Navigating an electrical wheelchair can be very challenging due to its large size and limited maneuverability. Additionally, target users often suffer from cognitive or physical disabilities, which interfere with safe navigation. Therefore, a robotic wheelchair that helps to drive can prove invaluable. Such a wheelchair shares the control with its human operator. Typically, robots excel in fine-motion control whereas users want to remain in charge. Hence, the robot should focus its help locally and let the user decide about global behavior. Further, an effective robot should understand the navigation plans of its user. It needs to consider the user's abilities to avoid frustrating the user with wrong assistance. In order to address these requirements, we propose a probabilistic framework to recognize local navigation plans in a user-specific way. The framework infers navigation plans online and provides a method to calibrate all model parameters from real driving data. It fuses past local information with a user-specific model to reason about how and where the user intends to navigate. We illustrate the validity of our approach by recognizing the local navigation plans of a spastic user driving in a daily environment.

This article introduces a set of novel haptic guidance algorithms intended to provide intuitive and reliable assistance for electric wheelchair navigation through narrow or crowded spaces. The proposed schemes take hereto the non-holonomic nature and a detailed geometry of the wheelchair into consideration. The methods encode the environment as a set of collision-free circular paths and, making use of a model-free impedance controller, `haptically' guide the user along collision-free paths or away from obstructed paths or paths that simply do not coincide with the motion intended by the user. The haptic feedback plays a central role as it establishes a fast bilateral communication channel between user and wheelchair controller and allows a direct negotiation about wheelchair motion. If found unsatisfactory, suggested trajectories can always be overruled by the user. Relying on inputs from user modeling and intention recognition schemes, the system can reduce forces needed to move along intended directions, thereby avoiding unnecessary fatigue of the user. A commercial powered wheelchair was upgraded and feasability tests were conducted to validate the proposed methods. The potential of the proposed approaches was hereby demonstrated.

For people with very severe motor dysfunctions, Brain-Computer Interfaces (BCIs) may provide the solution to regain mobility and manipulation capabilities. Unfortunately, BCIs are characterized by a limited bandwidth and uncertainty on the BCI output. In the past, we have developed a Bayesian plan recognition framework that estimates from uncertain human-robot interface signals the task a robot should execute. This paper extends our plan recognition framework to incorporate uncertain BCI signals. A benchmark test is proposed and adopted to evaluate both the plan recognition framework and the performance of the BCI user, for the concrete application of wheelchair driving.

Many elderly and disabled people experience difficulties when maneuvering an electric wheelchair. In order to make wheelchair driving a safer and more comfortable experience, there has long been the claim to equip wheelchairs with some form of intelligent controller assisting in difficult or unsafe situations. It has been observed that every user presents different symptoms causing a specific driving pattern. Therefore, if the user is to be helped and not frustrated, his/her particular driving behavior should be taken into account when assisting him/her. In this paper we present a general user modeling technique for our Bayesian framework for plan recognition and shared wheelchair control. Plan recognition corresponds to estimating the plan a user has in mind. Assistive actions can then be taken based on the estimated user plan. A user modeling technique based on Gaussian processes has been selected, which can be adapted online to any type of driving style. The potential of Gaussian processes for user modeling is illustrated on a case study with a disabled patient suffering from spastic quadriplegia.

The last years have witnessed a significant increase in the percentage of old and disabled people. Members of this population group very often require extensive help for performing daily tasks like moving around or grasping objects. Unfortunately, assistive technology is not always available to people needing it. For instance, steering a wheelchair can represent an extremely fatiguing or simply impossible task to many elderly or disabled users. Most of the existing assistance platforms try to help users without considering their specific needs. However, driving performance may vary considerably across users due to different pathologies or just due to temporary effects like fatigue. Therefore, we propose in this paper a user adapted shared control approach aimed at helping users in driving a power wheelchair. Adaption to the user is achieved by estimating the user's true intent out of potentially noisy steering signals before assisting him/her. The user's driving performance is explicitly modeled in order to recognize the user's intention or plan together with the uncertainty on it. Safe navigation is achieved by merging the potentially noisy input of the user with fine motion trajectories computed online by a 3D planner. Encouraging results on assisting a user who cannot steer to the left are reported on K.U.Leuven's intelligent wheelchair Sharioto.

Many elderly and disabled people today experience difficulties when manoeuvring an electric wheelchair. In order to help these people, several robotic assistance platforms have been devised in the past. In most cases, these platforms consist of separate assistance modes, and heuristic rules are used to automatically decide which assistance mode should be selected in each time step. As these decision rules are often hard-coded and do not take uncertainty regarding the user's intent into account, assistive actions may lead to confusion or even irritation if the user's actual plans do not correspond to the assistive system's behavior. In contrast to previous approaches, this paper presents a more user-centered approach for recognizing the intent of wheelchair drivers, which explicitly estimates the uncertainty on the user's intent. The paper shows the benefit of estimating this uncertainty using experimental results with our wheelchair platform Sharioto

This paper describes a novel adaptive filter approach to reduce the handicap a patient may experience when navigating an electric wheelchair. The filter automatically adapts to the specific handicap the patient has by training a connectionist structure that converts the joystick signal of the patient to the signal a reference user would give in the same context. Experimental results show that for various handicaps the filter improves the driving performance significantly