The research presented in this paper describes a new architecture for controlling powered wheelchairs. A Raspberry Pi microcomputer is considered to assist in controlling direction. A Raspberry Pi is introduced between user input switches and powered wheelchair motors to create an intelligent Human Machine Interface (HCI). An electronic circuit is designed that consists of an ultrasonic sensor array and a set of control relays. The sensors delivered information about obstructions in the surrounding environment of the wheelchair. Python programming language was used to create a program that digitized the user switches output and assessed information provided by the ultrasonic sensor array. The program was installed on a Raspberry Pi and the Raspberry Pi controlled power delivered to the motors. Tests were conducted and results showed that the new system successfully assisted a wheelchair user in avoiding obstacles. The new architecture can be used to intelligently interface any input device or sensor system to powered wheelchair.

Using an expert system to make driving a poweredwheelchair less problematic is investigated. The system interprets sensor and joystick signals and then mixes them and improves that collaboration to control speed and direction. Ultrasonic sensors are used to identify hazardous circumstances and suggest a safer direction and speed. Results from drivers completing a series of timed routes are presented. Users completed tests using joysticks to control their chair with and then without a microcomputer and sensor system. A recent system is used to compare and contrast the results. This new system consistently performed quicker than the recent system. It also appears that the quantity of support provided by the sensors and microcomputer should be adjusted depending on situations and surroundings

This paper presents a new technique for controlling powered wheelchairs. A Raspberry Pi microcomputer is used to assist in controlling direction. A Raspberry Pi is inserted between user input switches and powered wheelchair motors to create a more intelligent Human Machine Interface (HMI). An electronic circuit is created that consists of an ultrasonic sensor array and a set of control relays. The sensors provided information about obstacles surrounding the wheelchair. Python programming language was used to create a code that digitized the output from the user switches and assessed information provided by the ultrasonic sensor array. The code was loaded onto a Raspberry Pi and the Raspberry Pi controlled voltages supplied to the motors. Tests were conducted and results showed that the new system can successfully assist a wheelchair user in avoiding obstacles. The system can be used as an intelligent interface between any input device or sensor system and wheelchair motors.

The Analytical Hierarchy Process (AHP) is utilized to propose a driving course for a powered-wheelchair. A safe route for a wheelchair is proposed by a decision-making system that aims to avoid obstacles. Two ultrasonic transceivers are fitted onto a wheelchair. The area in front of a wheelchair is segmented to left and right zones. The system inputs are distance to an object from the midpoint of the chair, distance to an object from the left of the chair and distance to an object from the right of the chair. The resulting route is a blend between a provided direction from a user's input device and a proposed direction from the decision-making system that steers a powered-wheelchair to safely avoid obstacles in the way of the wheelchair. The system helps a disabled user to navigate their wheelchair by deciding on a direction that is a compromise between a direction provided by the sensors and a direction desired by the driver. Sensitivity analysis investigates the effects of risk and uncertainty on the resulting directions. An appropriate direction is identified but a human driver can over-ride the decision if necessary.

The research presented in this paper creates an intelligent system that collects powered wheelchair users' driving session data. The intelligent system is based on a Python programming platform. A program is created that will collect data for future analysis. The collected data considers driving session details, the ability of a driver to operate a wheelchair, and the type of input devices used to operate a powered wheelchair. Data is collected on a Raspberry Pi microcomputer and is sent after each session via email. Data is placed in the body of the emails, in an attached file and saved on microcomputer memory. Modifications to the system is made to meet confidentiality and privacy concerns of potential users. Data will be used for future analysis and will be considered as a training data set to teach an intelligent system to predict future path patterns for different wheelchair users. In addition, data will be used to analyze the ability of a user to drive a wheelchair, and monitor users' development from one session to another, compare the progress of various users with similar disabilities and identify the most appropriate input device for each user and path.