo Zhao, Wentao Cao, Chuan Deng, Tianchen Wang, Jingchuan Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Although LiDAR semantic segmentation advances rapidly, state-of-the-art methods often incorporate specifically designed inductive bias derived from benchmarks originating from mechanical spinning LiDAR. This can limit model generalizability to other kinds of LiDAR technologies and make hyperparameter tuning more complex. To tackle these issues, we propose a generalized framework to accommodate various types of LiDAR prevalent in the market by replacing window-attention with our sparse focal point modulation. Our SFPNet is capable of extracting multi-level contexts and dynamically aggregating them using a gate mechanism. By implementing a channel-wise information query, features that incorporate both local and global contexts are encoded. We also introduce a novel large-scale hybrid-solid LiDAR semantic segmentation dataset for robotic applications. SFPNet demonstrates competitive performance on conventional benchmarks derived from mechanical spinning LiDAR, while achieving state-of-the-art results on benchmark derived from solid-state LiDAR. Additionally, it outperforms existing methods on our novel dataset sourced from hybrid-solid LiDAR. Code and dataset are available at https://***/Cavendish518/SFPNet and https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Event-Triggered Distributed control for Nonlinear Vehicle Platoons with Eso-Based Uncertainty Estimation

SSRN

引用

SSRN 2024年

作者： Zhang, Haisen Wang, Changshun Zhang, Huang School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China Jinan Key Laboratory for New Energy System Control and Intelligent Transportation China

In this paper, the distributed platooning control problem is investigated for a group of automated construction vehicles subject to external disturbances and communication delay. The constant spacing policy is employed, where each follower vehicle’s control law relies solely on information gathered by onboard sensors. This information includes the vehicle’s velocity, acceleration, the preceding vehicle’s velocity, and the inter-vehicle distance. An extended state observer (ESO) is utilized to estimate unmodeled dynamics within the vehicle model. Event-triggered mechanism is introduced to address the transmission delays in communication and mitigate redundant information exchanged among vehicles. Bounded observation errors of the ESOs can be ensured and the inter-vehicle distance errors will converge to a small neighborhood around zero with appropriately designed control parameters. Zeno behavior is excluded from the designed event-triggered mechanism through rigorous proof in this paper. The stability of vehicle platooning controller is analyzed via Lyapunov stability theory and the effectiveness and practicality is validated by using co-simulation of CarSim/Simulink. The vehicle platoon exhibits smooth longitudinal following control under various conditions, including medium and high speeds, with a minimal error range. © 2024, The Authors. All rights reserved.

关键词： Military applications

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