Implementation of target tracking in Smart Wheelchair Component System

Independent mobility is critical to individuals of any age. While the needs of many individuals with disabilities can be satisfied with power wheelchairs, some members of the disabled community find it difficult or impossible to operate a standard power wheelchair. This population includes, but is not limited to, individuals with low vision, visual field neglect, spasticity, tremors, or cognitive deficits. To meet the needs of this population, our group is involved in developing cost effective modularly designed Smart Wheelchairs. Our objective is to develop an assistive navigation system which will seamlessly integrate into the lifestyle of individual with disabilities and provide safe and independent mobility and navigation without imposing an excessive physical or cognitive load. The Smart Wheelchair Component System (SWCS) can be added to a variety of commercial power wheelchairs with minimal modification to provide navigation assistance. Previous versions of the SWCS used acoustic and infrared rangefinders to identify and avoid obstacles, but these sensors do not lend themselves to many desirable higher-level behaviors. To achieve these higher level behaviors we integrated a Continuously Adapted Mean Shift (CAMSHIFT) target tracking algorithm into the SWCS, along with the Minimal Vector Field Histogram (MVFH) obstacle avoidance algorithm. The target tracking algorithm provides the basis for two distinct operating modes: (1) a "follow-the-leader" mode, and (2) a "move to stationary target" mode.The ability to track a stationary or moving target will make smart wheelchairs more useful as a mobility aid, and is also expected to be useful for wheeled mobility training and evaluation. In addition to wheelchair users, the caregivers, clinicians, and transporters who provide assistance to wheelchair users will also realize beneficial effects of providing safe and independent mobility to wheelchair users which will reduce the level of assistance needed by wheelchair users

Development of a Modular Real-time Shared-control System for a Smart Wheelchair

In this paper, we propose a modular navigation system that can be mounted on a regular powered wheelchair to assist disabled children and the elderly with autonomous mobility and shared-control features. The lack of independent mobility drastically affects an individual's mental and physical health making them feel less self-reliant, especially children with Cerebral Palsy and limited cognitive skills. To address this problem, we propose a comparatively inexpensive and modular system that uses a stereo camera to perform tasks such as path planning, obstacle avoidance, and collision detection in environments with narrow corridors. We avoid any major changes to the hardware of the wheelchair for an easy installation by replacing wheel encoders with a stereo camera for visual odometry. An open source software package, the Real-Time Appearance Based Mapping package, running on top of the Robot Operating System (ROS) allows us to perform visual SLAM that allows mapping and localizing itself in the environment. The path planning is performed by the move base package provided by ROS, which quickly and efficiently computes the path trajectory for the wheelchair. In this work, we present the design and development of the system along with its significant functionalities. Further, we report experimental results from a Gazebo simulation and real-world scenarios to prove the effectiveness of our proposed system with a compact form factor and a single stereo camera

An Incremental Navigation Localization Methodology for Application to Semi-Autonomous Mobile Robotic Platforms to Assist Individuals Having Severe Motor Disabilities.

In the present work, the author explores the issues surrounding the design and development of an intelligent wheelchair platform incorporating the semi-autonomous system paradigm, to meet the needs of individuals with severe motor disabilities. The author presents a discussion of the problems of navigation that must be solved before any system of this type can be instantiated, and enumerates the general design issues that must be addressed by the designers of systems of this type. This discussion includes reviews of various methodologies that have been proposed as solutions to the problems considered. Next, the author introduces a new navigation method, called Incremental Signature Recognition (ISR), for use by semi-autonomous systems in structured environments. This method is based on the recognition, recording, and tracking of environmental discontinuities: sensor reported anomalies in measured environmental parameters. The author then proposes a robust, redundant, dynamic, self-diagnosing sensing methodology for detecting and compensating for hidden failures of single sensors and sensor idiosyncrasies. This technique is optimized for the detection of spatial discontinuity anomalies. Finally, the author gives details of an effort to realize a prototype ISR based system, along with insights into the various implementation choices made

3D Perception Based Lifelong Navigation of Service Robots in Dynamic Environments

Lifelong navigation of mobile robots is to ability to reliably operate over extended periods of time in dynamically changing environments. Historically, computational capacity and sensor capability have been the constraining factors to the richness of the internal representation of the environment that a mobile robot could use for navigation tasks. With affordable contemporary sensing technology available that provides rich 3D information of the environment and increased computational power, we can increasingly make use of more semantic environmental information in navigation related tasks.A navigation system has many subsystems that must operate in real time competing for computation resources in such as the perception, localization, and path planning systems. The main thesis proposed in this work is that we can utilize 3D information from the environment in our systems to increase navigational robustness without making trade-offs in any of the real time subsystems. To support these claims, this dissertation presents robust, real world 3D perception based navigation systems in the domains of indoor doorway detection and traversal, sidewalk-level outdoor navigation in urban environments, and global localization in large scale indoor warehouse environments.The discussion of these systems includes methods of 3D point cloud based object detection to find respective objects of semantic interest for the given navigation tasks as well as the use of 3D information in the navigational systems for purposes such as localization and dynamic obstacle avoidance. Experimental results for each of these applications demonstrate the effectiveness of the techniques for robust long term autonomous operation

