Marie Babel

A visual servoing approach for autonomous corridor following and doorway passing in a wheelchair

Navigating within an unknown indoor environment using an electric wheelchair is a challenging task, especially if the user suffers from severe disabilities. In order to reduce fatigability and increase autonomy, control architectures have to be designed that would assist users in wheelchair navigation. We present a framework for vision-based autonomous indoor navigation in an electric wheelchair capable of following corridors, and passing through open doorways using a single doorpost. Visual features extracted from cameras on board the wheelchair are used as inputs for image based controllers built-in the wheelchair. It has to be noted that no a-priori information is utilized except for the assumption that the wheelchair moves in a typical indoor environment while the system is coarsely calibrated. The designed control schemes have been implemented onto a robotized wheelchair and experimental results show the robust behaviour of the designed system. We devise a visual servoing approach for indoor wheelchair navigation.The fundamental tasks of corridor following and doorway passing are tackled.Results in simulation and on a robotic platform show the convergence and validity of the control system.This work is proposed as a first step in developing the concept of semi-autonomous assistive systems.

Vision-based adaptive assistance and haptic guidance for safe wheelchair corridor following

We devise a low-cost vision-based assistance scheme for wheelchair corridor navigation.Progressive assistance is activated if the wheelchair is in danger, as the user drives manually in a corridor.Natural image features that can be extracted robustly in real-time is employed in order to design the assistive controller.Optimal force feedback, also designed from visual information, is provided using a haptic joystick in order to increase intuitiveness of the system.Experimental results show the feasibility of the system for Commercialization. In case of motor impairments, steering a wheelchair can become a hazardous task. Joystick jerks induced by uncontrolled motions may lead to wall collisions when a user steers a wheelchair along a corridor. This work introduces a low-cost assistive and guidance system for indoor corridor navigation in a wheelchair, which uses purely visual information, and which is capable of providing automatic trajectory correction and haptic guidance in order to avoid wall collisions. A visual servoing approach to autonomous corridor following serves as the backbone to this system. The algorithm employs natural image features which can be robustly extracted in real time. This algorithm is then fused with manual joystick input from the user so that progressive assistance and trajectory correction can be activated as soon as the user is in danger of collision. A force feedback in conjunction with the assistance is provided on the joystick in order to guide the user out of his dangerous trajectory. This ensures intuitive guidance and minimal interference from the trajectory correction system. In addition to being a low-cost approach, it can be seen that the proposed solution does not require an a-priori environment model. Experiments on a robotised wheelchair equipped with a monocular camera prove the capability of the system to adaptively guide and assist a user navigating in a corridor.

Vision-based assistance for wheelchair navigation along corridors

In case of motor impairments, steering a wheelchair can become a hazardous task. Typically, along corridors, joystick jerks induced by uncontrolled motions are source of wall collisions. This paper describes a vision based assistance solution for safe indoor semi-autonomous navigation purposes. To this aim, the control process is based on a visual servoing process designed for wall avoidance purposes. As the patient manually drives the wheelchair, a virtual guide is defined to progressively activate an automatic trajectory correction. The proposed solution does not require any knowledge of the environment. Experiments have been conducted over corridors that present different configurations and illumination conditions. Results demonstrate the ability of the system to smoothly and adaptively assist people during their motions.

Corridor following wheelchair by visual servoing

In this paper, we present an autonomous navigation framework of a wheelchair by means of a single camera and visual servoing. We focus on a corridor following task where no prior knowledge of the environment is required. Our approach embeds an image-based controller, thus avoiding to estimate the pose of the wheelchair. The servoing process matches the non holonomous constraints of the wheelchair and relies on two visual features, namely the vanishing point location and the orientation of the median line formed by the straight lines related to the bottom of the walls. This overcomes the process initialization issue typically raised in the literature. The control scheme has been implemented onto a robotized wheelchair and results show that it can follow a corridor with an accuracy of ±3cm.

Low complex sensor-based shared control for power wheelchair navigation

Motor or visual impairments may prevent a user from steering a wheelchair effectively in indoor environments. In such cases, joystick jerks arising from uncontrolled motions may lead to collisions with obstacles. We here propose a perceptive shared control system that progressively corrects the trajectory as a user manually drives the wheelchair, by means of a sensor-based shared control law capable of smoothly avoiding obstacles. This control law is based on a low complex optimization framework validated through simulations and extensive clinical trials. The provided model uses distance information. Therefore, for low-cost considerations, we use ultrasonic sensors to measure the distances around the wheelchair. The solution therefore provides an efficient assistive tool that does not alter the quality of experience perceived by the user, while ensuring his security in hazardous situations.

