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Weighted Node Mapping and Localisation on a Pixel Processor Array

Weighted Node Mapping and Localisation on a Pixel Processor ...

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IEEE international Conference on robotics and automation (ICRA)

作者： Castillo-Elizalde, Hector Liu, Yanan Bose, Laurie Mayol-Cuevas, Walterio Univ Bristol Bristol Robot Lab Bristol BS34 8QZ Avon England Univ Bristol Visual Informat Lab Bristol BS1 5DD Avon England

ISBN: (纸本)9781728190778

This paper implements and demonstrates visual route mapping and localisation upon a Pixel Processor Array (PPA). The PPA sensor comprises of an array of Processing Elements (PEs), each of which can capture and process visual information directly. This provides significant parallel processing power allowing novel ways in which information can be processed on-sensor. Our method predicts the correct node within a topological map generated from an image sequence by measuring image similarities, spatial coherence, and exploiting the parallel nature of the PPA. Our implementation runs at +300Hz on large public datasets with +2K locations requiring 2.5W at 500 GOPS/W. We compare vs traditionally implemented methods demonstrating better F-1 performance even on simulation. As far as we are aware, we present the first on-sensor mapping and localisation system running entirely on-sensor.

关键词： Visualization Spatial coherence Prototypes Parallel processing Robot sensing systems Hardware computational efficiency

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Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

Limits of Probabilistic Safety Guarantees when Considering H...

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IEEE international Conference on robotics and automation (ICRA)

作者： Cheng, Richard Murray, Richard M. Burdick, Joel W. CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9781728190778

When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over human agents' trajectories (i.e. confidence tubes that contain trajectories with probability delta), which can then be used to guarantee safety with probability 1 - delta. However, almost all existing works consider delta >= 0.001. The purpose of this paper is to argue that (1) in safety-critical applications, it is necessary to provide safety guarantees with delta < 10(-8) , and (2) current learning-based methods are ill-equipped to compute accurate confidence bounds at such low delta. Using human driving data (from the highD dataset), as well as synthetically generated data, we show that current uncertainty models use inaccurate distributional assumptions to describe human behavior and/or require infeasible amounts of data to accurately learn confidence bounds for delta < 10(-8). These two issues result in unreliable confidence bounds, which can have dangerous implications if deployed on safety-critical systems.

关键词： Learning systems Uncertainty Conferences computational modeling Probabilistic logic Data models Safety

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Real-time Optimal Navigation Planning Using Learned Motion Costs

Real-time Optimal Navigation Planning Using Learned Motion C...

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IEEE international Conference on robotics and automation (ICRA)

作者： Yang, Bowen Wellhausen, Lorenz Miki, Takahiro Liu, Ming Hutter, Marco Hong Kong Univ Sci & Technol Robot Inst Robot & Multipercept Lab Hong Kong Peoples R China Swiss Fed Inst Technol Robot Syst Lab Zurich Switzerland

ISBN: (纸本)9781728190778

Navigation on challenging terrain topographies requires the understanding of robots' locomotion capabilities to produce optimal solutions. We present an integrated framework for real-time autonomous navigation of mobile robots based on elevation maps. The framework performs rapid global path planning and optimization that is aware of the locomotion capabilities of the robot. A GPU-aided, sampling-based path planner combined with a gradient-based path optimizer provides optimal paths by using a neural network-based locomotion cost predictor which is trained in simulation. We show that our approach is capable of planning and optimizing paths three orders of magnitude faster than RRT* on GPU-enabled hardware, enabling real-time deployment on mobile platforms. We successfully evaluate the framework on the ANYmal C quadrupedal robot in both simulations and real-world environments for path planning tasks on multiple complex terrains.

关键词： Costs Navigation computational modeling Training data Surfaces Sampling methods Real-time systems

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Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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international symposium on computational intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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2nd international Conference on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT, ICSD 2024

2nd International Conference on Sustainable Development Usin...

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2nd international Conference on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT, ICSD 2024

