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Trajectory Planning with Obstacle Avoidance using APF and COLREGS for USVs

Trajectory Planning with Obstacle Avoidance using APF and CO...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Kelvin K. C. F. Nunes Tiago T. Ribeiro Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

This paper presents a trajectory planning and obstacle avoidance system for Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. The developed system employs a modified Artificial Potential Field (APF) algorithm and the International Regulations for Preventing Collisions at Sea (COLREGS) to ensure safe and efficient navigation. Modifications were implemented to the original algorithm to deal with obstacles approaching the autonomous vehicle, including adding vectors to determine the direction of deviation based on the cross-product. The proposed system was validated in the Gazebo simulation environment within a dynamic scenario featuring static and moving obstacles.

关键词： Trajectory planning Navigation Heuristic algorithms Vectors Regulation Safety Vehicle dynamics

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Leveraging IoT and TinyML for Smart Battery Management in Electric Bicycles

Leveraging IoT and TinyML for Smart Battery Management in El...

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International Workshop on Secure Internet of Things (SIoT)

作者： Thommas Flores Matheus Andrade Morsinaldo Medeiros Miguel Amaral Marianne Silva Ivanovitch Silva Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil

Ahstract-Urban mobility and environmental sustainability are critical challenges intensified by population growth, urbanization, and increased vehicle presence, leading to issues like congestion, pollution, and climate change. To counter this, the urgency for efficient and eco-friendly transportation solutions is evident. Battery Management Systems (BMS) play a central role, overseeing batteries in electronics, vehicles, and energy storage systems for safety and reliability. Focusing on electric bicycles (e-Bikes), the article highlights BMS's role in enhancing cyclist experiences and delves into techniques utilizing energy-efficient hardware and deep learning, notably TinyML, to predict Lithium-Ion battery State of Health (SoH). It examines diverse architectures and parameters, unveiling relationships to guide machine learning model construction, emphasizing delayed feedback integration based on rigorous statistics. The article navigates through data segregation and model training processes, presenting a comprehensive approach that balances sustainability, efficiency, and technological progress in predicting e-Bike battery SoH.

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Devising adaptive migration policies for cooperative distributed genetic algorithms

Devising adaptive migration policies for cooperative distrib...

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IEEE International Conference on Systems, Man and Cybernetics

作者： E. Noda A.L.V. Coelho I.L.M. Ricarte A. Yamakami A.A. Freitas School of Electrical and Computer Engineering (FEEC) UNICAMP-State University of Campinas Brazil Postgraduate Program in Computer science Pontifical Catholic University of Paraná Brazil

Distributed genetic algorithms (DGAs) constitute an interesting approach to undertake the premature convergence problem in evolutionary optimization. This is done by spatial partitioning a huge panmitic population into several semi-isolated groups, called demes, each evolving in parallel by its own pace, and possibly exploring different regions of the search space. At the center of such approach lies the migratory process that simulates the swapping of individuals belonging to different demes, in such a way to ensure the sharing of good genetic material. In this paper, we model the migration step in DGAs as an explicit means to promote cooperation among genetic agents, autonomous entities encapsulating GA instances for possibly tackling different sub-problems of a complicated task. The focus is on the characterization of adaptive migration policies in which the choice of what individuals to migrate and/or replace is not defined a priori but according to a more knowledge-oriented rule. Comparative results obtained for a data-mining task were conducted, in order to assess the performance of adaptive migration according to efficiency/effectiveness criteria.

关键词： Genetic algorithms Identity-based encryption Inductors Search engines Dissolved gas analysis Parallel architectures Cloning

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Minimum-Time Trajectory Planning for Helicopter UAVs using Computational Dynamic Optimization

Minimum-Time Trajectory Planning for Helicopter UAVs using C...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Nathan Xu Wei Kang Guowei Cai Ben M. Chen Summer Intern Program Department of Applied Mathematics Naval Postgraduate School Department of Applied Mathematics Naval Postgraduate School Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781467317139

In this paper, we apply pseudospectral optimal control, a computational algorithm of dynamic optimization, to the problem of helicopter UAV for minimum-time trajectory planning in the presence of obstacles. The problem is formulated as a nonlinear optimal control subject to the dynamics and limitations of helicopter UAVs, in which the obstacles are formulated as inequality constraints by using p-norms. The dynamical system is defined by a set of fifteen states nonlinear differential equations developed for HeLion, a helicopter UAV constructed in National University of Singapore (NUS). The problem does not have an analytic solution. We numerically solve the problem using a pseudospectral method. Various terrain scenarios were tested, from a single obstacle to multiple obstacles. We found bifurcation points of minimum-time trajectories near obstacles. The bifurcation points and their relationship with the distance to the obstacle are analyzed.

