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Multi-objective reinforcement learning framework for beneficent artificial intelligence

Neural Computing and Applications 2025年 1-19页

作者： Nickelson, Anna Perkins, Russell London, Alex John Robinette, Paul Tumer, Kagan Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University Corvallis USA Electrical and Computer Engineering University of Massachusetts Lowell Lowell USA Department of Philosophy Carnegie Mellon University Pittsburgh USA

As artificial intelligence (AI) systems are more widely deployed and utilized, they have greater potential to inflict harm or provide benefits to individuals. Designers must consider these concepts when building AI systems, but it is difficult to capture these notions in a mathematical framework. An ethical framework put forth by London and Heidari (Minds Mach, 2024. https://***/10.1007/s11023-024-09696-8 ) provides structure and formal definitions of benefits and harms in relation to an individual’s life plans. This leaves an open question of how these concepts impact the decision-making of an AI system. We leverage their work to provide a direct translation of these theoretical ethical concepts to a standard multi-objective reinforcement learning (MORL) decision model. Using this model, we show that multi-objective rewards are necessary to capture benefits and harms in accordance with their definitions. We demonstrate how to capture these concepts in a MORL system and provide scenarios to highlight the utility of this work.

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Framework for Ship Trajectory Forecasting Based on Linear Stationary Models Using Automatic Identification System

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Procedia computer Science 2023年 218卷 1463-1474页

作者： Smita Srivastava Lovneet Kumar R. Jeyanthi K. Deepa Vinay Aggrawal Department of Computer Science and Engineering Centre for Artificial Intelligence and Robotics Defense Research and Development Organization Bangalore Karnataka Department of Electronics and Communication Engineering Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

Maritime surveillance is of utmost priority for a nation's security, and hence it's economy. For maritime awareness, coastal surveillance, and maritime activities in the Region of Interest (ROI) should be monitored. One of the ways to keep this in check is to restrain unwanted infiltration. Monitoring unwanted infiltration is feasible through vessel trajectory forecasting and anomaly detection in real time. Most solutions for trajectory predictions are available but require a huge amount of historical data, and high-power computing resources. Here, the requirement is developing a decision support framework consisting of both lite weight approaches for short-term predictions and deep learning-based techniques for long-term forecasting. This paper aims to find the suitability of Linear Stationary Models (LSM) like the Auto-Regressive Integrated Moving Average Model (ARIMA) for predicting and forecasting the Vessel Trajectory as means of lite weight short-term predictions. For this purpose, the Automatic Identification System (AIS) dataset of the U.S. West Coast is used. The significant effort was for data pre-processing to create a robust dataset for model training. An appropriate model after the model-selection process is used for trajectory forecasting. The model's accuracy is validated using Root Mean Square Error (RMSE) performance indices for residual and forecast errors. A window generator model is integrated with the best-fitted ARIMA model for recursive real-time predictions, with varied sizes and visualization. The proposed time-series model provided a very high accuracy as the RMSE value for prediction and 48 hours forecast are 0.023 and 0.017, respectively.

关键词： Maritime Surveillance Trajectory Forecast Time Series Analysis Auto-Regressive Integrated Moving Average Model Automatic Identification System Window Generator

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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IEEE Southeastcon

作者： Bhaavin Kishore Jogeshwar Joshua Bohannon Hakki Erhan Sevil Department of Intelligent Systems and Robotics University of West Florida (UWF) Pensacola FL USA Department of Electrical and Computer Engineering University of West Florida (UWF) Pensacola FL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies.

关键词： Navigation Toy manufacturing industry Software algorithms Prototypes Object detection Grasping Land vehicles Real-time systems Planning Vehicle dynamics

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computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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International Conference on electrical engineering and Informatics, ICEEI

作者： Samuel Raymond Pranowo Zener Lie Sukra Muhammad Zacky Asy'ari Marisa Paryasto Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Electrical Engineering Department Computer Engineering Program Telkom University Bandung Indonesia

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%.

