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A Self-organized Coverage Algorithm for UAV Swarms based on Color Information Recognition 4

A Self-organized Coverage Algorithm for UAV Swarms based on ...

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4th international symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Liu, Tao Zhang, Xiao Zhang, Junfu Jiang, Shiqi School of Mechanical Engineering Xi Hua University Chengdu China International Joint Institute of Robotics and Intelligent System School of Automation Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

This paper proposes a self-organized coverage method for unmanned aerial vehicle (UAV) swarm aimed at multi-area coverage. The proposed method attempts to take the color light emitted from the UVA as an important state information. The virtual forces and the color state are incorporated to develop the self-organized coverage algorithm, so that the communication dependency among UAV swarm has been weakened. Simulation experiments show that under the condition of weak communication, the algorithm effectively achieves coverage of multiple areas. Moreover, when part of UAVs in the swarm are damaged or fails, the swarm based on the proposed algorithm can achieve self-organized recovery and continue to complete the task. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation

Generalized Correspondence Matching via Flexible Hierarchica...

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ieee international Conference on robotics and automation (ICRA)

作者： Han, Yu Long, Ziwei Zhang, Yanting Wu, Jin Fang, Zhijun Fan, Rui Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Clear Water Bay Hong Kong Peoples R China Tongji Univ Machine Intelligence & Autonomous Syst MIAS Grp Shanghai Res Inst Intelligent Autonomous Syst Coll Elect & Informat EngnState Key Lab Intellig Shanghai 201804 Peoples R China Tongji Univ Frontiers Sci Ctr Intelligent Autonomous Syst Shanghai 201804 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM.

关键词： Semantic Segmentation

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Extending the Target-Search Algorithm for Kilobots by Adding Random Walk Behavior 9

Extending the Target-Search Algorithm for Kilobots by Adding...

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9th international Conference on automation, robotics and Applications (ICARA)

作者： Troxler, Michael Dornberger, Rolf Hanne, Thomas Univ Appl Sci & Arts Northwestern Switzerland Sch Business Olten Basel Switzerland Univ Appl Sci & Arts Northwestern Switzerland Inst Informat Syst Basel Switzerland Univ Appl Sci & Arts Northwestern Switzerland Inst Informat Syst Olten Switzerland

ISBN: (纸本)9781665489218

This paper discusses the moving behavior of Kilobots, particularly target search and collision avoidance. When using minimalistic robots (e.g., Kilobots), the approach to develop an algorithm is limited by computational power and sensorial functionality. Therefore, Kilobots help examine the challenges of limited simple medical nanorobots. In this paper, an algorithm is presented which is an extension of target-search and collision avoidance by introducing random walk behavior using the Levy distribution. The results show that the random walk behavior improves the movement of Kilobots in some respects. The robots tend to discover an unknown area more efficiently in a specific time. In addition, the robots are less likely to get stuck on boarders and obstacles. On the other hand, the introduced random walk does not improve the ability of target search and collision avoidance.

关键词： collision avoidance Kilobot random walk target-search Levy distribution V-REP

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StereoVAE: A lightweight stereo-matching system using embedded GPUs

StereoVAE: A lightweight stereo-matching system using embedd...

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ieee international Conference on robotics and automation (ICRA)

作者： Chang, Qiong Li, Xiang Xu, Xin Liu, Xin Li, Yun Miyazaki, Jun Tokyo Inst Technol Sch Comp Tokyo 1528550 Japan Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo 1350064 Japan

ISBN: (纸本)9798350323658

We propose a lightweight system for stereo-matching using embedded graphic processing units (GPUs). The proposed system overcomes the trade-off between accuracy and processing speed in stereo matching, thus further improving the matching accuracy while ensuring real-time processing. The basic idea is to construct a tiny neural network based on a variational autoencoder (VAE) to achieve the upscaling and refinement a small size of coarse disparity map. This map is initially generated using a traditional matching method. The proposed hybrid structure maintains the advantage of low computational complexity found in traditional methods. Additionally, it achieves matching accuracy with the help of a neural network. Extensive experiments on the KITTI 2015 benchmark dataset demonstrate that our tiny system exhibits high robustness in improving the accuracy of coarse disparity maps generated by different algorithms, while running in real-time on embedded GPUs.

关键词： Graphics processing unit

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Model Predictive fuzzy Control of the Degree of automation Optimizing Robot Health 17

Model Predictive fuzzy Control of the Degree of Automation O...

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17th ieee international symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Braun, Christian Alexander Ramesh, Aniketh Rothfus, Simon Chiou, Manolis Stolkin, Rustam Hohmann, Soren Karlsruhe Institute of Technology Institute of Control Systems Karlsruhe Germany University of Birmingham Extreme Robotics Lab Birmingham United Kingdom

ISBN: (纸本)9798350321104

Adjusting operator support in human-machine systems is a promising way of combining operator involvement with high overall system performance. Adaptive automation aims to achieve this goal without burdening the operator with the task of selecting and setting the desired amount of support. In this work, two novel adaptive automations are presented. We use the performance measure of 'robot health' to formulate the optimal control problem of maximizing the robot health of a human-robot system through the adaption of operator support to develop two model predictive controllers. The first one considers discrete levels of operator support, or levels of automation, the second one uses the continuous conception of the degree of automation. We report on a proof-of-concept simulation study evaluating the proposed model predictive controllers in a collaborative teleoperation of a mobile robot;the results demonstrate the ability of both model predictive controllers to successfully arbitrate control between the operator and the robot's controller to maximize robot health. © 2023 ieee.

关键词： Fuzzy control

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CINTI 2024 - ieee 24th international symposium on computational intelligence and Informatics, Proceedings

CINTI 2024 - IEEE 24th International Symposium on Computatio...

