Autonomous Wheelchair Research Articles

An autonomous wheelchair with health monitoring system based on Internet of Thing

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users’ four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs

Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment.

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

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.

Design and Implementation of an Autonomous Wheelchair with Medical Kit Access Using Raspberry Pi

This paper presents an autonomous wheelchair system designed to enhance the mobility of elderly individuals and people with disabilities through advanced automation and artificial intelligence. Recognizing the importance of independent movement for promoting physical and psychological well-being, this system addresses the challenges faced by those with limited mobility. The wheelchair leverages various sensors to minimize the need for human intervention, thereby fostering autonomy and reducing reliance on caregivers. The system includes an Android interface connected via Bluetooth, allowing for intuitive user interaction and control. Utilizing Raspberry Pi technology, the research aims to create a reliable framework that can adapt to user needs through data processing and analysis. Additionally, the system enables effective communication through head-mounted support, allowing users to record and relay information. Ultimately, this autonomous wheelchair aims to improve mobility and confidence for individuals with disabilities while enhancing their overall independence and quality of life.

Design and Implementation of an Autonomous Wheelchair with Medical Kit Access Using Raspberry Pi

Concrete is a fundamental construction material that is continually evolving to enhance its performance and sustainability. This research explores the potential of incorporating natural pumice powder and polypropylene as partial substitutes for cement in M20-grade concrete. The aim is to assess how these alternative materials influence concrete properties and sustainability. Through a comprehensive evaluation, the study provides valuable insights into improving the characteristics and environmental impact of concrete structures. This experimental study examines properties such as split tensile strength, compressive strength, and flexural strength of concrete specimens after curing periods of 7, 14, and 28 days. Concrete mixes with varying proportions of pumice powder (5%, 10%) and polypropylene (5%, 10%), as well as their combinations, are meticulously prepared and tested. The results indicate that adding pumice powder and polypropylene positively affects the strength properties of concrete, with some mixtures performing better than conventional concrete. The analysis reveals the underlying mechanisms contributing to enhanced strength, emphasizing the potential of these materials to boost the durability and long-term sustainability of concrete structures. These findings contribute to ongoing efforts to develop eco-friendly and highly durable concrete, promoting advancements in construction engineering.

Design and Analysis of a Novel Sidewalk Following Visual Controller for an Autonomous Wheelchair

Design and Analysis of a Novel Sidewalk Following Visual Controller for an Autonomous Wheelchair

Improving odometric sensor performance by real-time error processing and variable covariance

This paper presents a new method to increase odometric sensor accuracy by systematic and non-systematic errors processing. Mobile robot localization is improved combining this technique with a filter that fuses the information from several sensors characterized by their covariance. The process focuses on calculating the odometric speed difference with respect to the filter to implement an error type detection module in real time. The correction of systematic errors consists in an online parameter adjustment using the previous information and conditioned by the filter accuracy. This data is also applied to design a variable odometric covariance which describes the sensor reliability and determines the influence of both errors on the robot localization. The method is implemented in a low-cost autonomous wheelchair with a LIDAR, IMU and encoders fused by an UKF algorithm. The experimental results prove that the estimated poses are closer to the real ones than using other well-known previous methods.

Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control

<span lang="EN-US">This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.</span>

Autonomous Wheelchair for Indoor Navigation

Wheelchairs are a very beneficial tool for commuting used by physically challenged people. But not everyone can make the wheelchair move on their own and often depend on help for movement of the wheelchair, making them dependent on constant support for every movement. Even when the physically challenged individuals are indoors, they end up being the dependent. The idea being proposed in the paper is develop a wheelchair that can make the movement of the wheelchair either automatic with inbuilt indoor navigation for easy navigation along with voice-based commands and joystick control for easy control. The wheelchair proved to be extremely beneficial for the physically challenged, at a relatively low cost.

