Vision-Based Mobile Robot Self-localization and Mapping System for Indoor Environment

Localizing accurately and building map of an environment concurrently is a key factor of a mobile robot system. In this system, the robot makes localization and mapping with artificial landmarks and map-based system. It is a process by which a mobile robot can build a map of an environment while continuously determining the location of the robot within the map. The system estimates the robot position in indoor environments using sensors; a camera, three ultrasonic sensors and encoders. The main contribution of this paper is to reduce computational time and improve mapping with map-based system. The self-localization of mobile robot in an indoor environment is advanced through the construction of map based on sensors and recognition of artificial landmarks. Vision based localization system can benefit from using with ultrasonic sensors. From captured images, the system makes landmark detection by using Canny edge detection and Chain-code Approximation algorithms to represent the contour of landmarks by using edge points. The Kalman filter is aimed to accurately estimate position and orientation of the robot using relative distances to walls or artificial landmarks in environments. A robotic system is capable of mapping in an indoor environment and localizing with respect to the map, in real time, using artificial landmarks and sensors

Development of situation recognition, environment monitoring and patient condition monitoring service modules for hospital robots

An aging society and economic pressure have caused an increase in the patient-to-staff ratio leading to a reduction in healthcare quality. In order to combat the deficiencies in the delivery of patient healthcare, the European Commission in the FP6 scheme approved the financing of a research project for the development of an Intelligent Robot Swarm for Attendance, Recognition, Cleaning and Delivery (iWARD). Each iWARD robot contained a mobile, self-navigating platform and several modules attached to it to perform their specific tasks. As part of the iWARD project, the research described in this thesis is interested to develop hospital robot modules which are able to perform the tasks of surveillance and patient monitoring in a hospital environment for four scenarios: Intruder detection, Patient behavioural analysis, Patient physical condition monitoring, and Environment monitoring. Since the Intruder detection and Patient behavioural analysis scenarios require the same equipment, they can be combined into one common physical module called Situation recognition module. The other two scenarios are to be served by their separate modules: Environment monitoring module and Patient condition monitoring module. The situation recognition module uses non-intrusive machine vision-based concepts. The system includes an RGB video camera and a 3D laser sensor, which monitor the environment in order to detect an intruder, or a patient lying on the floor. The system deals with various image-processing and sensor fusion techniques. The environment monitoring module monitors several parameters of the hospital environment: temperature, humidity and smoke. The patient condition monitoring system remotely measures the following body conditions: body temperature, heart rate, respiratory rate, and others, using sensors attached to the patient’s body. The system algorithm and module software is implemented in C/C++ and uses the OpenCV image analysis and processing library and is successfully tested on Linux (Ubuntu) Platform. The outcome of this research has significant contribution to the robotics application area in the hospital environment