Development of an Autonomous Driving Smart Wheelchair for the Physically Weak

Ryu, Hye-Yeon; Kwon, Je-Seong; Lim, Jeong-Hak; Kim, A-Hyeon; Baek, Su‐Jin; Kim, Jong-Wook

doi:10.3390/app12010377

Cited by 13 publications

(7 citation statements)

References 16 publications

(17 reference statements)

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“…During the training process, depending on the small or large environment, the training time and the number of episodes will be different. Traditional Q-Learning [41] 60.0 198.4 s SARSA [42] 75.0 223.9 s Large (11 × 33) Traditional Q-Learning [41] 235.0 1.45 h SARSA [42] 275.0 57.23 min…”

Section: Reward Of Obstacle Romentioning

confidence: 99%

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

Ngo¹,

Hải²

2022

Applied Sciences

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A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built.

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Section: Reward Of Obstacle Romentioning

confidence: 99%

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

Ngo¹,

Hải²

2022

Applied Sciences

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“…Some of these technologies also require the users to wear some intrusive devices, making users more reliant on such wearables. On the other hand, for wheelchair users, although some advances have been made in autonomous wheelchair design [ 8 , 9 ], these technologies often rely on the use of computer vision and other technologies which are subject to some ethical and privacy regulations. These systems often need to learn the environmental map before they can be used with confidence.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

Falcon-Caro,

Peytchev,

Sanei

2024

Bioengineering

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Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.

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“…As a result, research is being conducted to assist wheelchair convenience and collision stability by applying it to wheelchairs due to the advancement of indoor autonomous driving technology used in AGVs (Automated Guided Vehicles) and AMRs (Autonomous Mobile Robots), which have recently become a technical topic following the advent of the fourth industry [6].…”

Section: Introductionmentioning

confidence: 99%

A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

Jang,

Eom,

Lee

2024

Applied Sciences

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As the world enters an aging and super-aged society, the application of advanced technology in assistive devices to support the daily life of the elderly is becoming a hot issue. Among them, electric wheelchairs are representative assistive devices for the walking support of the elderly, and their structural form is similar to AGV and AMR. For this reason, research is being introduced and underway to guarantee the right to voluntarily move or improve the convenience of movement for the elderly and severely disabled people who have difficulties in operating a joystick for operating an electric wheelchair. Autonomous driving of mobile robots is a technology that configures prior information on the driving environment as a map DB and operates based on it. However, active driving assistance technology is needed because wheelchairs do not move in a limited space, but can move to a place without a prior map DB or vehicle boarding depending on the passenger’s intention to move. Therefore, a system for estimating the moving position and direction of the wheelchair is needed to develop a driving assistance technology in the relevant driving environment. In order to solve the above problem, this study proposes a position and direction estimation algorithm suitable for active driving of a wheelchair based on a UWB sensor. This proposal is an algorithm for estimating the position and direction of the wheelchair through the fusion of UWB, IMU, and encoder sensors. In this proposal, it is difficult to design an active driving assistance system for wheelchairs due to low accuracy, obstacles, and errors due to signal strength in the position and direction estimation with UWB sensors alone. Therefore, this study proposes a wheelchair driving position and direction estimation system that fuses the dead recording information of a wheelchair and the UWB-based position estimation technique based on sensors applied in IMU and encoders. Applying quantitative verification to the proposed technique, the direction estimation accuracy of the wheelchair of about 15.3° and the position estimation error average of ±15 cm were confirmed, and it was verified that a driving guide for active driving was possible when the sensor system proposed in a mapless environment of the wheelchair was installed at a specific destination.

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Development of an Autonomous Driving Smart Wheelchair for the Physically Weak

Cited by 13 publications

References 16 publications

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

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