Path Generation Methods for Autonomous Wheelchairs in Congested Stations

Path Generation Methods for Autonomous Wheelchairs in Congested Stations

Augmented Kalman Filter Design in a Localization System Using Onboard Sensors With Intrinsic Delays

This paper presents a novel approach to address the limitations of the conventional Augmented Kalman Filter (AKF) when only on-board incremental sensors are used, which is a more realistic strategy for diverse robot configurations. The proposed AKF focuses on an online calibration of odometric parameters based on velocity measurements. Moreover, it shows the unsuitability of the AKF when data is not perfectly synchronized and introduces a real-time synchronization scheme to enhance the accuracy of parameter adjustments. The effectiveness of the method is demonstrated using an autonomous wheelchair equipped with encoders, a LIDAR and an IMU. A pre-module is added to synchronize LIDAR and IMU signals with respect to the encoders. The lags are included in the state vector and updated based on their reliability to synchronize the data in real time, which addresses the problem of real sensors where the delay period can change over time. The system also incorporates a fuzzy controller that designs a variable filter covariance inversely proportional to measurement reliability, enabling a dynamic parameter adjustment. The experiments show that the proposed localization outperforms the Extended Kalman Filter and the new AKF results provide better parameter estimation compared to when the implementation improvements are not taken into account.

Improving Odometric Model Performance Based on LSTM Networks

This paper presents a localization system for an autonomous wheelchair that includes several sensors, such as odometers, LIDARs, and an IMU. It focuses on improving the odometric localization accuracy using an LSTM neural network. Improved odometry will improve the result of the localization algorithm, obtaining a more accurate pose. The localization system is composed by a neural network designed to estimate the current pose using the odometric encoder information as input. The training is carried out by analyzing multiple random paths and defining the velodyne sensor data as training ground truth. During wheelchair navigation, the localization system retrains the network in real time to adjust any change or systematic error that occurs with respect to the initial conditions. Furthermore, another network manages to avoid certain random errors by using the relationship between the power consumed by the motors and the actual wheel speeds. The experimental results show several examples that demonstrate the ability to self-correct against variations over time, and to detect non-systematic errors in different situations using this relation. The final robot localization is improved with the designed odometric model compared to the classic robot localization based on sensor fusion using a static covariance.

Filling the Gap between Research and Market: Portable Architecture for an Intelligent Autonomous Wheelchair.

Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users' needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms.

Sensor Fusion Algorithm Selection for an Autonomous Wheelchair Based on EKF/UKF Comparison

Sensor Fusion Algorithm Selection for an Autonomous Wheelchair Based on EKF/UKF Comparison

Modeling and Prediction of User Stability and Comfortability on Autonomous Wheelchairs With 3-D Mapping

Traditional manual wheelchairs have a fixed seat with no movement or angle adjustment, which can seriously affect the user's comfort and greatly limit user experience. However, the electric wheelchair relies on strong intelligence and automatic features; it can not only realize the multidegree freedom adjustment of the human body and the seat but also has a rich and powerful man–machine control interface, which greatly facilitates and improves the user experience. This study upgraded a Permobil C400-powered wheelchair with multisensor data fusion technology to enrich its terrain recognition, tipping stability, and comfortability prediction. The tipping stability modeling of the wheelchair dummy system is carried out using multibody dynamics and vibration mechanics to obtain the tipping stability limit and the comfort evaluation of the wheelchair vibration acceleration on the human body during travel. Based on the elevation mapping method, the wheelchair can estimate the terrain from the local point of view at any point in time. At the same time, the RGB-D depth camera is connected to the robot operating system (ROS) system, and the open-source algorithm package RTAB-MAP is used to complete the MAP construction and collect the 3-D point-cloud terrain data. Then, the real 3-D terrain files are generated through the point-cloud stitching technology for stability simulation of the wheelchair–human system. The tipping stability and comfort indexes of the wheelchair–human system when passing over different physical terrains can be obtained. The experimental results show that the IMU data located on the human chest agree well with the simulation analysis data and are suitable for a variety of complex real-terrain conditions, verifying the accuracy of the wheelchair–human system dynamics model and the feasibility of the simulation analysis process. Thus, this modeling and simulation method can predict wheelchair stability and user comfortability well and ensure a high-performance experience.

A New Design Identification and Control Based on GA Optimization for An Autonomous Wheelchair

The daily lifestyle of an average human has changed drastically. Robotics and AI systems are applied to many fields, including the medical field. An autonomous wheelchair that improves the degree of independence that a wheelchair user has can be a very useful contribution to society. This paper presents the design and implementation of an autonomous wheelchair that uses LIDAR to navigate and perform SLAM. It uses the ROS framework and allows the user to choose a goal position through a touchscreen or using deep learning-based voice recognition. It also presents a practical implementation of system identification and optimization of PID control gains, which are applied to the autonomous wheelchair robot. Input/output data were collected using Arduino, consisting of linear and angular speeds and wheel PWM signal commands, and several black-box models were developed to simulate the actual wheelchair setup. The best-identified model was the NLARX model, which had the highest square error (0.1259) among the other candidate models. In addition, using MATLAB, Optimal PID gains were obtained from the genetic algorithm. Performance on real hardware was evaluated and compared to the identified model response. The two responses were identical, except for some of the noise due to the encoder measurement errors and wheelchair vibration.

