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17th International Symposium on Combinatorial Search, SoCS 2024

作者： Chen, Zhe Harabor, Daniel Li, Jiaoyang Stuckey, Peter J. Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Robotics Institute Carnegie Mellon University Pittsburgh United States OPTIMA Australian Research Council ITTC Melbourne Australia

ISBN: (纸本)9781577358916

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that asks us to compute collision-free paths for a team of agents, all moving across a shared map. Existing scalable approaches struggle as the number of agents grows, as they typically plan free-flow optimal paths, which creates congestion. To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths. Empirically, we report large improvements in overall throughput for lifelong MAPF while coordinating more than ten thousand agents. © 2024, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Multi agent systems

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Incentive Mechanism for Throughput Enhancement in Blockchain-based Energy Trading System

Incentive Mechanism for Throughput Enhancement in Blockchain...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei The Chinese University of Hong Kong Department of Information Engineering Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China City University of Hong Kong Department of Computer Science Hong Kong The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

ISBN: (纸本)9798350333336

In a blockchain-based energy trading system, prosumers engage in energy trading and simultaneously participate in blockchain mining to process trading transactions. However, as blockchain mining consumes energy, prosumers may lack incentives to participate, resulting in a low blockchain throughput and negatively affecting the energy trading market. To address this issue, we propose a Two-Piece Linear Compensation (TPLC) mechanism to incentivize sufficient participation for blockchain mining. We model the mechanism design as a two-stage game, wherein the system designer proposes the mechanism in Stage I, and prosumers decide on their energy allocation strategies in Stage II. We analyze the complex and coupled interactions of prosumers and uncover the reason for the low throughput issue: a self-interested prosumer neglects the positive externality of energy allocation to the blockchain, as it does not benefit him. Despite the diverse objectives of prosumers and the large search space of the mechanism, we demonstrate that the distributed TPLC mechanism achieves the maximum social welfare of a centralized decision benchmark. The numerical analysis compares the TPLC mechanism with linear compensation and no compensation benchmarks. Our results show that the TPLC mechanism achieves the highest social welfare and average payoff for prosumers. Additionally, the TPLC mechanism supports a large energy market without sacrificing the user experience. © 2023 IEEE.

关键词： Blockchain

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Enhancing reliability assessment of curved low-stiffness track-viaducts with an adaptive surrogate-based approach emphasizing track dynamic geometric state

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Journal of Central South University 2024年第11期31卷 4262-4275页

作者： CHENG Fang LIU Hui YANG Rui Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for *** overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is *** them,hyperparameter optimization algorithm’s effectiveness is confirmed through test ***,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is ***,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing ***,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based *** reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,*** TDGS mean management index,i.e.,track quality index,has reliability value of *** findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.

关键词： reliability assessment track dynamic geometric state hybrid machine learning algorithm adaptive learning strategy probability density evolution method

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MTPA-based Haptic Feedback Controller for Master Manipulator in Minimally Invasive Surgery

MTPA-based Haptic Feedback Controller for Master Manipulator...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhao, Wenda Chen, Jian Hong, Yilun Liu, Hongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Hong Kong School of Biomedical Engineering and Imaging Sciences King's College London LondonSE1 7EU United Kingdom

ISBN: (纸本)9781665481090

Haptic feedback is critical for teleoperation in surgical robots, particularly in Natural Orifice Transluminal Endoscopic Surgery (NOTES). This paper introduces a haptic controller designed to enhance force generation efficiency and resolution, surpassing traditional motor control frameworks. We propose a Maximum Torque Per Ampere (MTPA)-based Field-Oriented Control (FoC) strategy for haptic kinesthetic feedback controller, optimizing the performance of a master manipulator's joint with a brushless DC (BLDC) motor under stalled and ultra-slow conditions. This approach enables a master manipulator with three degrees of freedom (DoF) for force feedback. Its effectiveness is validated through simulator and surgical trials, demonstrating improved haptic feedback in surgical applications. © 2024 IEEE.

关键词： Brushless DC motors

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Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion

Scaling Diffusion Models to Real-World 3D LiDAR Scene Comple...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lucas Nunes Rodrigo Marcuzzi Benedikt Mersch Jens Behley Cyrill Stachniss Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D LiDAR sensors are commonly used to collect sparse 3D point clouds from the scene. However, compared to human perception, such systems struggle to deduce the unseen parts of the scene given those sparse point clouds. In this matter, the scene completion task aims at predicting the gaps in the LiDAR measurements to achieve a more complete scene representation. Given the promising results of recent diffusion models as generative models for images, we propose extending them to achieve scene completion from a single 3D LiDAR scan. Previous works used diffusion models over range images extracted from LiDAR data, directly applying image-based diffusion methods. Distinctly, we propose to directly operate on the points, reformulating the noising and denoising diffusion process such that it can efficiently work at scene scale. Together with our approach, we propose a regularization loss to stabilize the noise predicted during the denoising process. Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input, producing a scene with more details compared to state-of-the-art scene completion methods. We believe that our proposed diffusion process formulation can support further research in diffusion models applied to scene-scale point cloud data. 1 1 Code: https://***/PRBonn/LiDiff

关键词： Point cloud compression Computer vision Solid modeling Laser radar Three-dimensional displays Noise reduction Diffusion processes

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MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering

MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jingjing Jiang Nanning Zheng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

Recently, finetuning pretrained vision-language models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in VQA. However, as VLMs scale, it becomes computationally expensive, storage inefficient, and prone to overfitting when tuning full model parameters for a specific task in low-resource settings. Although current parameter-efficient tuning methods dramatically reduce the number of tunable parameters, there still exists a significant performance gap with full finetuning. In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. Specifically, MixPHM is a lightweight module implemented by multiple PHM-experts in a mixture-of-experts manner. To reduce parameter redundancy, we reparameterize expert weights in a low-rank subspace and share part of the weights inside and across MixPHM. Moreover, based on our quantitative analysis of representation redundancy, we propose Redundancy Regularization, which facilitates MixPHM to reduce task-irrelevant redundancy while promoting task-relevant correlation. Experiments conducted on VQA v2, GQA, and OK-VQA with different low-resource settings show that our MixPHM outperforms state-of-the-art parameter-efficient methods and is the only one consistently surpassing full finetuning.

