Human-wheelchair collaboration through prediction of intention and adaptive assistance

Powered wheelchair users want to be active drivers, not just passengers. However, in some situations (varying from person to person), they may require assistance; hence, research is being carried out into the development of 'smart' wheelchairs. Predominantly, this research has been derived from the field of mobile robotics, focussing on creating autonomous systems, which unfortunately tend to treat the human as little more than a precious piece of cargo. Instead, the design should be based around each individual user's abilities and desires, maximising the amount of control they are given. In this paper, we look at how collaborative control techniques can be used to achieve this, offering the user help, as and when it is required. We then evaluate the effects of this collaboration, which is built by predicting user intentions and responding to these predictions with adaptable levels of assistance

Knowing when to assist: Developmental issues in lifelong assistive robotics

Children and adults with sensorimotor disabilities can significantly increase their autonomy through the use of assistive robots. As the field progresses from short-term, task-specific solutions to long-term, adaptive ones, new challenges are emerging. In this paper a lifelong methodological approach is presented, that attempts to balance the immediate context-specific needs of the user, with the long-term effects that the robots assistance can potentially have on the users developmental trajectory

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

Design requirements and challenges for intelligent power wheelchair use in crowds: learning from expert wheelchair users

An intelligent or smart power wheelchair is normally built on a standard power wheelchair with additional modules for perception, navigation or interaction purposes. It adds autonomy to the wheelchair and provides a technical solution to the safety concerns, thus opening the possibility for people who are considered not suitable to use a standard power wheelchair. Although the research in this field has been going on for decades, most of them focus on dealing with static or simple dynamic environments. In addition, the role of the user is sometimes overlooked during the design and development process. In our project, we aim to design a user-centred intelligent wheelchair and extend its application area to one of the most difficult scenarios faced by wheelchair users­, ­­navigating among crowds. As we start the process of designing a smart wheelchair, we present the results of an initial study with expert wheelchair users' to gain insights into their design requirements and challenges when navigating in crowds

One-shot assistance estimation from expert demonstrations for a shared control wheelchair system

An emerging research problem in the field of assistive robotics is the design of methodologies that allow robots to provide human-like assistance to the users. Especially within the rehabilitation domain, a grand challenge is to program a robot to mimic the operation of an occupational therapist, intervening with the user when necessary so as to improve the therapeutic power of the assistive robotic system. We propose a method to estimate assistance policies from expert demonstrations to present human-like intervention during navigation in a powered wheelchair setup. For this purpose, we constructed a setting, where a human offers assistance to the user over a haptic shared control system. The robot learns from human assistance demonstrations while the user is actively driving the wheelchair in an unconstrained environment. We train a Gaussian process regression model to learn assistance commands given past and current actions of the user and the state of the environment. The results indicate that the model can estimate human assistance after only a single demonstration, i.e. in one-shot, so that the robot can help the user by selecting the appropriate assistance in a human-like fashion