Innovative Plug-and-Play System for Electrification of Wheel-Chairs

Pacini, Federico; Di Matteo, Stefano; Dini, Pierpaolo; Fanucci, Luca; Bucchi, Francesco

doi:10.1109/access.2023.3305923

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…Looking ahead, the future of deep learning in the IoT appears promising, especially in the realms of automotive, industrial, automation, and mechatronics applications. In the automotive sector, the potential for deep learning to enhance autonomous driving systems, advanced driver-assistance systems (ADAS), and predictive maintenance stands as a critical area for development [138][139][140][141][142][143][144][145][146][147][148][149][150][151][152]. Similarly, in industrial and manufacturing settings, the integration of deep learning holds the promise of optimizing production processes, predicting equipment failures, and improving overall operational efficiency.…”

Section: Discussionmentioning

confidence: 99%

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi,

Dini,

Saponara

et al. 2023

Electronics

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The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks.

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Section: Discussionmentioning

confidence: 99%

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Elhanashi,

Dini,

Saponara

et al. 2023

Electronics

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“…Using advanced control algorithms, like PID control and model predictive control, we could ensure a quick and stable response of the model to user requests. This control not only handles acceleration, deceleration, and turning but also deals with trajectory maintenance and obstacle management to ensure safe and smooth driving [108][109][110][111][112][113]. All of this was seamlessly integrated into the ROS and Gazebo ecosystem, enabling efficient communication between the wheelchair model and other components of the robotic system.…”

Section: System Definitionmentioning

confidence: 99%

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Pacini,

Dini,

Fanucci

2024

Electronics

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Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness.

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“…The use of mathematical models enables more accurate and predictive design of devices. Through advanced simulation, potential issues can be anticipated and resolved, significantly reducing development time and costs [3,4]. Detailed simulation of dynamic behaviors provides a solid foundation for the development of highly efficient control and monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Dini,

Colicelli,

Saponara

2024

Batteries

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Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

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Innovative Plug-and-Play System for Electrification of Wheel-Chairs

Cited by 8 publications

References 24 publications

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

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