The advancement of an obstacle avoidance bayesian neural network for an intelligent wheelchair

In this paper, an advanced obstacle avoidance system is developed for an intelligent wheelchair designed to support people with mobility impairments who also have visual, upper limb, or cognitive impairment. To avoid obstacles, immediate environment information is continuously updated with range data sampled by an on-board laser range finder URG-04LX. Then, the data is transformed to find the relevant information to the navigating process before being presented to a trained obstacle avoidance neural network which is optimized under the supervision of a Bayesian framework to find its structure and weight values. The experiment results showed that this method allows the wheelchair to avoid collisions while simultaneously navigating through an unknown environment in real-time. More importantly, this new approach significantly enhances the performance of the system to pass narrow openings such as door passing. © 2013 IEEE

A multi-hierarchical symbolic model of the environment for improving mobile robot operation

El trabajo desarrollado en esta tesis se centra en el estudio y aplicación de estructuras multijerárquicas, que representan el entorno de un robot móvil, con el objetivo de mejorar su capacidad de realizar tareas complejas en escenarios humanos. Un robot móvil debe poseer una representación simbólica de su entorno para poder llevar a cabo operaciones deliberativas, por ejemplo planificar tareas. Sin embargo a la hora de representar simbólicamente entornos reales, dado su complejidad, es imprescindible contar con mecanismos capaces de organizar y facilitar el acceso a la ingente cantidad de información que de ellos se deriva. Aparte del inconveniente de tratar con grandes cantidades de información, existen otros problemas subyacentes de la representación simbólica de entornos reales, los cuales aún no han sido resueltos por completo en la literatura científica. Uno de ellos consiste en el mantenimiento de la representación simbólica optimizada con respecto a las tareas que el robot debe realizar, y coherente con el entorno en el que se desenvuelve. Otro problema, relacionado con el anterior es la creación/modificación de la información simbólica a partir de información meramente sensorial (este problema es conocido como symbol-grounding). Esta tesis estudia estos problemas y aporta soluciones mediante estructuras multijerárquicas. Estas estructuras simbólicas, basadas en el concepto de abstracción, imitan la forma en la que los humanos organizamos la información espacial y permite a un robot móvil mejorar sus habilidades en entornos complejos. Las principales contribuciones de este trabajo son: - Se ha formalizado matemáticamente un modelo simbólico basado en múltiples abstracciones (multijerarquías) mediante Teoría de Categorías. Se ha desarrollado un planificador de tareas eficiente que es capaz de aprovechar la organización jerárquica del modelo simbólico del entorno. Nuestro método ha sido validado matemáticamente y se han implementado y comparado dos variantes del mismo (HPWA-1 y HPWA-2). - Una instancia particular del modelo multijerárquico ha sido estudiada e implementada para organizar información simbólica con el objetivo de mejorar simultáneamente diferentes tareas a realizar por un robot móvil. - Se ha desarrollado un procedimiento que (1) construye un modelo jerárquico del entorno de un robot, (2) lo mantiene coherente y actualizado y (3) lo optimiza con el fin de mejorar las tareas realizadas por un robot móvil. - Finalmente, se ha implementado una arquitectura robótica que engloba todas las cuestiones anteriormente citadas. Se han realizado pruebas reales con una silla de ruedas robotizada que ponen de manifiesto la utilidad del uso de estructuras multijerárquicas en robótica móvil

The use of brain-computer interfacing for ambient intelligence

This paper is aimed to introduce IDIAP Brain Computer Interface (IBCI) research that successfully applied Ambience Intelligence (AmI) principles in designing intelligent brain-machine interactions. We proceed through IBCI applications describing how machines can decode and react to the human mental commands, cognitive and emotive states. We show how effective human-machine interaction for brain computer interfacing (BCI) can be achieved through, 1) asynchronous and spontaneous BCI, 2) shared control between the human and machine, 3) online learning and 4) the use of cognitive state recognition. Identifying common principles in BCI research and ambiance intelligence (AmI) research, we discuss IBCI applications. With the current studies on recognition of human cognitive states, we argue for the possibility of designing empathic environments or devices that have a better human like understanding directly from brain signals