Self-E: a self-driving wheelchair for elders and physically challenged

Megalingam, Rajesh Kannan; Rajendraprasad, Anandu; Raj, Akhil; Teja, Chinta Ravi; Sreekanth, Sarath; Sankaran, Ravi

doi:10.1007/s41315-021-00209-9

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…Moreover, wheelchair users have a higher probability of upper limb injuries due to the use of their arms [2]. Additionally, the joystick controllers of electric wheelchairs are difficult for users with hand impairments [3]. If autonomous driving functionality is applied to electric wheelchairs, users could move to their desired destinations without the need to manually operate the wheelchair [2].…”

Section: Introductionmentioning

confidence: 99%

Shared eHMI: Bridging Human–Machine Understanding in Autonomous Wheelchair Navigation

Zhang,

Song,

Huang

et al. 2024

Applied Sciences

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As automated driving system (ADS) technology is adopted in wheelchairs, clarity on the vehicle’s imminent path becomes essential for both users and pedestrians. For users, understanding the imminent path helps mitigate anxiety and facilitates real-time adjustments. For pedestrians, this insight aids in predicting their next move when near the wheelchair. This study introduces an on-ground projection-based shared eHMI approach for autonomous wheelchairs. By visualizing imminent motion intentions on the ground by integrating real and virtual elements, the approach quickly clarifies wheelchair behaviors for all parties, promoting proactive measures to reduce collision risks and ensure smooth wheelchair driving. To explore the practical application of the shared eHMI, a user interface was designed and incorporated into an autonomous wheelchair simulation platform. An observation-based pilot study was conducted with both experienced wheelchair users and pedestrians using structured questionnaires to assess the usability, user experience, and social acceptance of this interaction. The results indicate that the proposed shared eHMI offers clearer motion intentions display and appeal, emphasizing its potential contribution to the field. Future work should focus on improving visibility, practicality, safety, and trust in autonomous wheelchair interactions.

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Section: Introductionmentioning

confidence: 99%

Shared eHMI: Bridging Human–Machine Understanding in Autonomous Wheelchair Navigation

Zhang,

Song,

Huang

et al. 2024

Applied Sciences

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“…Robotic technology has become ubiquitous, spanning across various fields such as agriculture [1], search and rescue [2], surgery [3], rehabilitation [4], service [5], healthcare [6], and underwater operations [7]. In recent years, the interaction between humans and robots has surged significantly.…”

Section: Introductionmentioning

confidence: 99%

Reduced Kinematic Error for Position Accuracy in a High-Torque, Lightweight Actuator

Megalingam,

Vadivel,

Manoharan

et al. 2024

Actuators

Self Cite

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In this paper, we propose the design, development, and testing of high-torque and lightweight actuators suitable for lightweight robotic applications. The detailed design of the actuator module is described, and its performance evaluation is also presented. Further, the mathematical modelling of the actuator is discussed. Various performance analysis tests were carried out for the elucidation of the designed actuator, which included primarily position, velocity, and torque analyses. The position accuracy analysis included position repeatability at the maximum payload for calculating the acceptable tolerance. The velocity elucidation included a velocity test for the variable load. The torque analysis of the actuator was completed at different supply currents. These tests and the results indicate that the proposed actuator has high precision in reaching the desired position and provides a stabilized performance with variable loads up to the limit for which it was designed. Based on the torque output and the weight, the proposed actuator could be a good fit for lightweight robotic applications.

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“…Autonomous guided mobility is achieved by installing inertial sensors, lidar, vision and range sensors on the wheelchair to sense the wheelchair’s surroundings [ 21 , 22 ]. Megalingam et al installed 2D lidar on the wheelchair to enable autonomous navigation of the wheelchair within a specified area without human control [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

IoT Wheelchair Control System Based on Multi-Mode Sensing and Human-Machine Interaction

Cui

Huang

et al. 2022

Micromachines

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Traditional wheelchairs are unable to actively sense the external environment during use and have a single control method. Therefore, this paper develops an intelligent IoT wheelchair with the three functions, as follows. (1) Occupant-wheelchair-environment multimode sensing: the PAJ7620 sensor is used to recognize gesture information, while GPS (Global Positioning System) and IMU (Inertial Measurement Unit) sensors are used to sense positioning, speed and postural information. In addition, Lidar, DHT11, and BH1750 sensors obtain environmental information such as road information, temperature and humidity and light intensity. (2) Fusion control scheme: a mobile control scheme based on rocker and gesture recognition, as well as a backrest and footrest lifting, lowering and movement control scheme based on Tencent Cloud and mobile APP (Application). (3) Human-machine interaction: the wheelchair is docked to Tencent IoT Explorer through ESP8266 WiFi module, using MQTT (Message Queuing Telemetry Transport) protocol is used to upload sensory data, while the wheelchair status can be viewed and controlled on the APP. The wheelchair designed in this paper can sense and report the status of the occupant, environment and wheelchair in real time, while the user can view the sensory data on the mobile APP and control the wheelchair using the rocker, gestures and APP.

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Self-E: a self-driving wheelchair for elders and physically challenged

Cited by 12 publications

References 28 publications

Shared eHMI: Bridging Human–Machine Understanding in Autonomous Wheelchair Navigation

Shared eHMI: Bridging Human–Machine Understanding in Autonomous Wheelchair Navigation

Reduced Kinematic Error for Position Accuracy in a High-Torque, Lightweight Actuator

IoT Wheelchair Control System Based on Multi-Mode Sensing and Human-Machine Interaction

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