ISBN: (纸本)9783031717284

The proceedings contain 38 papers. The special focus in this conference is on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT. The topics include: Advancing Sustainable Security: AI-Driven Embedded Hardware for Mobile robotics;Evaluating Cyber-Crime Using Machine Learning and AI Approach for Environmental Sustainability;Fostering Social Relationships in Higher Education Institutions Through AI-Powered Solutions for Sustainable Development;impact of Artificial intelligence and Machine Learning in the Sustainable Transformation of the Pharma Industry;Integrating Robot-Based Resources to Foster computational Thinking and Communication Skills in EFL Education: A Path Towards Sustainable Development;Sustainable Development in Cancer Diagnosis: Empowering Precision Medicine with Artificial intelligence and CRC Detection Tools;sustainable Development Through Digital Twin Technology: Optimizing the Supply Chain;fostering Sustainable Growth via the Fusion of Artificial intelligence and Computer Vision;Sustainable Digital and Enterprise Transformation for Omni-Channel Experiences Empowered by Data and AI/ML;sustainable Finance Beyond Banking Shaping the Future of Financial Technology;Sustainable Safeguarding: Implementing Fraud Defense Systems for Authorized Push Payments Through robotics Process automation and AI Integration;Advancing Sustainable Development: Harnessing AI for Efficient and Eco-Friendly Customer Support in IoT Solutions;enabling Sustainable Interaction - A Novel Approach to Hand Gesture-Based Action Recognition for Intelligent Systems;exploring Network Privacy Measures in Mobile Networks;exploring Sustainable Financial Network Analytics: Opportunities and Challenges in Systemic Risk Management and Investment Analysis;fostering Sustainable Circular Economy Practices in India: A Bibliometric Review of IoT Based Technologies;Leveraging Block-DAO Integration for Sustainable Smart Manufacturing: Operational Efficiency, Supply Chai

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Using Weka API for creating a custom classification application

Using Weka API for creating a custom classification applicat...

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international symposium on Applied computational intelligence and Informatics ( SACI)

作者： Raul Robu Paul Arseni-Ailoi Dan Ungureanu-Anghel Department of Automation and Applied Informatics Faculty of Automation and Computers Politehnica University of Timisoara Timisoara Romania

In real world applications, classification models can be built by repeatedly going through the steps of data preprocessing, building classification models with different dedicated algorithms, testing the resulted models, until building a good enough classification model is achieved. The model built in this way can be used in order to perform predictions on new data, with a degree of trust resulted by testing the model. All the operations described above can be done with Weka, which is a very powerful machine learning tool. The authors consider that after going through the previous steps, for many real world applications, it would be beneficial to go through a supplementary step, of developing a custom application for data classification, specific to the realized study. The proposed application will use the Weka API, which will allow it to rebuild the selected classification model, save and load it in/from a binary file and then make predictions with the help of a user interface perfectly adapted to the data set used. The proposed application makes it easier to make predictions for a final beneficiary. The paper presents how such a custom classification application can be developed, using the Weka API and Java programming language. In order to validate the proposed solution, the authors have built a custom application for the classification of data of patients who may have heart diseases, starting from the Cleveland heart disease data set, obtained from the UCI Machine Learning Repository. The proposed application allows saving and loading the classification model through serialization and deserialization, so that building it in order to make predictions is necessary only once.

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A Novel Model Predictive Control Framework Using Dynamic Model Decomposition Applied to Dynamic Legged Locomotion

A Novel Model Predictive Control Framework Using Dynamic Mod...

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IEEE international Conference on robotics and automation (ICRA)

作者： Shen, Junjie Hong, Dennis Univ Calif Los Angeles Robot & Mech Lab Dept Mech & Aerosp Engn Los Angeles CA 90095 USA

ISBN: (纸本)9781728190778

Dynamic locomotion for legged robots is difficult because the system dynamics are highly nonlinear and complex, nominally underactuated and unstable, multi-input and multi-output, as well as time-variant and hybrid. One usually faces the choice between the intricate full-body dynamics which remains computationally expensive and sometimes even intractable, and the empirically simplified model which inevitably limits the locomotion capability. In this paper, we explore the legged robot dynamics from a different perspective. By decomposing the robot into the body and the legs, with interaction forces and moments connecting them, we enjoy a novel method called Dynamic Model Decomposition that involves lower-dimensional dynamics for each subsystem while their composition maintaining the equivalence to the original full-order robot model. Based on that, we further propose a corresponding model predictive control framework via quadratic programming, which considers linearly approximated body dynamics with constrained leg reaction forces as inputs. The overall methodology was successfully applied to a planar five-link biped robot. The simulation results show that the robot is capable of body reference tracking, push recovery, velocity tracking, and even blind locomotion on fairly rough terrain. This suggests a promising dynamic motion control scheme in the future.

关键词： Legged locomotion Tracking System dynamics computational modeling System performance Dynamics Predictive models

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Inverse Kinematics Analysis of Cassie Robot using Radial Basis Function Networks

Inverse Kinematics Analysis of Cassie Robot using Radial Bas...

