Highly efficient Localisation utilising Weightless neural systems

Efficient localisation is a highly desirable property for an autonomous navigation system. Weightless neural networks offer a real-time approach to robotics applications by reducing hardware and software requirements for pattern recognition techniques. Such networks offer the potential for objects, structures, routes and locations to be easily identified and maps constructed from fused limited sensor data as information becomes available. We show that in the absence of concise and complex information, localisation can be obtained using simple algorithms from data with inherent uncertainties using a combination of Genetic Algorithm techniques applied to a Weightless Neural Architecture

Mobile robot localization using robust extended H-infinity filtering

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 Institution of Mechanical Engineers.In this paper, a novel methodology is provided for accurate localization of a mobile robot using autonomous navigation based on internal and external sensors. A new robust extended H∞ filter is developed to deal with the non-linear kinematic model of the robot and the non-linear distance measurements, together with process and measurement noises. The proposed filter relies on a two-step prediction-correction structure, which is similar to a Kalman filter. Simulations are provided to demonstrate the effectiveness of the proposed method.EPSRC, the Nuffield Foundation, and the Alexander von Humboldt Foundation

MOBILE ROBOT SELF-PLANNING AND NAVIGATION BASED ON ARTIFICIAL LANDMARK LOCALIZATION METHOD AND BINOCULAR STEREO VISION

This paper describes design of artificial landmark based on colour model used for Self-planning in unstructured environment to a robot for its movement. This method provides less error in estimation when compared to existing methods. This project is an investigation into building a system which visually detects artificial landmarks to determine the landmarks within a location, decipher their position within that location and track the landmark throughout the location using Binocular stereo vision

Position and orientation errors in mobile robot absolute self-localization using an improved version of the generalized geometric triangulation algorithm

Triangulation with active beacons is widely used in the absolute localization of mobile robots. The original Generalized Geometric Triangulation algorithm suffers only from the restrictions that are common to all algorithms that perform self-localization through triangulation. But it is unable to compute position and orientation when the robot is over the segment of the line that goes by beacons 1 and 2 whose origin is beacon 1 and does not contain beacon 2. An improved version of the algorithm allows self-localization even when the robot is over that line segment. Simulations results suggest that a robot is able to localize itself, with small position and orientation errors, over a wide region of the plane, if measurement uncertainty is small enough

Robust Invariants From Functionally Constrained Motion

We address in the problem of control-based recovery of robot pose and the environmental lay-out. Panoramic sensors provide us with an 1D projection of characteristic features of a 2D operation map. Trajectories of these projections contain the information about the position of a priori unknown landmarks in the environment. We introduce here the notion of spatiotemporal signatures of projection trajectories. These signatures are global measures, like area, characterized by considerably higher robustness with respect to noise and outliers than the commonly applied point correspondence. By modeling the 2D motion plane as the complex plane we show that by means of complex analysis our method can be embedded in the well-known affine reconstruction paradigm

Generalized geometric triangulation algorithm for mobile robot absolute self-localization

Triangulation with active beacons is widely used in the absolute localization of mobile robots. The Geometric Triangulation algorithm allows the self- localization of a robot on a plane. However, the three beacons it uses must be “properly ordered” and the algorithm works consistently only when the robot is within the triangle formed by these beacons. This paper presents an improved version of the algorithm, which does not require beacon ordering and works over the whole navigation plane except for a few well-determined lines where localization is not possible

Development and localization of a mobile robot

An autonomous mobile robot needs to have the capability to locate itself within an environment, besides the ability to avoid any obstacles during its movement. This paper covers the development of an autonomous mobile robot. It was equipped with two ultrasonic sensors for obstacle avoidance purposes. The mobile robot was tested within a static environment. The results show that the robot is capable of navigating itself in the defined environment without being provided with a prior map. In summary, the mobile robot was successfully developed, and its localization capability was achieved