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24th ieee international symposium on computational intelligence and Informatics, CINTI 2024

ISBN: (纸本)9798350353433

The proceedings contain 45 papers. The topics discussed include: digital twins for space robotics applications;ai-driven engineering: redefining possibilities and problem-solving;towards fully automated optimization with agile workflows and large language models;an agent-based model of avascular tumor growth;a weighted consensus measure for group decision-making with dichotomous inputs;lower and upper bounds on the vagueness values of logical operations in pliant systems;advanced time series embeddings using prior knowledge of change point information;machine learning in ASL fingerspelling recognition: a literature review;exploring fuzzy signatures in sensor fusion: a comparative study with the complementary filter;a theoretical review of mitigation techniques for thick client application vulnerabilities;attention management: extending in progress;tabu search algorithm for cost optimization in multimodal transport;urban rehabilitation and the reflection of spatial;and hybrid quantum approximate algorithms for solving flow shop scheduling problem.

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Achieving Reinforcement Learning Speedup via Quantum Kernel Estimation 29

Achieving Reinforcement Learning Speedup via Quantum Kernel ...

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29th Annual ieee international symposium on Technology and Society (ISTAS)

作者： Gilvarry, John Emeakaroha, Vincent C. Munster Technol Univ Comp Sci Dept Cork Ireland

ISBN: (纸本)9798350324860

Reinforcement learning is playing a crucial role in sustainable developments and enabling wide applications in our daily lives ranging from self-driving cars, robotics, automation, personalised product suggestions, user notifications and many more. Kernel-based reinforcement learning holds theoretical guarantees of convergence to a unique solution for decision function approximation. This has motivated the development of implementations that exploit the advantage of kernel methods whilst overcoming the 'curse of dimensionality'. The theoretical properties of kernel-based methods also support the combination of learning agents in loosely coupled systems into a single combined model for composable learning. Learning speedup is achievable by introducing expert knowledge to the algorithm through the kernel, which reduces variance by increasing bias and thus learning speed. Despite the theoretical promise of kernel based reinforcement learning methods, and continued evolution of more efficient algorithms, practical implementation are not prevalent. Research in quantum machine learning has successfully applied quantum algorithms to achieve computational speed-ups for various machine-learning tasks, and has also identified potential quantum advantage through the identification of patterns not accessible to classical data mapping. To date, this has not been applied to kernel based reinforcement learning. Recent work in the field of quantum learning suggests an important duality between quantum machine learning and kernel machine learning methods. Therefore, this paper proposes a framework to complement kernel-based reinforcement learning with quantum generated feature maps for function approximation to improve learning speed. This has positive consequences for optimisation of reinforcement learning techniques already in use to support sustainability targets in applications such as industry innovation and infrastructure, power generation management, data centre cooling and s

关键词： Kernel Based Reinforcement Learning Quantum Machine Learning Quantum Kernel Estimation Artificial intelligence Sustainable Development

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations 33

A History Information-Based A* Algorithm for the Path Planni...

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33rd international symposium on Industrial Electronics (ISIE)

作者： Wang, Ke Liang, Wei Shi, Huaguang Zhang, Jialin Wang, Qi Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Henan Univ Sch Artificial Intelligence Zhengzhou Peoples R China

ISBN: (纸本)9798350394085;9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： multiple alternative destinations path planning history information A

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The underwater acoustic recognition network based on dynamic attention 4

The underwater acoustic recognition network based on dynamic...

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4th international symposium on Artificial intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhang, Wenwu Shi, Liangchen Xu, Jinyan Jiangsu Automation Research Institute JARI Lianyungang China Jiangsu Automation Research Institute JARI Shang hai China

ISBN: (纸本)9798331541729

This paper proposes an innovative underwater acoustic target recognition network framework, BiTransKan, which integrates Dynamic Sparse Attention (BiFormer), a Dual Dynamic Mixing Module (D-Mixer), and interpretable Kolmogorov-Arnold Networks (KAN), significantly improving recognition efficiency and accuracy in complex underwater acoustic environments. Experimental results based on the DeepShip dataset show that the proposed model outperforms existing methods in terms of accuracy, computational efficiency, and model interpretability, providing a new technical solution and development direction for future research and practical applications in underwater acoustic target recognition. © 2024 ieee.

关键词： Underwater acoustics

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CoDANet: Hardware-Aware Neural Architecture Search for Optimized Deep Learning on FPGAs 2

CoDANet: Hardware-Aware Neural Architecture Search for Optim...

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2nd international Conference on Artificial intelligence and automation Control, AIAC 2024

作者： Lan, Zheng Wang, Bo Liu, Senting Shi, Xiupeng School of Microelectronics Shanghai University Shanghai China School of Artificial Intelligence Chenzhou Score Vocational College Hunan China

ISBN: (纸本)9798331529147

Deep neural networks have significantly advanced scientific discovery and engineering. However, their substantial computational and storage requirements hinder deployment on resource-constrained systems, particularly robotics and edge intelligence, where efficiency and low latency are essential. Herein, we present CoDANet, a hardware-aware Neural Architecture Search (NAS) framework designed to optimize neural network models for FPGA deployment by balancing network performance with hardware resource utilization. By integrating hardware feed-back into the NAS process, CoDANet simultaneously enhances network accuracy and optimizes FPGA resource usage. Experiments demonstrate that CoDANet achieves substantial reductions in hardware consumption across various scenarios, such as an 85.3% reduction in Lookup Table (LUT) usage with only a 1.6% decrease in AUC under performance-prioritized settings. These results highlight CoDANet's effectiveness in designing neural networks tailored for efficient deployment on robotics and edge systems. © 2024 ieee.

关键词： Table lookup

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