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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Junnan Zhang, Gang Hu, Xiaolin Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (纸本)9798331516147

LiDAR-based 3D object detection is a critical component of perception systems in autonomous driving. Existing detectors typically rely on dense feature maps for 3D object prediction. However, as the perception range expands, the computational overhead associated with dense feature maps increases quadratically, creating significant challenges for scaling to long-range detection scenarios. To address this issue, fully sparse detectors have recently been proposed, but they often suffer from suboptimal performance or excessively complex architectures. In this paper, we present CHNet, a fully sparse detector designed to overcome these limitations by introducing a cascade head module, which consists of two sequential heads. By generating coarse object predictions, creating new feature points based on centers of the predicted objects, and aggregating features of the entire object to these new center points, CHNet enhances the qualities of object feature representations, achieving higher detection performance. Notably, CHNet effectively addresses the center feature missing problem in LiDAR-based 3D object detection by adding at most a hundred new feature points, offering a potential avenue for future research. On the widely used Waymo Open and Argoverse2 datasets, CHNet achieves impressive results without any additional embellishments. © 2025 IEEE.

关键词： Object recognition

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Improved redundancy of an islanded microgrid via seamless transition between operation modes

Improved redundancy of an islanded microgrid via seamless tr...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Breve, Matheus Montanini Michalke, Gabriele Bohnet, Bernd Kowal, Julia Strunz, Kai Corporate Research Robert Bosch GmbH Renningen Germany Institute of Energy and Automation Technology Technical University of Berlin Berlin Germany

ISBN: (纸本)9798350396782

关键词： availability droop grid-following grid-forming microgrid modular redundancy seamless transition

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Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent Systems

arXiv

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arXiv 2025年

作者： Weber, Jakob Gurtner, Markus Alt, Benedikt Trachte, Adrian Kugi, Andreas The Center for Vision Automation & Control AIT Austrian Institute of Technology GmbH Vienna Austria The Robert Bosch GmbH Renningen Germany The Automation and Control Institute TU Wien Austria The AIT Austrian Institute of Technology GmbH Vienna Austria

Feedforward control (FF) is often combined with feedback control (FB) in many control systems, improving tracking performance, efficiency, and stability. However, designing effective data-driven FF controllers in multi-agent systems requires significant data collection, including transferring private or proprietary data, which raises privacy concerns and incurs high communication costs. Therefore, we propose a novel approach integrating Federated Learning (FL) into FF control to address these challenges. This approach enables privacy-preserving, communication-efficient, and decentralized continuous improvement of FF controllers across multiple agents without sharing personal or proprietary data. By leveraging FL, each agent learns a local, neural FF controller using its data and contributes only model updates to a global aggregation process, ensuring data privacy and scalability. We demonstrate the effectiveness of our method in an autonomous driving use case. Therein, vehicles equipped with a trajectory-tracking feedback controller are enhanced by FL-based neural FF control. Simulations highlight significant improvements in tracking performance compared to pure FB control, analogous to model-based FF control. We achieve comparable tracking performance without exchanging private vehicle-specific data compared to a centralized neural FF control. Our results underscore the potential of FL-based neural FF control to enable privacy-preserving learning in multi-agent control systems, paving the way for scalable and efficient autonomous systems applications. © 2025, CC BY-NC-ND.

关键词： Differential privacy

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Cooperation of Human and Active Learning based AI for Fast and Precise Complaint Management

Cooperation of Human and Active Learning based AI for Fast a...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Hennebold, Christoph Mei, Xiaodong Mailahn, Ortwin Huber, Marco F. Mannuß, Oliver Fraunhofer Institute for Manufacturing Engineering and Automation Ipa Germany Bosch Rexroth Ag - Assembly Technology Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In highly competitive markets, customer loyalty plays an increasingly important role for companies. An important aspect is the recording and processing of customer complaints, on the one hand for problem-solving and on the other hand for internal process optimization. The complaint handling process extends over several phases, in which different people with varying expertise and experience may be involved, which poses a major challenge in order to achieve Consistent quality in the recording process of complaints. To address this issue, this work presents a robust active learning based AI system that allows using existing expert knowledge to create more reliable classifications in a shorter amount of time. The implemented prototype shows a decrease of up to 86% in the average processing time of complaints, with an average increase of up to 37% in the classification accuracy. © 2022 IEEE.

关键词： Quality management

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Combining Federated Learning and Control: A Survey

arXiv

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arXiv 2024年

作者： Weber, Jakob Gurtner, Markus Lobe, Amadeus Trachte, Adrian Kugi, Andreas The Center for Vision Automation & Control AIT Austrian Institute of Technology GmbH Vienna Austria The Robert Bosch GmbH Renningen Germany The Automation and Control Institute TU Wien Austria The AIT Austrian Institute of Technology GmbH Vienna Austria

This survey provides an overview of combining Federated Learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real-world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state-of-the-art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modeling over controller design, focusing on adaptive control, to knowledge transfer in multi-agent decision-making systems. © 2024, CC BY.

