Path Planning Algorithm for an Autonomous Electric Wheelchair in Hospitals

Jung, Yeontaek; Kim, Yoonjae; Lee, Woo Hyung; Bang, Moon Suk; Kim, Youdan; Kim, Sung‐Wan

doi:10.1109/access.2020.3038452

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…In purely obstacle avoidance systems, the objective is for the system to navigate around obstacles while adhering to the user's input according to a specified policy [34][35][36][37][38][39][40]. In hybrid systems, the primary goal of obstacle avoidance is combined with supplementary tasks, such as target tracking, wall following, door crossing, and reaching points of interest [41][42][43][44].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…A very comprehensive analysis of the wheelchair review was completed by many researchers. Some voice-based control wheelchairs were developed by researchers with some intelligence [26].…”

Section: ) Wheelchair Robotmentioning

confidence: 99%

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Jayasekara,

Kulathunge,

Premaratne

et al. 2023

Journal of Robotics and Control

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This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

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“…Oleh karena itu algoritma perencanaan jalur sangat perlu dilakukan untuk menghasilkan jarak yang optimal. Perencanaan jalur pada robot otonom memiliki banyak kegunaan seperti Autonomus Vacum Cleaner Robots [1], Unmanned Aerial Vehicles (UAV) menjelajahi lingkungan yang belum diketahui [2], pada bidang medis perencanaan jalur digunakan untuk mendesain obat yang lebih baik [3], kursi roda elektronik otonom di rumah sakit [4]. Pada pengaplikasian perencanaan jalur, algoritma yang sering digunakan adalah Sampling Based Planning (SBP).…”

Section: Pendahuluan a Latar Belakangunclassified

Informed-RRT* Using Hybrid Sampling to Finding Fast Final Path Solution

Fauzi¹,

Pohan²

2021

Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan

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Informed Rapidly-exploring Random Tree* (Informed-RRT*) merupakan hasil pengembangan dari algoritma Rapidly-exploring Random Tree (RRT) yang dapat menghasilkan solusi jalur yang bersifat asimptotik optimal tetapi waktu komputasi yang dibutuhkan menjadi lebih lama. Pada awalnya algoritma Informed-RRT* masih menggunakan metode random sampling yang mana metode ini akan mengambil sampel acak pada ruang pencarian. Pengambilan sampel acak inilah yang akan membuat waktu komputasi menjadi tidak optimal. Penelitian ini bertujuan untuk merancang algoritma Informed-RRT* menggunakan metode hybrid sampling. Metode hybrid sampling merupakan integrasi dari beberapa metode pengambilan sampel. Pada pengujian ini, performansi metode random sampling akan dibandingkan dengan perfomansi metode hybrid sampling dalam hal waktu komputasi. Pengujian metode hybrid sampling pada algoritma Informed-RRT* ini berbasis simulasi dan dilakukan pada lingkungan narrow, clutter, trap. Hasil yang didapatkan dari pengujian ini adalah penggunaan metode hybrid sampling pada algoritma Informed-RRT* mampu menghasilkan performansi waktu rata- rata komputasi yang lebih cepat 26,4 detik bila dibandingkan dengan metode random sampling pada lingkungan clutter. Pada lingkungan narrow metode hybrid sampling menghasilkan waktu komputasi 24,52 detik lebih cepat bila dibandingkan dengan metode random sampling. Pada lingkungan trap metode hybrid sampling menghasilkan waktu komputasi 5,25 detik lebih cepat dibandingkan dengan metode random sampling. Dari data hasil pengujian, metode hybrid sampling ini dapat menjadi metode pangambilan sampel alternatif untuk digunakan pada algoritma Informed-RRT*

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Path Planning Algorithm for an Autonomous Electric Wheelchair in Hospitals

Cited by 15 publications

References 28 publications

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

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

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Informed-RRT* Using Hybrid Sampling to Finding Fast Final Path Solution

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