Exploratory Study on Navigation System for Visually Impaired Person

Direction is arguably the most pressing concern for visually impaired person. Nevertheless, we lack a convenient navigation system to guide a visually impaired person using point to point direction to reach desired destination independently while walking on tactile paving. Accessibility of this navigation system must be convenient and simple for visually impaired people. In order to provide an efficient and user friendly assistive tool, it is proposed to design and develop a navigation system using Radio Frequency Identification (RFID) to guide the visually impaired walking on tactile paving. Path planning algorithm will be implemented in this project to give the shortest path and direction as a navigation guide for visually impaired people. This project will directly contribute to society by making available a convenient navigation system for visually impaired people for a better lifestyle

Navigation of mobile robots using artificial intelligence technique.

The ability to acquire a representation of the spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. This document presents a computer vision method and related algorithms for the navigation of a robot in a static environment. Our environment is a simple white colored area with black obstacles and robot (with some identification mark-a circle and a rectangle of orange color which helps in giving it a direction) present over it. This environment is grabbed in a camera which sends image to the desktop using data cable. The image is then converted to the binary format from jpeg format using software which is then processed in the computer using MATLAB. The data acquired from the program is then used as an input for another program which controls the robot drive motors using wireless controls. Robot then tries to reach its destination avoiding obstacles in its path. The algorithm presented in this paper uses the distance transform methodology to generate paths for the robot to execute. This paper describes an algorithm for approximately finding the fastest route for a vehicle to travel one point to a destination point in a digital plain map, avoiding obstacles along the way. In our experimental setup the camera used is a SONY HANDYCAM. This camera grabs the image and specifies the location of the robot (starting point) in the plain and its destination point. The destination point used in our experimental setup is a table tennis ball, but it can be any other entity like a single person, a combat unit or a vehicle

Dynamic path planning of initially unknown environments using an RGB-D camera

In this thesis an RGB-D camera was used with the goal to perform dynamic path planning in an initially unknown environment. Depth data from an RGB-D camera together with a discretizising algorithm is continuously used for maintaining an obstacle map of the environment which within the path planning algorithm D* Lite [S. Koening, 2005] is performed on the flight. Experiments were conducted on two different systems, on Combine’s hexacopter and on a Gantry Tau robot at the Robot Lab of the Department of Automatic Control, LTH. On Combine’s hexacopter different tracking algorithms such as ICP, Translation Approximation and SDF where evaluated for 3D positioning while the robots internal positioning where used on the Gantry Tau robot. For discretization purposes we compare the use of Box Approximation and Signed Distance Function (SDF) for creating the obstacle map

Analysis of Trajectories by Preserving Structural Information

The analysis of trajectories from traffic data is an established and yet fast growing area of research in the related fields of Geo-analytics and Geographic Information Systems (GIS). It has a broad range of applications that impact lives of millions of people, e.g., in urban planning, transportation and navigation systems and localized search methods. Most of these applications share some underlying basic tasks which are related to matching, clustering and classification of trajectories. And, these tasks in turn share some underlying problems, i.e., dealing with the noisy and variable length spatio-temporal sequences in the wild. In our view, these problems can be handled in a better manner by exploiting the spatio-temporal relationships (or structural information) in sampled trajectory points that remain considerably unharmed during the measurement process. Although, the usage of such structural information has allowed breakthroughs in other fields related to the analysis of complex data sets [18], surprisingly, there is no existing approach in trajectory analysis that looks at this structural information in a unified way across multiple tasks. In this thesis, we build upon these observations and give a unified treatment of structural information in order to improve trajectory analysis tasks. This treatment explores for the first time that sequences, graphs, and kernels are common to machine learning and geo-analytics. This common language allows to pool the corresponding methods and knowledge to help solving the challenges raised by the ever growing amount of movement data by developing new analysis models and methods. This is illustrated in several ways. For example, we introduce new problem settings, distance functions and a visualization scheme in the area of trajectory analysis. We also connect the broad fild of kernel methods to the analysis of trajectories, and, we strengthen and revisit the link between biological sequence methods and analysis of trajectories. Finally, the results of our experiments show that - by incorporating the structural information - our methods improve over state-of-the-art in the focused tasks, i.e., map matching, clustering and traffic event detection