Learned navigation in unknown terrains: A retraction method

The problem of learned navigation of a circular robot R, of radius delta (is greater than or equal to 0), through a terrain whose model is not a-priori known is considered. Two-dimensional finite-sized terrains populated by an unknown (but, finite) number of simple polygonal obstacles are also considered. The number and locations of the vertices of each obstacle are unknown to R. R is equipped with a sensor system that detects all vertices and edges that are visible from its present location. In this context two problems are covered. In the visit problem, the robot is required to visit a sequence of destination points, and in the terrain model acquisition problem, the robot is required to acquire the complete model of the terrain. An algorithmic framework is presented for solving these two problems using a retraction of the freespace onto the Voronoi diagram of the terrain. Algorithms are then presented to solve the visit problem and the terrain model acquisition problem

An Algorithmic Framework for Robot Navigation in Unknown Terrains.

The problem of navigating a robot body through a terrain whose model is a priori known is well-solved problem in many cases. Comparatively, a lesser number of research results have been reported about the navigation problem in unknown terrains i.e., the terrains whose model are not a priori known. The focus of our work is to obtain an algorithmic framework that yields algorithms to solve certain navigational problems in unknown terrains. We consider a finite-sized two-dimensional terrain populated by a finite set of obstacles $O$ = \{O\sb1,O\sb2,\...,O\sb{n}\} where O\sb{i} is a simple polygon with a finite number of vertices. Consider a circular body R, of diameter $\delta\geq$ O, capable of translational and rotational motions. R houses a computational device with storage capability. Additionally, R is equipped with a sensor system capable of detecting all visible vertices and edges. We consider two generic problems of navigation in unknown terrains: the Visit Problem, VP, and the Terrain model acquisition Problem, TP. In the visit problem, R is required to visit a sequence of destination points d\sb1,d\sb2,\...,d\sb{M} in the specified order. In the terrain model acquisition problem, R is required to acquire the model of the terrain so that it can navigate to any destination without using sensors and by using only the path planning algorithms of known terrains. We present a unified algorithmic framework that yields correct algorithms to solve both VP and TP. In this framework, R \u27simulates\u27 a graph exploration algorithm on an incrementally-constructible graph structure, called the navigation course, that satisfies the properties of finiteness, connectivity, terrain-visibility and local-constructibility. Additionally, we incorporate the incidental learning feature in our solution to VP so as to enhance the performance. We consider solutions to VP and TP using navigation courses based two geometric structures, namely the visibility graph and the Voronoi diagram. In all the cases, we analyze the performance of the algorithms for VP and TP in terms of the number of scan operations, the distance traversed and the computational complexity

Knowledge/geometry-based Mobile Autonomous Robot Simulator (KMARS)

Ongoing applied research is focused on developing guidance system for robot vehicles. Problems facing the basic research needed to support this development (e.g., scene understanding, real-time vision processing, etc.) are major impediments to progress. Due to the complexity and the unpredictable nature of a vehicle's area of operation, more advanced vehicle control systems must be able to learn about obstacles within the range of its sensor(s). A better understanding of the basic exploration process is needed to provide critical support to developers of both sensor systems and intelligent control systems which can be used in a wide spectrum of autonomous vehicles. Elcee Computek, Inc. has been working under contract to the Flight Dynamics Laboratory, Wright Research and Development Center, Wright-Patterson AFB, Ohio to develop a Knowledge/Geometry-based Mobile Autonomous Robot Simulator (KMARS). KMARS has two parts: a geometry base and a knowledge base. The knowledge base part of the system employs the expert-system shell CLIPS ('C' Language Integrated Production System) and necessary rules that control both the vehicle's use of an obstacle detecting sensor and the overall exploration process. The initial phase project has focused on the simulation of a point robot vehicle operating in a 2D environment