Wheelchair Assistance and Guidance Using IOT

A Smart Wheelchair is a Power Wheelchair with numerous sensors, assistive technology, and computers built in that allow a disabled person to travel around freely and safely. People with disabilities, such as impairments, handicaps, and life-altering injuries, are the users. The conventional wheelchairs are being increasingly replaced by these new models. Only 5 to 15% of the 70 million disabled individuals worldwide, according to the World Health Organization (WHO), have access to wheelchairs. As a result, we must supply a cost-effective Smart that uses cutting-edge components and technology while keeping costs to a minimum. They have embraced a number of technologies, including artificial intelligence, where they created an autonomous wheelchair that used machine learning principles to travel. Some of them have also used the Internet of Things to manage the wheelchair using a voice recognition system. This report will outline an inexpensive Smart Wheelchair-based Arduino Nano microcontroller and Internet of Things (IoT) technology that has a number of features to help disabled people, particularly poor people who cannot afford an expensive Smart Wheelchair, get the assistance they need to complete daily tasks without help. As a result of this project, a variety of disabled people will be able to afford smart wheelchairs. It will be powered by Arduino Nano, ESP-12e module for Wi-Fi access, MPU6050 for fall detection with voice message notification using IFTTT platform, obstacle detection with buzzer and LED for hazard detection, voice recognition system, and joysticks for wheelchair control.

Cost-Efficient Remote-Controlled Wheelchair

A Smart Wheelchair is a Power Wheelchair with numerous sensors, assistive technology, and computers built in that allow a disabled person to travel around freely and safely. These wheelchair designs are gradually replacing regular wheelchairs, although a sizable number of disabled persons are unable to afford one due to their high pricing. As a result, we must deliver a smart wheelchair that is both affordable and packed with capabilities that take advantage of the most recent hardware and software. There have been many admirable attempts made in recent years to achieve this goal. They have embraced several technologies, including artificial intelligence, where they created an autonomous wheelchair that used machine learning principles to travel. Some of them have also used the Internet of Things to manage wheelchairs using a voice recognition system. This report will present a cost-effective Smart Wheelchair-based on PWM and DIR from microcontroller and IoT technology that have several features to gain disabled people, especially poor people who cannot afford expensive Smart Wheelchair, the required help to finish daily life tasks without external help. To conclude this project, we will make the Smart Wheelchair affordable to a wide range of disabled people and will be using PMDC motor, CYTRON smart drive MIDDS30 dual 30A, 12V 12AH battery and a 6-channel controller to control the wheelchair

Road Surface Map Generation Method for Motion Planning of Autonomous Wheelchair on Rough Terrain

Road Surface Map Generation Method for Motion Planning of Autonomous Wheelchair on Rough Terrain

A new gap-based obstacle avoidance approach: follow the obstacle circle method

AbstractOne of the most challenging tasks for autonomous robots is avoiding unexpected obstacles during their path following operation. Follow the gap method (FGM) is one of the most popular obstacle avoidance algorithms that recursively guides the robot to the goal state by considering the angle to the goal point and the distance to the closest obstacles. It selects the largest gap around the robot, where the gap angle is calculated by the vector to the midpoint of the largest gap. In this paper, a novel obstacle avoidance procedure is developed and applied to a real fully autonomous wheelchair. This proposed algorithm improves the FGM’s travel safety and brings a new solution to the obstacle avoidance task. In the proposed algorithm, the largest gap is selected based on gap width. Moreover, the avoidance angle (similar to the gap center angle of FGM) is calculated considering the locus of the equidistant points from obstacles that create obstacle circles. Monte Carlo simulations are used to test the proposed algorithm, and according to the results, the new procedure guides the robot to safer trajectories compared with classical FGM. The real experimental test results are in parallel to the simulations and show the real-time performance of the proposed approach.