Eric Demeester

User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving

Abstract Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to ease maneuvering, powered wheelchairs have been equipped with sensors, additional computing power and intelligence by various research groups. This paper presents a Bayesian approach to maneuvering assistance for wheelchair driving, which can be adapted to a specific user. The proposed framework is able to model and estimate even complex user intents, i.e. wheelchair maneuvers that the driver has in mind. Furthermore, it explicitly takes the uncertainty on the user’s intent into account. Besides during intent estimation, user-specific properties and uncertainty on the user’s intent are incorporated when taking assistive actions, such that assistance is tailored to the user’s driving skills. This decision making is modeled as a greedy Partially Observable Markov Decision Process (POMDP). Benefits of this approach are shown using experimental results in simulation and on our wheelchair platform Sharioto.

Bayesian Estimation of Wheelchair Driver Intents: Modeling Intents as Geometric Paths Tracked by the Driver

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 users intent into account, assistive actions may lead to confusion or even irritation if the users actual plans do not correspond to the assistive systems 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 users intent. The paper shows the benefit of estimating this uncertainty using experimental results with our wheelchair platform Sharioto

Lino, the user-interface robot

This paper reports on the development of a domestic user-interface robot that is able to have a natural human interaction by speech and emotional feedback and is able to navigate in a home environment. The natural interaction with the user is achieved by means of a mechanical head able to express emotions. The robot is aware of the position and identities of the users, both from visual and auditory information. The robot estimates its location in the environment with an appearance-based localization method using a stereo camera system. The navigation to the goal is achieved with a hybrid method, combining planning with reactive control. The robot is designed to operate in an intelligent environment, such that external information can be used to localize users and their intentions (context awareness), and that additional information can be retrieved from various databases in the environment. The result is a service robot that can have a simple dialogue with the user, provide information in a natural way (speech and expressions) and can be instructed to navigate to any specific goal in the environment.

Towards safe human-robot interaction in robotic cells: An approach based on visual tracking and intention estimation

Removing the safety fences that separate humans and robots, to allow for an effective human-robot interaction, requires innovative safety control systems. An advanced functionality of a safety controller might be to detect the presence of humans entering the robotic cell and to estimate their intention, in order to enforce an effective safety reaction. This paper proposes advanced algorithms for cognitive vision, empowered by a dynamic model of human walking, for detection and tracking of humans. Intention estimation is then addressed as the problem of predicting online the trajectory of the human, given a set of trajectories of walking people learnt offline using an unsupervised classification algorithm. Results of the application of the presented approach to a large number of experiments on volunteers are also reported.

Bayesian plan recognition and shared control under uncertainty: assisting wheelchair drivers by tracking fine motion paths

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 users true intent out of potentially noisy steering signals before assisting him/her. The users driving performance is explicitly modeled in order to recognize the users 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.Leuvens intelligent wheelchair Sharioto.

Selection of suitable human-robot interaction techniques for intelligent wheelchairs

There are many user interfaces already available to drive electric wheelchairs. These enable users to convey their intention explicitly to the wheelchair control system. However, we have observed users who find it nonetheless extremely difficult to do so. For some severely disabled users it is almost impossible to drive a conventional electric wheelchair in a safe way. This paper explores several ways of implicit communication to assist the user to perform daily manoeuvres. Some initial user trials have been performed at a hospital. The results are evaluated and conclusions are drawn on the selection of suitable human-robot interaction techniques.

Probabilistic approach to recognize local navigation plans by fusing past driving information with a personalized user model

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 users 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.

Global dynamic window approach for holonomic and non-holonomic mobile robots with arbitrary cross-section

This paper presents an extension of current global dynamic window approaches to holonomic and nonholonomic mobile robots with an arbitrary cross-section. The algorithm proceeds in two stages. In order to account for an arbitrary robot footprint, the first stage takes the robots orientation explicitly into account by constructing a navigation function in the (x, y, /spl theta/) configuration space. In a second stage, an admissible velocity is chosen from a window around the robots current velocity, which contains all velocities that can be reached under the acceleration constraints. Fast computation over large areas is achieved by adopting multi-resolution (x, y) and (x, y, /spl theta/) planning. Several measures are taken to obtain safe and robust robot behaviour. Experimental results on our wheelchair test platform show the feasibility of the approach.

Shared Autonomy for Wheel Chair Control: Attempts to Assess the User's Autonomy

People who suffer from paresis, tremor, spasticity etc. may experience considerable difficulties when driving electric wheel chairs. A sensor-based wheel chair may assist these users in their daily driving manoeuvres. There are two extreme cases: on the one hand, the wheel chair can be fully in control and on the other, the user can be fully in control. It is however difficult to determine a suitable level of shared autonomy situated in between these two extreme cases. This paper presents a framework for shared autonomy and addresses the issue of assessing the user’s autonomy.

Is structure needed for omnidirectional visual homing

Upcoming fast vision techniques for finding image correspondences enable reliable real-time visual homing, i.e. the guidance of a mobile robot from a arbitrary start pose towards a goal pose defined by an image taken there. Two approaches emerge in the field that differ in the fact that the structure of the scene is estimated or not. In this paper, we compare these two approaches for the general case and especially for our application, being automatic wheelchair navigation.