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Kinematic models of mobile robots: Types and simulation

AIP Conference Proceedings 2024年第1期3035卷

作者： Keshav Selvaraj S. Harshini Ahilesh R. Ram Anbarasi Muthuswamy Palaniswamy K. Senthil Kumar Department of Robotics and Automation Engineering PSG College of Technology Coimbatore Tamil Nadu India 641004

This paper entails a comparative study between the different types of kinematic models of mobile robots and simulation of the same using MATLAB Software. Four different Kinematic models have been taken into consideration i) Unicycle Kinematic Model, ii) Bicycle Kinematic Model iii) Differential Drive Kinematic Model, and iv) Ackermann Kinematic Model. The different models are simulated using the derivative function to update the state. In order to plot the path of the unicycle, bicycle, and differential-drive robots, the ordinary differential equation (ODE) solver is utilized in order to generate the solutions.

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Image-Goal Navigation Using Refined Feature Guidance and Scene Graph Enhancement

arXiv

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arXiv 2025年

作者： Feng, Zhicheng Chen, Xieyuanli Shi, Chenghao Luo, Lun Chen, Zhichao Liu, Yun-Hui Lu, Huimin The College of Intelligence Science and Technology The National Key Laboratory of Equipment State Sensing and Smart Support National University of Defense Technology China Zhejiang University China Jiangxi University of Science and Technology China The T Stone Robotics Institute Department of Mechanical and Automation Engineering The Chinese University of Hong Kong China

In this paper, we introduce a novel image-goal navigation approach, named RFSG. Our focus lies in leveraging the fine-grained connections between goals, observations, and the environment within limited image data, all the while keeping the navigation architecture simple and lightweight. To this end, we propose the spatial-channel attention mechanism, enabling the network to learn the importance of multi-dimensional features to fuse the goal and observation features. In addition, a self-distillation mechanism is incorporated to further enhance the feature representation capabilities. Given that the navigation task needs surrounding environmental information for more efficient navigation, we propose an image scene graph to establish feature associations at both the image and object levels, effectively encoding the surrounding scene information. Cross-scene performance validation was conducted on the Gibson and HM3D datasets, and the proposed method achieved state-of-the-art results among mainstream methods, with a speed of up to 53.5 frames per second on an RTX3080. This contributes to the realization of end-to-end image-goal navigation in real-world scenarios. The implementation and model of our method have been released at: https://***/nubot-nudt/RFSG. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Leveraging the benefits of digital twin in delivering personalized medicine using IOT

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AIP Conference Proceedings 2024年第1期3035卷

作者： Sanjana Raghavan Oviya Arunachalam Amrita Babu Subashini Govindaraju Department of Robotics and Automation Engineering PSG College of Technology Coimbatore Tamil Nadu India 641004

A digital twin is a computer program that combines real-world data and artificial intelligence to create simulations that predict the performance of a product or process. Through the use of machine learning and big data, digital twin programs can now integrate various data sources to enhance their output, this in turn makes the digital twin a heuristic algorithm that can be well domesticated. The creation of a Digital Twin allows hospitals to monitor and improve their operations through a series of interrelated models. These models can help determine which actions to take to improve patient care. It has allowed researchers to study diseases and develop new drugs and medical devices. Digital Twin technology may also be used to model an individual's genomic makeup and physiological characteristics to create personalized medication. In the long term, digital twin technology can help improve the efficiency and safety of healthcare operations. This paper provides a prototype of a digital twin model that is built based on the Internet of things (IoT). Through the use of digital twin technology, it can also be used to create personalized medicine for each individual, the developed prototype assists in this aspect of healthcare by using artificial intelligence. This paper aims to provide a prototype of a domesticated digital twin that is user-friendly and a viable device for diagnosis and research.

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Path planning for an autonomous fruit harvesting system using A * algorithm

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AIP Conference Proceedings 2024年第1期3035卷

作者： Krishna Rajan Sriram Sindhu Mohan Bindu Bhaskaran Department of Robotics and Automation Engineering PSG College of Technology Coimbatore Tamil Nadu India-641004

The paper describes an autonomous fruit-harvesting system that implements an A* path planning algorithm with the aim of boosting productivity. The proposed system makes use of an unmanned aerial manipulator tethered to a mobile rover at the base and employs the A* algorithm for path planning. The drone is mounted with a 6-DoF manipulator and synchronized cameras for stereo imaging to accurately map the workspace and collect the fruits at a high rate. The drone-based system eliminates any reachability challenges the trees might pose while also resulting in prolonged endurance since it is tethered. The algorithm is used to identify the most efficient paths to harvest the fruit from trees while also minimizing the damage to the tree.