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ASFM: Augmented Social Force Model for Legged Robot Social Navigation 23

ASFM: Augmented Social Force Model for Legged Robot Social N...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Ægidius, Sebastian Chacón-Quesada, Rodrigo Delfaki, Andromachi Maria Kanoulas, Dimitrios Demiris, Yiannis Imperial College London Personal Robotics Lab Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University College London Robot Perception and Learning Lab Department of Computer Science Gower Street LondonWC1E 6BT United Kingdom Ai Centre Department of Computer Science Ucl United Kingdom Archimedes/Athena Rc Greece

ISBN: (纸本)9798350373578

Social navigation in robotics primarily involves guiding mobile robots through human-populated areas, with pedestrian comfort balanced with efficient path-finding. Although progress has been seen in this field, a solution for the seamless integration of robots into pedestrian settings remains elusive. In this paper, a social force model for legged robots is developed, utilizing visual perception for human localization. In particular, an augmented social force model is introduced, incorporating refined interpretations of repulsive forces and avoidance behaviors based on pedestrian actions, alongside a target following mechanism. Experimental evaluation on a quadruped robot, through various scenarios, including interactions with oncoming pedestrians, crowds, and obstructed paths, demonstrates that the proposed augmented model significantly improves upon previous baseline methods in terms of chosen path length, average velocity, and time-to-goal for effective and efficient social navigation. The code isopen-source, while video demonstrations can be found on the project's webpage: https://***/ASFM/ © 2024 IEEE.

关键词： Mobile robots

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ODD: Omni Differential Drive for Simultaneous Reconfiguration and Omnidirectional Mobility of Wheeled Robots

arXiv

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arXiv 2024年

作者： Zhao, Ziqi Xie, Peijia Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

Wheeled robots are highly efficient in human living environments. However, conventional wheeled designs, with their limited degrees of freedom and constraints in robot configuration, struggle to simultaneously achieve stability, passability, and agility due to varying footprint needs. This paper proposes a novel robot drive model inspired by human movements, termed as the Omni Differential Drive (ODD). The ODD model innovatively utilizes a lateral differential drive to adjust wheel spacing without adding additional actuators to the existing omnidirectional drive. This approach enables wheeled robots to achieve both simultaneous reconfiguration and omnidirectional mobility. To validate the feasibility of the ODD model, a functional prototype was developed, followed by comprehensive kinematic analyses. Control systems for self-balancing and motion control were designed and implemented. Experimental validations confirmed the feasibility of the ODD mechanism and the effectiveness of the control strategies. The results underline the potential of this innovative drive system to enhance the mobility and adaptability of robotic platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Kinematics

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Robust rateless space time block coding for mmWave massive MIMO system

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Heliyon 2024年第21期10卷 e40114页

作者： Kelem, Zelalem A. Hussein, Habib M. Artificial Intelligence and Robotics Center of Excellence Addis Ababa Science and Technology University Department of Electrical and Computer Engineering Ethiopia

In the vast growing wireless communication system creating robust encoding mechanisms using space-time block codes (STBC) for mmWave massive MIMO to overcome uncertainty and ensure reliability is a critical concept to be covered. This article reviews the core concepts behind MIMO communication, Massive MIMO communication, space-time block codes, and rateless codes in the context of wireless communication systems. Building on the foundational concepts of information theory and mmWave massive MIMO, we developed space-time block codes that maintain orthogonality for real-valued symbols. Following a thorough analysis of these codes, we successfully extended them into rateless orthogonal space-time block codes (ROSTBC) for massive MIMO. This extension enables the codes to adapt their rates dynamically based on the unknown channel conditions at the transmitter. The research work was concluded by comparing static G4 encoded Tarokah work and other Orthogonal Space Time Block Codes (OSTBCs) with Rateless Space Time Block Codes (ROSTBCs). The results show that as the number of blocks used to encode the message in Rateless Space Time Block Code (ROSTBC) increases, this coding scheme can outperform static Rateless Space Time Block Codes (OSTBC) in very low SNR values by a minimum of 8.5 %. This work can enhance wireless communication by creating reliable communication over inherently unreliable systems. © 2024 The Authors