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TASF: Terrestrial-Aerial Synergistic Framework for a New Generation of Intelligent Transportation 27

TASF: Terrestrial-Aerial Synergistic Framework for a New Gen...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Wang, Cong Nie, Dujun Zhang, Ruijun Teng, Siyu Chen, Long Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hong Kong Baptist University Kowloon Hong Kong Waytous Inc. Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798331505929

Terrestrial-Aerial Synergistic Framework (TASF) represents a novel approach to intelligent transportation, integrating terrestrial and aerial vehicles to enhance urban transportation efficiency and capacity. In this paper, we offer an in-depth analysis of TASF, emphasizing its innovative applications in urban transportation, parcel delivery, and emergency response services. TASF incorporates a five-layer architecture comprising perception, communication, planning, control, and interaction. With the five-layer architecture, TASF can achieve effective collaboration among terrestrial and aerial vehicles, encompassing two principal features: terrestrial-aerial synergistic parallel perception and multi-agent parallel management. Parallel perception results from fusing Bird's Eye View (BEV) representations from Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), whereas decentralized multi-agent management employs foundational models to enhance decision-making capabilities. Additionally, we consider challenges and potential solutions related to TASF, including safety, reliability, energy efficiency, management and integration. TASF marks a paradigm-shifting leap in the realm of intelligent transportation systems, providing a scalable and efficacious solution tailored to the dynamic requirements of urban mobility and emergency services. © 2024 IEEE.

关键词： Decision making

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Open-World Semantic Segmentation Including Class Similarity

Open-World Semantic Segmentation Including Class Similarity

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Matteo Sodano Federico Magistri Lucas Nunes Jens Behley Cyrill Stachniss Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world se-mantic segmentation, i.e., the variant of interpreting image data in which objects occur that have not been seen during training. We propose a novel approach that performs accu-rate closed-world semantic segmentation and, at the same time, can identify new categories without requiring any ad-ditional training data. Our approach 1 1 Code: https://***/PRBonn/ContMAV additionally provides a similarity measure for every newly discovered class in an image to a known category, which can be useful information in downstream tasks such as planning or mapping. Through extensive experiments, we show that our model achieves state-of-the-art results on classes known from training data as well as for anomaly segmentation and can distinguish between different unknown classes.

关键词： Training Computer vision Semantic segmentation Machine vision Training data Computer architecture Data models

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Personalized gait trajectory generation based on anthropometric features using Random Forest

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Journal of Ambient intelligence and Humanized Computing 2023年第12期14卷 15597-15608页

作者： Ren, Shixin Wang, Weiqun Hou, Zeng-Guang Chen, Badong Liang, Xu Wang, Jiaxing Peng, Liang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Using lower limb rehabilitation robots (LLRRs) to help stroke patients recover their walking ability is attracting more and more attention presently. Previous studies have shown that gait rehabilitation training with natural gait pattern can improve the therapeutic outputs. However, how to generate the personalized gait trajectory has not been well researched. In this paper, a personalized gait generation method based anthropometric features is proposed. Firstly, gait trajectories are fitted and simplified into Fourier coefficient vectors, which are used to represent gait trajectories. Secondly, fourteen body features are used to generate the personalized gait trajectories and the feature set is further optimized based on the minimal redundancy maximal relevance criterion for easy application on the LLRR. Then, the relationship between the optimized feature set and gait trajectories is modeled by using the RF algorithm. Finally, the performance of the proposed method is demonstrated by several comparison experiments. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Trajectories

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Research on underwater target tracking method based on IPSO-LSTM

Research on underwater target tracking method based on IPSO-...

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2024 International Conference on Mechatronic Engineering and artificial intelligence, MEAI 2024

作者： Guo, Xaingyun Liu, Kaizhou Wang, Yanhong School of Artificial Intelligence Shenyang University of Technology Liaoning Province Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Province Shenyang110016 China

ISBN: (纸本)9781510689176

When tracking underwater targets, due to the limited performance of detection equipment and complex environment interference, the target data is lost in the transmission process, which affects the accuracy of target trajectory prediction. The traditional target trajectory prediction method for establishing mathematical model has some limitations and difficulty, which can not meet the requirements of real-time and accurate target trajectory prediction in underwater field. An IPSO-LSTM trajectory prediction model for underwater targets is proposed. In this method, the motion characteristics of underwater targets are analyzed, and the learning rate of LSTM is optimized by optimizing the inertia weight of particle swarm and improving the learning factor. The real trajectory data of Autonomous Underwater Vehicle were used for experimental and comparative analysis. The results show that IPSO-LSTM combined with PSO has higher prediction accuracy and better fitting effect than LSTM and PSO-LSTM. The proposed model provides a new idea for underwater moving target tracking, which has better accuracy and stability. © 2025 SPIE.

关键词： Aircraft detection

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