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SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation

arXiv

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arXiv 2022年

作者： Wei, Yi Zhao, Linqing Zheng, Wenzhao Zhu, Zheng Rao, Yongming Huang, Guan Lu, Jiwen Zhou, Jie Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China School of Electrical and Information Engineering Tianjin University China PhiGent Robotics

Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temporal photometric constraints enables self-supervised depth estimation without labels, further facilitating its application. However, most existing methods predict the depth solely based on each monocular image and ignore the correlations among multiple surrounding cameras, which are typically available for modern self-driving vehicles. In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. We apply cross-view self-attention to efficiently enable the global interactions between multi-camera feature maps. Different from self-supervised monocular depth estimation, we are able to predict real-world scales given multi-camera extrinsic matrices. To achieve this goal, we adopt the two-frame structure-from-motion to extract scale-aware pseudo depths to pretrain the models. Further, instead of predicting the ego-motion of each individual camera, we estimate a universal ego-motion of the vehicle and transfer it to each view to achieve multi-view ego-motion consistency. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets DDAD and nuScenes. Code is available at https://***/weiyithu/SurroundDepth. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Control and autonomy of microbots

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AIP Conference Proceedings 2024年第1期3035卷

作者： Bindu Bhaskaran Arikaran Ramesh Babu Oviya Arunachalam Sanjana Raghavan Department of Robotics and Automation Engineering PSG College of Technology Coimbatore Tamil Nadu India - 641004

Microbots are miniature robots that have great potential in applications such as the delivery of drugs to the target organ, invasive surgery, etc., in the medical industry. Micro/nanobots are preferred in comparison to conventional medical robots due to their high accuracy and precision. These robots are operated majorly in closed-loop control to achieve a higher rate of accuracy with the involvement of feedback signals, while they can also be operated in open-loop control depending on the applications. The automatic control theories are implemented to improve the efficiency of microbots and for them to adapt to the working environment. Systematic manipulation of microbots with the implementation of different types of control is stated in this review.

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Intracranial Photodynamic Therapy Strategy Based Magnetic Continuum Robots for Glioblastoma Management

SSRN

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SSRN 2024年

作者： Wu, Junfeng Li, Na Lin, Daojing Zhou, Junjian Song, Yingqiu Liu, Lianqing Jiao, Niandong School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiotherapy Cancer Hospital of China Medical University Shenyang110042 China

Glioblastoma multiforme (GBM), a malignant brain tumor, poses a significant global health challenge because of its high invasiveness and resistance to treatment. Photodynamic therapy (PDT), with its high spatial-temporal selectivity, holds promise for GBM treatment, offering strong local selectivity, minimal systemic side effects, and low resistance. However, delivering photosensitizers across the blood-brain barrier and precisely targeting brain tumors through the skull with a laser present critical challenges to clinical application. Addressing these challenges, the soft, continuous structure and remote magnetic control of magnetic continuum robots (MCRs) offer new possibilities for precise delivery to GBM. Here, we propose an intracranial PDT strategy using MCRs, featuring a magnetic control model based on Gaussian Process Regression and a computer vision-based closed-loop control algorithm. This approach enables automatic delivery of nanomedicines and localized laser irradiation, characterized by simplicity, minimal data needs, and robustness. Additionally, under the combined effects of photodynamic nanoparticles (PNPs) and a laser, both U87 and GL261 cells exhibit high mortality rates, which increase with the concentration of PNPs, laser energy, and duration of exposure. In conclusion, this strategy overcomes PDT's limitations to superficial lesions, offers a novel GBM treatment approach, and constitutes a key milestone for using PDT in GBM treatment. © 2024, The Authors. All rights reserved.

关键词： Photodynamic therapy

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DDGC: Generative deep dexterous grasping in clutter

arXiv

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arXiv 2021年

作者： Lundell, Jens Verdoja, Francesco Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps. In this work, we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against two state-of-the-art methods on 1200 simulated cluttered scenes and 7 real-world scenes. The results show that DDGC outperforms the baselines in synthesizing high-quality grasps and removing clutter. DDGC is also 4-5 times faster than GraspIt!. This, in turn, opens the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods. Code and videos are available at https://***/ddgc/. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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A Distributed Framework for Deep Reinforcement Learning by Consensus

A Distributed Framework for Deep Reinforcement Learning by C...

引用

Chinese Control and Decision Conference, CCDC

作者： Bo Liu Shuang Zhu Peng Sun Qisheng Huang Zhengtao Ding Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China PowerChina Huadong Engineering Corporation Limited Hangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China Department of Electrical and Electronic Engineering University of Manchester Manchester UK

This paper proposes a distributed training framework for deep reinforcement learning algorithms to address large-scale problems with privacy protection. First, we design a hierarchical decentralized communication topology with a server to alleviate the heavy burden on the central server. By pushing the model updating and data storage to the edge side, it not only unleashes the computing potential of the terminal devices but also shortens the response time. Second, the consensus algorithm is applied for all agents over the decentralized topology to approach each other, where each agent only requires neighboring information to achieve consensus. Then, the distributed training framework for deep Q-networks is designed based on the consensus algorithm, which consists of local learning and global consensus. Finally, the simulation demonstrates that the proposed distributed learning deep Q-networks shows better performance than its centralized learning counterpart.

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Implementing Genetic Algorithms for Optimization in Neuro-Cognitive Rehabilitation robotics

Implementing Genetic Algorithms for Optimization in Neuro-Co...

引用

Cognitive robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： L. B. Abhang Ravindra Changala Anudeb Ghosh Prabhakar S Manage Vuda Sreenivasa Rao B Kiran Bala Automation and Robotics Dept Pravara Rural Engineering College Loni Under. SPPU University Pune Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of Environmental Studies Siksha - Bhavana Visva-Bharati Santiniketan Department of Electronics and Communication KLE Technological University's MSSCET Belagavi Campus Belagavi Karnataka Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power.

关键词： Support vector machines Neurological diseases Visualization Knowledge based systems Receivers Ontologies Robustness

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