On equitably approaching and joining a group of interacting humans

In this work we introduce a low-level system that could be employed by a social robot like a robotic wheelchair or a humanoid, for approaching a group of interacting humans, in order to become a part of the interaction. Taking into account an interaction space that is created when at least two humans interact, a meeting point can be calculated where the robot should reach in order to equitably share space among the interacting group. We propose a sensor-based control task which uses the position and orientation of the humans with respect to the sensor as inputs, to reach the said meeting point while respecting spatial social constraints. Trials in simulation demonstrate the convergence of the control task and its capability as a low-level system for human-aware navigation.

HandiViz project: Clinical validation of a driving assistance for electrical wheelchair

Autonomy and independence in daily life, whatever the impairment of mobility, constitute fundamental needs that participate to the self-esteem and the well-being of disabled people. In this context, assistive technologies are a relevant answer. To address the driving assistance issue, we propose in this paper a unified shared control framework able to smoothly correct the trajectory of the electrical wheelchair. The system integrates the manual control with sensor-based constraints by means of a dedicated optimization strategy. The resulting low-complex and low-cost embedded system is easily plugged onto on-the-shelf wheelchairs. The robotic solution has been then validated through clinical trials that have been conducted within the Rehabilitation Center of Pôle Saint Hélier (France) with 25 volunteering patients presenting different disabling neuro-pathologies. This assistive tool is shown to be intuitive and robust as it respects the user intention, it does not alter perception while reducing the number of collisions in case of hazardous maneuvers or in crowded environment.

Non-rigid target tracking in 2D ultrasound images using hierarchical grid interpolation

In this paper, we present a new non-rigid target tracking method within 2D ultrasound (US) image sequence. Due to the poor quality of US images, the motion tracking of a tumor or cyst during needle insertion is considered as an open research issue. Our approach is based on well-known compression algorithm in order to make our method work in real-time which is a necessary condition for many clinical applications. Toward that end, we employed a dedicated hierarchical grid interpolation algorithm (HGI) which can represent a large variety of deformations compared to other motion estimation algorithms such as Overlapped Block Motion Compensation (OBMC), or Block Motion Algorithm (BMA). The sum of squared difference of image intensity is selected as similarity criterion because it provides a good trade-off between computation time and motion estimation quality. Contrary to the others methods proposed in the literature, our approach has the ability to distinguish both rigid and non-rigid motions which are observed in ultrasound image modality. Furthermore, this technique does not take into account any prior knowledge about the target, and limits the user interaction which usually complicates the medical validation process. Finally, a technique aiming at identifying the main phases of a periodic motion (e.g. breathing motion) is introduced. The new approach has been validated from 2D ultrasound images of real human tissues which undergo rigid and non-rigid deformations.

Fast pseudo-semantic segmentation for joint region-based hierarchical and multiresolution representation

In this paper, we present a new scalable segmentation algorithm called JHMS (Joint Hierarchical and Multiresolution Segmentation) that is characterized by region-based hierarchy and resolution scalability. Most of the proposed algorithms either apply a multiresolution segmentation or a hierarchical segmentation. The proposed approach combines both multiresolution and hierarchical segmentation processes. Indeed, the image is considered as a set of images at different levels of resolution, where at each level a hierarchical segmentation is performed. Multiresolution implies that a segmentation of a given level is reused in further segmentation processes operated at next levels so that to insure contour consistency between different resolutions. Each level of resolution provides a Region Adjacency Graph (RAG) that describes the neighborhood relationships between regions within a given level of the multiresolution representation. Region label consistency is preserved thanks to a dedicated projection algorithm based on inter-level relationships. Moreover, a preprocess based on a quadtree partitioning reduces the amount of input data thus leading to a lower overall complexity of the segmentation framework. Experiments show that we obtain effective results when compared to the state of the art together with a lower complexity.

Analysis of an adaptive strategy for equitably approaching and joining human interactions

Since social, assistive and companion robots need to navigate within human crowds, understanding spatial social conventions while designing navigation solutions for such robots is an essential issue. This work presents analysis of an socially compliant robot motion strategy that could be employed by social robots such as humanoids, service robots or intelligent wheelchairs, for approaching and joining humans groups in interaction, and then become an equitable part of the interaction. Following our previous work that formalized the motion strategy, a detailed synthesis is presented here with experiments that validate the proposed system in the real world.