关键词： Minimum-time control Helicopter UAV Computational optimal control Obstacle avoidance Unmanned Aerial Vehicle Helicopters trajectory planning barriers BIFURCATION nonlinear differential equations inequality constraint drone honey bees

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Classification and tracking of items on a moving conveyor belt using convolutional networks and image processing

Classification and tracking of items on a moving conveyor be...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Angelo Leite Medeiros De Góes Adrião Duarte Dória Neto Matheus Gomes Diniz Andrade Thiago Theiry De Oliveira Electrical and Computer Engineering Postgraduate Program Federal University of Rio Grande do Norte Natal Brazil Computer Engineering Federal University of Rio Grande do Norte Natal Brazil

The prolonged waiting time at supermarket checkout lines poses a significant challenge to the shopping experience, impacting customer satisfaction and operational efficiency. This paper presents a prototype that addresses this issue by utilizing computer vision and deep learning. The model, incorporating convolutional neural networks such as YOLO v4 tiny and YOLO v5 small, along with tools like OpenCV and Roboflow for dataset management, achieves a remarkable 98% mean average precision for two-class detection. It efficiently detects, classifies, tracks, and counts items on a mobile supermarket conveyor belt. Additionally, we introduce a versatile framework designed for seamless integration into real-world applications. It comprises a customizable monitoring application and simulator that facilitates synthetic image data generation. Managing diverse items in a supermarket presents a major challenge for data gathering, labeling, and training. In that sense, the importance of customizable monitoring and simulation tools is highlighted, emphasizing their practical role. Our findings demonstrate the feasibility of maintaining a minimal 0% to 2.85% precision tradeoff while using half of the data as synthetic for two-class detection, indicating potential practicality in supermarkets with proper scaling. In summary, this study brings tangible benefits to both customers and retailers, offering a potential to streamline, speed up, and cut costs in the supermarket checkout process.

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A data-stream TinyML compression algorithm for vehicular applications: a case study

A data-stream TinyML compression algorithm for vehicular app...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Marianne Silva Gabriel Signoretti Thommas Flores Pedro Andrade Jordã o Silva Ivanovitch Silva Emiliano Sisinni Paolo Ferrari Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil University of Brescia Brescia Italy

The Internet of Things (IoT) has reduced the distance between one point and another and between people by connecting multiple devices to the web. However, the volume and speed of data creation and transmission have also increased. In this scenario, some challenges start to emerge, such as potentially irrelevant or redundant data transmission, that is, generating a more significant expenditure of energy and processing, in addition to the unnecessary use of the communication channel. Thus, to mitigate these, a solution for IoT devices would be data compression techniques. However, such devices available in the market today have severe limitations in terms of storage and processing power. Therefore, to overcome these limitations, the TinyML can be used to seek ways to implement machine learning models in low-power devices. In this context, this article aims to evaluate the impact of the compression algorithm (Tiny Anomaly Compress - TAC) on the performance of a microcontroller applied to the context of vehicles in a real scenario. As a result, it was found that even with the embedded algorithm, the microcontroller processing time is not affected in a meaningful way.

关键词： Performance evaluation Microcontrollers Data compression Communication channels Machine learning Real-time systems Internet of Things

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Can adaptive strategies sustain bidirectional LoRaWAN traffic?

Can adaptive strategies sustain bidirectional LoRaWAN traffi...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Emiliano Sisinni Alessandro Depari Paolo Bellagente Alessandra Flammini Ivanovitvh Silva Thommas Flores Paolo Ferrari Dept. of Information Engineering University of Brescia Brescia ITALY Dept. of Computer Engineering and Automation UFRN Natal BRAZIL Postgraduate Program in Electrical and Computer Enigeneering UFRN Natal BRAZIL

