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2nd IEEE International Conference on artificial intelligence in Everything, AIE 2022

作者： Ameen, Zubaida Said Ibrahim, Abdullahi Umar Mubarak, Auwalu Saleh Al-Turjman, Fadi Near East University Research Genter for Ai and IoT Ai and Robotics Institute Artificial Intelligence Dept. Turkey Near East University Biomedical Engineering Nicosia Turkey Ai and Robotics Institute NearEast University Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

Gene editing with CRISPR-Cas12 method has immense applications in diverse organisms from bacteria, and plants to humans especially in treating genetic diseases. GuideRNA and Cas enzyme are the two components required by the Cas12 gene editing method. However, the CRISPR-Cas12 system varies depending on the guideRNA sequence used. Therefore, it is important to develop improved methods for designing guideRNA sequences needed for gene editing. Here, we utilized a combination of convolutional neural networks and long short-term memory a type of recurrent neural network (bi-LSTM) model to classify guideRNA as either active or inactive, and the addition of an embedding layer for encoding guideRNA sequence improved model performance. Accuracy, AUC and F1-score were used to evaluate CNN-LSTM and achieved outstanding performance. CNN-LSTM will help in the design of guideRNA for genes with CRISPR-Cas12 system. © 2022 IEEE.

关键词： Embeddings

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Task Allocation for Heterogeneous Multi-Robot Systems with Diverse Capabilities

Task Allocation for Heterogeneous Multi-Robot Systems with D...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Goeun Lee Donggil Lee Yoonseob Lim Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul Korea Department of Electrical Engineering Incheon National University Incheon Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper presents an algorithm for task allocation in a team of robots with diverse capabilities, aiming to minimize overall costs. Each robot’s abilities are described by a capability vector, and the algorithm forms subgroups within the team to ensure they can collectively complete individual tasks. We particularly address cases where each robot can only handle a subset of the tasks due to physical limitations. The algorithm’s effectiveness and practical application are demonstrated through simulations, which validate its ability to optimize task allocation in multi-robot teams.

关键词： Costs Automation Control systems Vectors Resource management Multi-robot systems Robots

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2354-2369页

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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Time-optimal Planning for the Quadrotor Transportation System Landing a Payload on the Mobile Platform 11

Time-optimal Planning for the Quadrotor Transportation Syste...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Liang, Xiao Gao, Jinjiang Han, Jianda Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9781665425278

Nowadays, aerial transportation has been widely used in various aspects, such as fire fighting, life-rescue, emergency-supplies delivery, and etc. Existing works for quadrotor transportation systems concentrate on the objectives of quadrotor positioning and payload swing suppression. Nevertheless, the process to land the payload onto a mobile platform is seldom discussed. In this paper, a time-optimal trajectory generation algorithm for unmanned quadrotor transportation system is proposed with effective quadrotor positioning, payload antiswing and landing performance. Specifically, the system model is discretized and various constraints are included to guarantee safe transportation, based on which, a nonlinear programming problem is constructed and solved. Finally, a series of simulation tests are implemented to verify the effectiveness of the proposed trajectory generation method. © 2021 IEEE.

关键词： Antennas

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Design and Implementation of a Robotic Ping-Pong Server System

Design and Implementation of a Robotic Ping-Pong Server Syst...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Dong, Yibing Ji, Xiaoqiang Sun, Zhenglong School of Science and Engineering The Chinese University of HongKong Guangdong Shenzhen China The Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of HongKong Shenzhen China

ISBN: (纸本)9781665481090

A robotic Ping-Pong server system could play an important role in skill training for players. Current available systems in the market provides different modes with rough control of the ball landing point. In this paper, we present the design and implementation of a robotic Ping-Pong server system to achieve precise ball landing point. A cost-effective multimodal landing point detection system is proposed to detect the landing point by fusing video and audio signals. A lookup table is used to relate the server launching parameters with the landing points. To keep the lookup table dynamic, an iterative learning control (ILC) algorithm is adopted to automatically update the lookup table in case of environmental changes. The ILC algorithm is tested and validated in simulation. Results showed the system was able to precisely serve the ball to the desired point automatically. © 2022 IEEE.

关键词： Table lookup

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RealisHuman: A Two-Stage Approach for Refining Malformed Human Parts in Generated Images

arXiv

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arXiv 2024年

作者： Wang, Benzhi Zhou, Jingkai Bai, Jingqi Yang, Yang Chen, Weihua Wang, Fan Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong Alibaba Group China

In recent years, diffusion models have revolutionized visual generation, outperforming traditional frameworks like Generative Adversarial Networks (GANs). However, generating images of humans with realistic semantic parts, such as hands and faces, remains a significant challenge due to their intricate structural complexity. To address this issue, we propose a novel post-processing solution named RealisHuman. The RealisHuman framework operates in two stages. First, it generates realistic human parts, such as hands or faces, using the original malformed parts as references, ensuring consistent details with the original image. Second, it seamlessly integrates the rectified human parts back into their corresponding positions by repainting the surrounding areas to ensure smooth and realistic blending. The RealisHuman framework significantly enhances the realism of human generation, as demonstrated by notable improvements in both qualitative and quantitative metrics. Code is available at https://***/Wangbenzhi/RealisHuman. © 2024, CC BY.