关键词： Data privacy

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A Fully Sparse Detector with Cascade Heads for LiDAR-based 3D Object Detection

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Junnan Chen Gang Zhang Xiaolin Hu Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

关键词： Three-dimensional displays Aggregates Detectors Object detection Information processing Computer architecture Feature extraction Autonomous vehicles Intelligent control

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Improved redundancy of an islanded microgrid via seamless transition between operation modes

Improved redundancy of an islanded microgrid via seamless tr...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Matheus Montanini Breve Gabriele Michalke Bernd Bohnet Julia Kowal Kai Strunz Corporate Research Robert Bosch GmbH Renningen Germany Institute of Energy and Automation Technology Technical University of Berlin Berlin Germany

This paper presents a hybrid islanded microgrid with DC/AC clusters, each fed by 10 fuel cells and a buffer battery, and the devised control algorithm for the seamless transition between droop-grid-forming and grid-following cluster operation modes. The transition is triggered after a battery failure, which can be detected by analysing the behaviour of the cluster DC-link voltage. This concept allows for a highly modular, scalable, and redundant microgrid. With this feature, the continued operation of the individual clusters is no longer tied to the state of their energy-storage units. This changes the functional structure of the microgrid, increasing its ***: BESS: battery energy-storage system, BFDS: battery failure detection system, CL: cluster, CPS: constant power source, CPL: constant power load, FC: fuel cell, GFM: grid forming, GFL: grid following, LPF: low-pass filter, MSC: master-slave controller, PA: power adjustment, PLL: phase locked loop, RS: resynchronization, SRF: synchronous reference frame.

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Force control for Robust Quadruped Locomotion: A Linear Policy Approach

Force control for Robust Quadruped Locomotion: A Linear Poli...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Aditya Shirwatkar Vamshi Kumar Kurva Devaraju Vinoda Aman Singh Aditya Sagi Himanshu Lodha Bhavya Giri Goswami Shivam Sood Ketan Nehete Shishir Kolathaya Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bengaluru Department of Computer Science & Automation Indian Institute of Science Bengaluru Indian Institute of Technology Kharagpur Department of Computer Science & Automation Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bengaluru

This work presents a simple linear policy for direct force control for quadrupedal robot locomotion. The motivation is that force control is essential for highly dynamic and agile motions. We learn a linear policy to generate end-foot trajectory parameters and a centroidal wrench, which is then distributed among the legs based on the foot contact information using a quadratic program (QP) to get the desired ground reaction forces. Unlike the majority of the existing works that use complex nonlinear function approximators to represent the RL policy or model predictive control (MPC) methods with many optimization variables in the order of hundred, our controller uses a simple linear function approximator to represent policy along with only a twelve variable QP for the force distribution. A centroidal dynamics-based MPC method is used to generate reference trajectory data, and then the linear policy is trained using imitation learning to minimize the deviations from the reference trajectory. We demonstrate this compute-efficient controller on our robot Stoch3 in simulation and real-world experiments on indoor and outdoor terrains with push recovery.

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic Control 2025年

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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Utilizing Data Analysis for Optimized Determination of the Current Operational State of Heating Systems 16

Utilizing Data Analysis for Optimized Determination of the C...

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16th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Qarqour, Ahmed Arora, Sahil-Jai Heisenberg, Gernot Rabe, Markus Kleinert, Tobias Bosch Thermotechnik GmbH Junkersstraße 20-24 Neckar Wernau73243 Germany Department of Information Management TH Cologne University Claudiusstraße 1 Köln50678 Germany Department IT in Production and Logistics TU Dortmund University Dortmund44221 Germany Department of Information and Automation Systems for Process and Material Technology RWTH Aachen University Turmstraße 46 Aachen52064 Germany

ISBN: (纸本)9789897587160

In response to the pressing global challenge of climate change, the emphasis on sustainable energy technologies has escalated, spotlighting the critical role of heat pump systems as eco-friendly alternatives for heating and cooling. These systems stand at the forefront of efforts to reduce greenhouse gas emissions and improve energy efficiency. The advent of Internet of Things (IoT) technology has unlocked the potential for comprehensive data collection on the operational intricacies of heat pump systems in real-world settings, offering precious insights into their performance and guiding technological advancements. This paper introduces an analytical approach to optimize air-to-water heat pump systems using time series data from bosch Home Comfort Group's systems. Utilizing Fayyad's data-driven analysis model and the Random Forest algorithm, the study tackles system behavior complexities. Characterized by interpretability crucial for application, it achieves a 97.6% fault detection accuracy. The method encounters difficulties in accurately predicting compressor control faults due to limited data quality and a lack of comprehensive system information. The findings highlight IoT's potential to enhance system efficiency and availability, but also point to the limitations of relying solely on data-driven models for fault prediction in field systems. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Network security

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