Reusable Software Components for Robots Using Fuzzy Abstractions

Mobile robots today, while varying greatly in design, often have a large number of similarities in terms of their tasks and goals. Navigation, obstacle avoidance, and vision are all examples. In turn, robots of similar design, but with varying configurations, should be able to share the bulk of their controlling software. Any changes required should be minimal and ideally only to specify new hardware configurations. However, it is difficult to achieve such flexibility, mainly due to the enormous variety of robot hardware available and the huge number of possible configurations. Monolithic controllers that can handle such variety are impossible to build. This paper will investigate these portability problems, as well as techniques to manage common abstractions for user-designed components. The challenge is in creating new methods for robot software to support a diverse variety of robots, while also being easily upgraded and extended. These methods can then provide new ways to support the operational and functional reuse of the same high-level components across a variety of robots

Platform Relative Sensor Abstractions across Mobile Robots using Computer Vision and Sensor Integration

Uniform sensor management and abstraction across different robot platforms is a difficult task due to the sheer diversity of sensing devices. However, because these sensors can be grouped into categories that in essence provide the same information, we can capture their similarities and create abstractions. An example would be distance data measured by an assortment of range sensors, or alternatively extracted from a camera using image processing. This paper describes how using software components it is possible to uniformly construct high-level abstractions of sensor information across various robots in a way to support the portability of common code that uses these abstractions (e.g. obstacle avoidance, wall following). We demonstrate our abstractions on a number of robots using different configurations of range sensors and cameras

Software Reuse across Robotic Platforms: Limiting the effects of diversity

Robots have diverse capabilities and complex interactions with their environment. Software development for robotic platforms is time consuming due to the complex nature of the tasks to be performed. Such an environment demands sound software engineering practices to produce high quality software. However software engineering in the robotics domain fails to facilitate any significant level of software reuse or portability. This paper identifies the major issues limiting software reuse in the robotics domain. Lack of standardisation, diversity of robotic platforms, and the subtle effects of environmental interaction all contribute to this problem. It is then shown that software components, fuzzy logic, and related techniques can be used together to address this problem. While complete software reuse is not possible, it is demonstrated that significant levels of software reuse can be obtained. Without an acceptable level of reuse or portability, software engineering in the robotics domain will not be able to meet the demands of a rapidly developing field. The work presented in this paper demonstrates a method for supporting software reuse across robotic platforms and hence facilitating improved software engineering practices

Image Searching Tool Using Category Based Indexing

The aim of this research is to propose a new content based image retrieval (CBIR) system using categories. Different images have different characteristics and thus often require different image processing techniques. Most current CBIR systems operate on all images, without pre-sorting images into different categories. This results in limitations on retrieval performance and accuracy. Two semantic and four syntactic image categories are proposed. The category for an image is generated automatically by analysing the image for the presence of a dominant object or for correspondence to an image ‘template’. Dominant objects are obtained by performing region grouping of segmented thumbnails. The result of this research is a new Internet image retrieval and indexing system

Content Based Image Retrieval Using Category-Based Indexing

Currently, most content based image retrieval (CBIR) systems operate on all images, without sorting images into different types or categories. Different images have different characteristics and thus often require different analysis techniques and query types. Additionally, placing an image into a category can help the user to navigate retrieval results more effectively. To categorise an image, firstly the dominant region needs to be extracted using multi level colour segmentation. Based on the regions’ features of colour, texture, shape and relation between regions, the image is then categorised. Users are presented with retrieval results sorted into different categories, where dominant region extraction will allow for object based retrieval to be performed

Perceptual grouping of natural images for CBIR

In current developments of CBIR system, it is desirable to design a computer program that can index the content of an image automatically into a set of perceptually significant components. Results from existing image segmentation techniques are not sufficient to represent the content of an image. Further grouping is required to produce more meaningful segmentation. This paper describes our approach to implement Gestalt principles for region grouping. Results from image segmentation are further grouped into regions representing major components of image content

Using high level information for region grouping

Effective labeling for an image indexing system requires all objects in the image to be identified. This identification process can be performed by extracting components of the objects and grouping these components together. We propose the use of image segmentation techniques as a first step to solve the problem of extracting these components automatically. The difficult task is how the grouping of these components is performed. This paper presents an approach in region grouping using high level information. This information permits image segments grouped into “more meamngfC regions. In this paper, we present the issues and problems involved in region grouping. Some experiment results will be presented

Automatic Image Structure Analysis

The rapid growth of multimedia technology has resulted in an enormous amount of data that needs to be managed and indexed efficiently to provide effective labeling for an image indexing system requires all objects in the image to be identified. To perform better and effective object identification, the process needs to be performed automatically and without priori knowledge of the image content. This paper presents an approach in automatic object identification scheme, by analysing the image structural information. The image is segmented using image automatic segmentation techniques and components of objects are obtained by grouping the segments together. In this paper, we present the issues and problems involved in providing such identification scheme. Some experiment results will be presented

Robot Simulation for Education

This paper describes the work-in-progress of creating an artificial 3D environment and robot, suitable for educational simulation. A 3D vehicle robot, equipped with a monocular camera navigates in a physics based 3D environment, with some artificial intelligence capabilities. Students can interact with the robot, add new objects and set the robot for some tasks. This multimedia tool is designed for student with very little experience with robotics

Diagram recognition using hidden Markov models

User input interfaces are quickly become more natural and intuative, relying less and less on the traditional mouse and keyboard interface, and moving towards a pen based input system. Current technologies which take advancement of these developments in hardware have been based primarily on handwriting recognition to allow the user to write there instructions instead of typing. Some work has been performed regarding diagram recognition from on-line input, however these system have been developed using hardcoded parameters with fuzzy sets and common feature sets. Hidden Markov Models are a powerful mathematical recognition tool, which is current being used in feilds such as speech recognition, handwriting recognition and DNA cell searching and classification applications. This is the first attempt at using a hidden markov model to recognize input from a two dimensonal spacial environment, the result of the initial implementation have shown promising results for more complex recognition models