关键词： Generative adversarial networks

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Vision-Language Model-based Physical Reasoning for Robot Liquid Perception

arXiv

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arXiv 2024年

作者： Lai, Wenqiang Gao, Yuan Lam, Tin Lun Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

There is a growing interest in applying large language models (LLMs) in robotic tasks, due to their remarkable reasoning ability and extensive knowledge learned from vast training corpora. Grounding LLMs in the physical world remains an open challenge as they can only process textual input. Recent advancements in large vision-language models (LVLMs) have enabled a more comprehensive understanding of the physical world by incorporating visual input, which provides richer contextual information than language alone. In this work, we proposed a novel paradigm that leveraged GPT-4V(ision), the state-of-the-art LVLM by OpenAI, to enable embodied agents to perceive liquid objects via image-based environmental feedback. Specifically, we exploited the physical understanding of GPT-4V to interpret the visual representation (e.g., time-series plot) of non-visual feedback (e.g., F/T sensor data), indirectly enabling multimodal perception beyond vision and language using images as proxies. We evaluated our method using 10 common household liquids with containers of various geometry and material. Without any training or fine-tuning, we demonstrated that our method can enable the robot to indirectly perceive the physical response of liquids and estimate their viscosity. We also showed that by jointly reasoning over the visual and physical attributes learned through interactions, our method could recognize liquid objects in the absence of strong visual cues (e.g., container labels with legible text or symbols), increasing the accuracy from 69.0%—achieved by the best-performing vision-only variant— to 86.0%. Copyright © 2024, The Authors. All rights reserved.

关键词： Containers

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Deep Reinforcement Learning-Based Swing Suppression Control for Unmanned Multirotor Transportation System

Deep Reinforcement Learning-Based Swing Suppression Control ...

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IEEE International Conference on Mechatronics and Automation

作者： Teng Wang Shunxuan Wang Yi Chai Xiao Liang Institute of Robotics and Automatic Information System College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin P. R. China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Unmanned multirotors have demonstrated excellent flexibility in complex real-world scenarios, making them promising for aerial payload transportation. However, it is challenging to achieve stable control of the unmanned multirotor transportation system due to its double under-actuated property. Traditional control methods are limited by model complexity and parameter tuning difficulty, resulting in deficient control capabilities. In response to these shortcomings, an end-to-end controller based on deep reinforcement learning is proposed in this article, ensuring that the payload arrives at the target while suppressing the process swing. Based on the Soft Actor Critic (SAC) algorithm and the design of state-action space, the efficiency of the controller is enhanced. By comparing with PID and Fuzzy PID control methods, the proposed approach presents superior payload swing suppression capacity with comparable position control performance.

关键词： PI control Simulation Transportation Process control Position control Aerospace electronics Deep reinforcement learning

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ActiveGS: Active Scene Reconstruction using Gaussian Splatting

arXiv

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arXiv 2024年

作者： Jin, Liren Zhong, Xingguang Pan, Yue Behley, Jens Stachniss, Cyrill Popovic, Marija Center for Robotics University of Bonn Germany MAVLab TU Delft Netherlands Lamarr Institute for Machine Learning and Artificial Intelligence Germany

robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene using an on-board RGB-D camera. We propose a hybrid map representation that combines a Gaussian splatting map with a coarse voxel map, leveraging the strengths of both representations: the high-fidelity scene reconstruction capabilities of Gaussian splatting and the spatial modelling strengths of the voxel map. The core of our framework is an effective confidence modelling technique for the Gaussian splatting map to identify under-reconstructed areas, while utilising spatial information from the voxel map to target unexplored areas and assist in collision-free path planning. By actively collecting scene information in under-reconstructed and unexplored areas for map updates, our approach achieves superior Gaussian splatting reconstruction results compared to state-of-the-art approaches. Additionally, we demonstrate the applicability of our active scene reconstruction framework in the real world using an unmanned aerial vehicle. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Efficient and Cost-effective Vehicle Recruitment for HD Map Crowdsourcing

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IEEE Transactions on Mobile Computing 2025年

作者： Ye, Wentao Luo, Yuan Liu, Bo Huang, Jianwei The Chinese University of Hon g Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering Shenzhen Guangdong518172 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Shenzhen Guangdong518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong518172 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen Guangdong518172 China

The high-definition (HD) map is a cornerstone of autonomous driving. The crowdsourcing paradigm is a cost-effective way to keep an HD map up-to-date. Current HD map crowdsourcing mechanisms aim to enhance HD map freshness within recruitment budgets. However, many overlook unique and critical traits of crowdsourcing vehicles, such as random arrival and heterogeneity, leading to either compromised map freshness or excessive recruitment costs. Furthermore, these characteristics complicate the characterization of the feasible space of the optimal recruitment policy, necessitating a method to compute it efficiently in dynamic transportation scenarios. To overcome these challenges, we propose an efficient and cost-effective vehicle recruitment (ENTER) mechanism. Specifically, the ENTER mechanism has a threshold structure and balances freshness with recruitment costs while accounting for the vehicles' random arrival and heterogeneity. It also integrates the bound-based relative value iteration (RVI) algorithm, which utilizes the threshold-type structure and upper bounds of thresholds to reduce the feasible space and expedite convergence. Numerical results show that the proposed ENTER mechanism increases the HD map company's payoff by 23.40% and 43.91% compared to state-of-the-art mechanisms that do not account for vehicle heterogeneity and random arrivals, respectively. Furthermore, the bound-based RVI algorithm in the ENTER mechanism reduces computation time by an average of 18.91% compared to the leading RVI-based algorithm. © 2025 IEEE.

关键词： Markov processes

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