Optimal Wheelchair Multi-LiDAR Placement for Indoor SLAM

One of the most prevalent technologies used in modern robotics is Simultaneous Localization and Mapping or, SLAM. Modern SLAM technologies usually employ a number of different probabilistic mathematics to perform processes that enable modern robots to not only map an environment but, also, concurrently localize themselves within said environment. Existing open-source SLAM technologies not only range in the different probabilistic methods they employ to achieve their task but, also, by how well the task is achieved and by their computational requirements. Additionally, the positioning of the sensors in the robot also has a substantial effect on how well these technologies work. Therefore, this dissertation is dedicated to the comparison of existing open-source ROS implemented 2D SLAM technologies and in the maximization of the performance of said SLAM technologies by researching optimal sensor placement in a Intelligent Wheelchair context, using SLAM performance as a benchmark

Using a simple expert system to assist a powered wheelchair user

A simple expert system is described that helps wheelchair users to drive their wheelchairs. The expert system takes data in from sensors and a joystick, identifies obstacles and then recommends a safe route. Wheelchair users were timed while driving around a variety of routes and using a joystick controlling their wheelchair via the simple expert system. Ultrasonic sensors are used to detect the obstacles. The simple expert system performed better than other recently published systems. In more difficult situations, wheelchair drivers did better when there was help from a sensor system. Wheelchair users completed routes with the sensors and expert system and results are compared with the same users driving without any assistance. The new systems show a significant improvement

A Personal Robot as an Improvement to the Customers’ In- Store Experience

Robotics is a growing industry with applications in numerous markets, including retail, transportation, manufacturing, and even as personal assistants. Consumers have evolved to expect more from the buying experience, and retailers are looking at technology to keep consumers engaged. In today’s highly competitive business climate, being able to attract, serve, and satisfy more customers is a key to success. It is our belief that smart robots will play a significant role in physical retail in the future. One successful example is wGO, a robotic shopping assistant developed by Follow Inspiration. The wGO is an autonomous and self-driven shopping cart, designed to follow people with reduced mobility (the elderly, people in wheelchair, pregnant women, those with temporary reduced mobility, etc.) in commercial environments. With the Retail Robot, the user can control the shopping cart without the need to push it. This brings numerous advantages and a higher level of comfort since the user does not need to worry about carrying the groceries or pushing the shopping cart. The wGO operates under a vision-guided approach based on user-following with no need for any external device. Its integrated architecture of control, navigation, perception, planning, and awareness is designed to enable the robot to successfully perform personal assistance, while the user is shopping

Smarter wheelchairs who can talk to each other: An integrated and collaborative approach

2012 IEEE 14th International Conference on e-Health Networking, Applications and Services, Healthcom 2012, Beijing, 10-13 October 2012Pervasive computing technologies can benefit the injured, disabled or elderly people in their daily lives, and smart wheelchair has been a representative of this kind of technologies. However, most existing smart wheelchairs have limitations on extensibility and flexibility of building new functionalities. One big reason is they are just stand-alone ones considering other wheelchairs and surrounding things as dummy objects. To address this issue, we proposed an integrated and collaborative approach: Smarter Wheelchairs that can "talk" to each other, and even "talk" to other things in the surrounding. Smarter Wheelchairs take advantages of smart objects deployed in living environment or hospital environment. Smarter Wheelchairs can harness functions provided by smart objects, therefore, their functionality can be flexibly extended. We have implemented and evaluated a prototype system of Smarter Wheelchairs to demonstrate the feasibility and efficiency of our approach.Department of ComputingRefereed conference pape

Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload

Powered wheelchair users often struggle to drive safely and effectively and in more critical cases can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists the user as and when they require help. The system uses a multiple–hypotheses method to predict the driver’s intentions and if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance, but, perhaps more importantly, we characterise the user performance, in an experiment that combines eye–tracking with a secondary task. Without assistance, participants experienced multiple collisions whilst driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely, but they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain–machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input

Combined visual odometry and visual compass for off-road mobile robots localization

In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strateg

Brain-Controlled Wheelchairs: A Robotic Architecture

Independent mobility is core to being able to perform activities of daily living by oneself. However, powered wheelchairs are not an option for a large number of people who are unable to use conventional interfaces, due to severe motor–disabilities. Non-invasive brain–computer interfaces (BCIs) offer a promising solution to this interaction problem and in this article we present a shared control architecture that couples the intelligence and desires of the user with the precision of a powered wheelchair. We show how four healthy subjects are able to master control of the wheelchair using an asynchronous motor–imagery based BCI protocol and how this results in a higher overall task performance, compared with alternative synchronous P300–based approaches