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2021 international symposium of Asian Control Association on Intelligent robotics and Industrial automation, IRIA 2021

作者： Mitra, Mukund Raj, Suman Kolathaya, Shishir Indian Institute of Science Centre for Product Design and Manufacturing Bangalore India Indian Institute of Science Department of Computational and Data Sciences Bangalore India Indian Institute of Science Department of Computer Science and Automation Bangalore India

ISBN: (纸本)9781665433235

Inverse Kinematics of bipedal humanoid robots remains a challenging problem in the domain of robotics and computation, due to high order non-linearity and computation involved in Inverse Kinematics solutions. Also, there are many constraints involved with the various joint parameters which makes their analysis even more complex. Through this paper, we attempt to solve the Inverse Kinematics problem of a bipedal humanoid robot, Cassie, using Radial Basis Function (RBF) Networks. Our method can also be applied to other higher degrees of freedom serial manipulators. Our simulation analyses the results based on size of datasets, data distribution and network parameters. We have considered datasets of size ∼300k and ∼1 million, single and multiple hidden layers, equal and random data distribution, different number of neurons in layers and different training functions. We achieve our target of limiting the Mean Squared Error (MSE) calculated using the trained model below 0.1° for each joint angle, which is under acceptable limits for practical implementation. The configurations obtained from the RBF network are simulated and compared with the original input configuration. This is compared visually in MATLAB and the resulting pose of end-effector are similar for both the cases, complementing the performance that we get for the networks. © 2021 IEEE.

关键词： Radial basis function networks

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Review Analysis Real Time Implementation of Hand Gesture Recognition Model 1

Review Analysis Real Time Implementation of Hand Gesture Rec...

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1st international Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Kandasamy, Manivel Shanmugam, Raju Grishma, B. Shreemad, K. Kushal, R. Vishwa, C. Unitedworld School of Computational Intelligence Karnavati University Gujarat Gandhinagar India

ISBN: (纸本)9798350341737

Hand gesture recognition is a pivotal area of research with numerous applications in human-computer interaction, augmented reality, and robotics. This research paper presents a real-time implementation of a hand gesture recognition model designed to accurately and efficiently interpret hand gestures in real-world scenarios. Due to its adaptability and user-friendliness, hand gesture recognition is one of the most recent and happening study area in terms of human-computer interface. The development of a system that can be used to communicate with disabled individuals or to operate a device using the gesture recognition approach. High intergroup similarities in hand gesture poses, light variance, non-uniform backgrounds, and user-specific hand size and shape variations present significant hurdles for the development of an effective hand gesture identification algorithm. The given research paper represents a basic technique that could be used to develop a hand gesture recognition technique along with cursor movements. On three benchmark static hand gesture datasets with uniform and irregular backdrops for the CV2 tests, a thorough analysis is conducted. The labeling for the hand motions was created via the media pipe library. It enables the user to have their hand recognized as they move it in the video input window that pops up and the hand detection works simultaneously. Thus, automation with mouse and keyboard control is accomplished using the PV auto GUI module. Alternately, we could preferably say that python language would be used in order to automate keyboard and mouse, as it is diverse and dynamic language. A handful of the numerous features it offers are listed below. In the windows of the other apps, we may move the mouse and click. We can communicate keystrokes with another program. As an illustration, filling out a form, entering a search term into a browser, etc. Another option is to take pictures and provide an image. It enables us to find an application

关键词： Augmented reality

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Meta-AGI: A Hierarchical Meta-Learning Framework for Generalizable intelligence

Meta-AGI: A Hierarchical Meta-Learning Framework for General...

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Computer Applications and Information Technology (ISCAIT), international symposium on

作者： Bhaskar Jyoti Dutta Bircham International University Madrid Spain

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Meta learning carries the hope of adaptation by Artificial intelligence (AI) agents for novel tasks. However current meta-learning is not able to meet the need of generalising across diverse task distributions as well as the need for meeting sufficient learning dynamics. Here in this paper we propose Meta-Artificial General intelligence (Meta-AGI). Meta-AGI is a hierarchical meta-learning framework developed to meet the requirements in generalization and efficiency which is achieved by the combination of three key components including Hierarchical Task Decomposition (HTD), Adaptive Meta-Optimization (AMO) and Structured Experience Replay (SER). In this paper we have provided theoretical analysis establishing generalization bounds and convergence rates as well as demonstrating superior performance across globally accepted multiple benchmarks consisting of Meta-World, Omniglot, Mini-ImageNet and MuJoCo. MetaAGI demonstrated a 15% improvement in few-shot accuracy on Meta-World by reducing the error rate from 31.8% to20.5% and a $\mathbf{2 0 \%}$ reduction in sample complexity which allowed faster learning with fewer data points and thus lowering computational overhead by approximately $10 \%$. These results established MetaAGI as the foundational framework for generalizable artificial intelligence. Meta-AGI demonstrated exceptional results in real-world applications showcasing its capacity beyond theoretical advances. In industrial robotics our Meta-AGI achieved a $\mathbf{4 0 \%}$ reduction in robot programming time and $65 \%$ improvement in task adaptation. In healthcare implementation our Meta-AGI showed92% diagnostic accuracy across diverse medical imaging tasks and in educational applications it demonstrated $\mathbf{4 5 \%}$ faster student learning rates. These results establish our Meta-AGI as the foundational framework for applied generalizable artificial intelligence.

关键词： Metalearning Accuracy Service robots Memory management Medical services Transforms Reinforcement learning Production Complexity theory Robot programming

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