Low Power Wide Area Networks (LPWANs) are a viable solution for addressing the requirements of Internet of Things (IoT) applications that can tolerate sporadic updates, possibly only in the uplink (from the field towards the cloud and the user) direction. LoRaWAN is currently the most diffused example of LPWAN, exploiting LoRa radios. LoRa, an enhanced version of chirp spread spectrum modulation, trades off the radio sensitivity for the data rate, permitting extensive area coverage. However, the price is a relatively long frame duration, leading to high collision probability in dense, asynchronous scenarios; fortunately, polarity inversion minimizes collisions between uplink and downlink traffic. Therefore, adaptive strategies (based on some feedback from the backend) have also been suggested to adapt the data rate depending on the environment condition dynamically. This work compares the ADR (Adaptive Data Rate) and the ADP (Adaptive Data Payload) strategies when bidirectional traffic exists, using a purposely designed simulator. Results considering a different number of gateways show that ADP tries to maximize the amount of data effectively received in both direction but suffer from the adoption of longer frame duration, leading to higher collision probability. In contrast, ADR tries to force the adoption of the fastest data rate, limiting the overall amount of transferred data.

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A Framework to Support Collection, Processing and Analysis of Smart Vehicles Data

A Framework to Support Collection, Processing and Analysis o...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Mariana Azevedo Thais Medeiros Morsinaldo Medeiros Carlos M. D. Viegas Marianne Silva Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Thanks to the Internet of Things (IoT) revolution and advancements in embedded systems, the incorporation of an extensive array of sensors and computational resources into vehicles has become a reality, significantly enhancing their capabilities to produce relevant data. This has opened up new opportunities for acquiring and processing vehicular data in various areas, supporting new applications such as vehicular monitoring to the use of information for artificial intelligence for improved decision-making tasks. However, although the benefits are promising, there is a current challenge when providing flexible and standardized procedures to capture, process, and store this information for further analysis. In this context, this article proposes an framework to support the the handling of vehicular data, including data processing by a server and storage in a cloud database. This framework was validated through a real-world case study, analyzing the collected sensor data. The results indicated the feasibility of the proposal, contributing to the availability of vehicular data analysis in a way that is distinct from proposals in the literature. Additionally, this framework offers another significant contribution, related to the use of an appropriate database for time series, enabling scalability and high availability.

关键词： Data analysis Torque Databases Time series analysis Proposals Internet of Things Servers

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An Adaptive TinyML Unsupervised Online Learning Algorithm for Driver Behavior Analysis

An Adaptive TinyML Unsupervised Online Learning Algorithm fo...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Marianne Silva Thais Medeiros Mariana Azevedo Morsinaldo Medeiros Mikael Themoteo Tatiane Gois Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal

The Internet of Things (IoT) has significantly impacted various industries, particularly the automotive sector, due to the growing integration of IoT technologies in vehicles. As a result, the volume of data generated by vehicular sensors has increased, leading to a surge in studies focusing on driver behavior to enhance road safety and optimize transportation networks. However, traditional approaches to analyzing driver behavior have relied on supervised offline learning models, which are unsuitable for handling data streams in online learning environments. This study introduces an unsupervised online k-fix AutoCloud algorithm for detecting driver behavior patterns, leveraging the concepts of typicity and eccentricity while considering the historical-temporal relationships between samples. Furthermore, the algorithm autonomously and adaptively evolves without requiring a supervised training phase, making it compatible with the TinyML concept, encompassing Artificial Intelligence algorithms designed for low-power IoT devices. To validate the proposed method, a real case study was conducted over four days using a vehicle to compare the quantity and quality of clusters generated by the algorithm. The findings demonstrate the potential of the proposed approach for optimizing data processing with minimal computational power.

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Minimum time control of helicopter UAVs using computational dynamic optimization

Minimum time control of helicopter UAVs using computational ...

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International Conference on Control and Automation (ICCA)

作者： K. Tang B. Wang W. Kang B. Chen Internship program Naval Postgraduate School Monterrey CA USA Department of Electrical and Computer Engineering University of Singapore Singapore Department of Applied Mathematics Naval Postgraduate School Monterrey CA USA

This paper focuses on the problem of minimum time trajectory planning for helicopter UAVs. It is formulated as a nonlinear optimal control subject to the dynamics and limitations of helicopter UAVs. The dynamical system is defined by a set of fifteen states nonlinear differential equations developed for HeLion, a UAV helicopter constructed in National University of Singapore (NUS). The problem is then solved numerically using pseudospectral method for dynamic optimization. The results show that minimum time trajectories are highly nonlinear that require complicated maneuvering.

关键词： Trajectory Helicopters Mathematical model Approximation methods Planning Aerodynamics Vectors

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