Wheelchair collaborative control for disabled users navigating indoors.

https://v2.sherpa.ac.uk/id/publication/12586Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual control is not possible. However, these persons should contribute to control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared control approaches either give control to person or robot depending on the situation. Methods and materials: We propose a new shared control technique where robot and person contribute simultaneously to control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as collaborative control. Results: Collaborative control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when collaborative control is used. (C) 2011 Elsevier B.V. All rights reserved

On the construction of a skill-based wheelchair navigation profile.

https://v2.sherpa.ac.uk/id/publication/42766Assisted wheelchair navigation is of key importance for persons with severe disabilities. The problem has been solved in different ways, usually based on the shared control paradigm. This paradigm consists of giving the user more or less control on a need basis. Naturally, these approaches require personalization: each wheelchair user has different skills and needs and it is hard to know a priori from diagnosis how much assistance must be provided. Furthermore, since there is no such thing as an average user, sometimes it is difficult to quantify the benefits of these systems. This paper proposes a new method to extract a prototype user profile using real traces based on more than 70 volunteers presenting different physical and cognitive skills. These traces are clustered to determine the average behavior that can be expected from a wheelchair user in order to cope with significant situations. Processed traces provide a prototype user model for comparison purposes, plus a simple method to obtain without supervision a skill-based navigation profile for any user while he/she is driving. This profile is useful for benchmarking but also to determine the situations in which a given user might require more assistance after evaluating how well he/she compares to the benchmark. Profile-based shared control has been successfully tested by 18 volunteers affected by left or right brain stroke at Fondazione Santa Lucia, in Rome, Italy

A new multi-criteria optimization strategy for shared control in wheelchair assisted navigation

In todays aging society, many people require mobility assistance, that can be provided by robotized assistive wheelchairs with a certain degree of autonomy when manual control is unfeasible due to disability. Robot wheelchairs, though, are not supposed to be completely in control because lack of human intervention may lead to loss of residual capabilities and frustration. Most of these systems rely on shared control, which typically consists of swapping control from human to robot when needed. However, this means that persons never deal with situations they find difficult. We propose a new shared control approach to allow constant cooperation between humans and robots, so that assistance may be adapted to the user’s skills. Our proposal is based on the reactive navigation paradigm, where robot and human commands become different goals in a Potential Field. Our main novelty is that human and robot attractors are weighted by their respective local efficiencies at each time instant. This produces an emergent behavior that combines both inputs in an efficient, safe and smooth way and is dynamically adapted to the user’s needs. The proposed control scheme has been successfully tested at hospital Fondazione Santa Lucia (FSL) in Rome with several volunteers presenting different disabilities