关键词： Massive MIMO Orthogonal space time block codes (OSTBC) Rateless orthogonal space time block codes (ROSTBC) Rateless space-time block coding (RSTBC) Wireless communication

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ODD: Omni Differential Drive for Simultaneous Reconfiguration and Omnidirectional Mobility of Wheeled Robots

arXiv

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arXiv 2024年

作者： Zhao, Ziqi Xie, Peijia Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Wheeled robots are highly efficient in human living environments. However, conventional wheeled designs, limited by degrees of freedom, struggle to meet varying footprint needs and achieve omnidirectional mobility. This paper proposes a novel robot drive model inspired by human movements, termed as the Omni Differential Drive (ODD). The ODD model innovatively utilizes a lateral differential drive to adjust wheel spacing without adding additional actuators to the existing omnidirectional drive. This approach enables wheeled robots to achieve both simultaneous reconfiguration and omnidirectional mobility. Additionally, a prototype was developed to validate the ODD, followed by kinematic analysis. Control systems for self-balancing and motion were designed and implemented. Experimental validations confirmed the feasibility of the ODD mechanism and the effectiveness of the control strategies. The results underline the potential of this innovative drive system to enhance the mobility and adaptability of robotic platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Wheels

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An 8-bit Quantized Globalization Model for Cat Skin Disease Detection Using Convolutional Neural Networks and TensorFlow Lite

An 8-bit Quantized Globalization Model for Cat Skin Disease ...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Karisma Trinanda Putra Hasan Zidni Rifdha Turrizka Hsueh-Ting Chu Dinh-Trung Vu Prayitno Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Electrical Engineering Institut Teknologi Bandung Bandung Indonesia Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Electrical Engineering Semarang State Polytechnic Semarang Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Skin diseases are health issues that can be experienced by pets and potentially be transmitted from wild animals. However, current prediction systems predominantly rely on complex calculations that necessitate high computational power, making them challenging to deploy on mobile applications. Therefore, this study aims to optimize a skin disease detection system based on convolutional neural networks (CNN) using datasets from both wild and domestic cats. The quantized globalization model is a modified ResNet50 converted with TensorFlow Lite. This 8-bit quantization is necessary to develop a lightweight deep learning model that could operate on low-computational power units, such as smartphone or 8-bit edge computing devices. This study utilized a dataset comprising 343 images, categorized into six classes: five classes of diseases (abscesses, mange, ringworm, skin cancer, dermatitis) and one class for healthy skin. Next, the dataset was augmented to increase data variation and quantity. Subsequently, the dataset was trained on the ResNet-50 model and then converted to TensorFlow Lite with 8-bit integer data type. Finally, the quantized globalization model was validated using a separate 20% of the dataset. The validation results yielded an accuracy score of 0.930, a precision of 0.908, a recall of 0.902, and an F1-score of 0.903. Additionally, there was a significant 74.7% reduction in model size compared to the non-quantized version, demonstrating the efficiency and effectiveness of the quantization process in maintaining performance while substantially decreasing the computational resource requirements.

关键词： Analytical models Accuracy Computational modeling Globalization Predictive models Skin Data models Convolutional neural networks Optimization Diseases

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A Survey of Stroke Image Analysis Techniques 9th

A Survey of Stroke Image Analysis Techniques

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9th EAI International Conference on Advancement of Science and Technology, ICAST 2021

作者： Agizew, Henok Yared Beyene, Asrat Mulatu AI & Robotics CoE Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783030937089

Stroke is one of the instantaneously shocking and spiking cerebrovascular diseases having substantial residual effects. Image analysis techniques have the ability to diagnosing and providing proper treatment for stroke patients. To lighten the problem, various techniques of image analysis have been proposed. Thus, this survey intended to analyze these proposed image analysis approaches intending to thoroughly examine the state-of-the-art image analysis techniques. To prepare this survey, the systematic literature review method was employed. Based on the reviewed literature, several clinical and biological image datasets are found to be used in the process of stroke diagnosis. However, there are very few publicly accessible datasets are available currently. In this survey, each image analysis technique used for stroke image analysis processes is briefly discussed. Finally, open research challenges are identified that could be addressed in the future. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Image analysis

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