On fast planning of suboptimal paths amidst polygonal obstacles in plane

AbstractThe problem of planning a path for a point robot from a source point s to a destination point d so as to avoid a set of polygonal obstacles in plane is considered. Using well-known methods, a shortest path from s to d can be computed with a time complexity of O(n2) where n is the total number of obstacle vertices. The focus here is in 1.(a) planning paths faster at the expense of setting for suboptimal path lengths and2.(b) performance analysis of simple and/or well-known suboptimal methods. A method that enables a hierarchical implementation of any path planning algorithm with no increase in the worst-case time complexity, is presented; this implementation enables fast planning of simple paths. Then methods are presented based on the Voronoi diagrams, trapezoidal decomposition and triangulation, which compute (suboptimal) paths in O(n√log n) time with the preprocessing costs of O(n log n), O(n2) and O(n log n), respectively. Using existing navigational algorithms for unknown terrains, algorithms that run in O(n log n) time (after preprocessing) and yield suboptimal paths, are presented. For all these algorithms, upper bounds on the path lengths are estimated in terms of the shortest of the obstacles, etc

How do learners respond to computer based learning material which has been designed to suit their particular learning style

The development of ICT in education has changed the nature of people's learning. The evolution of Computer Based Learning (CBL) to virtual learning has had a huge effect on learning methodology. Learning theories from behaviourism, cognitivism and constructivism have been re-assessed. This study explored students' feedback and experiences when using CBL material which has been adapted to particular learning styles. Studies show that individuals learn in different ways. They have different preferences in collecting, organizing and delivering information. These differences impact on learning outcomes. The framework in this study concerns itself with modal preferences known as the VARK Model. The study focuses on CBL material which has been designed for learning new software. This learning material was designed with four different learning routes to appeal to those with dominant Visual, Aural, Reading and Kinaesthetic preferences respectively. The learning package was called the MINDs learning system. Respondents involved were student teachers in two Universities in the UK and Malaysia. Sixty two respondents agreed to participate interviews and in trialling courseware. Data was collected through questionnaire, survey, interview and observation. Quantitative and qualitative data was analysed descriptively, triangulation of the findings was carried out and conclusions were drawn. Findings from the study show that learning styles instruments measure general preferences rather than offering an indication of the specific context in which learning takes place. Matching learning material with particular learning styles did not significantly increase motivation, comprehension or have a major impact on learning. However, learners are aware of having learning styles and found that learning with suited learning preferences made them feel more comfortable. Recommendations were put forward for future research to design and develop a 'new type' of CBL material which takes into account individual learning preferences

Mobile Robot Online Motion Planning Using Generalized Voronoi Graphs

In this paper, a new online robot motion planner is developed for systematically exploring unknown environ¬ments by intelligent mobile robots in real-time applications. The algorithm takes advantage of sensory data to find an obstacle-free start-to-goal path. It does so by online calculation of the Generalized Voronoi Graph (GVG) of the free space, and utilizing a combination of depth-first and breadth-first searches on the GVG. The planner is equipped with components such as step generation and correction, backtracking, and loop handling. It is fast, simple, complete, and extendable to higher spaces

Istraživanje i modeliranje nepoznatog poligonalnog prostora zasnovano na nesigurnim podacima udaljenosti

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman’s exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot’s pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman’s algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.Razmatramo problem istraživanja i izgradnje karte nepoznatog unutarnjeg prostora koristeći laserski sensor udaljenosti. Pretpostavljamo riješenu lokalizaciju robota i poznati model nesigurnosti senzora. Većna se algoritama istraživanja zasniva na otkrivanju granica istraženog i neistraženog područja. Međutim, u praksi nisu učinkoviti zbog nesigurnosti mjerenja, lokalizacije i izgradnje karte. Razvijen je algoritam istraživanja i izgradnje karte koji proširuje Ekmanov algoritam uklanjanjem strogih ograničenja na senzor udaljenosti i lokalizaciju robota. Razvijeni algoritam uključuje algoritam izdvajanja linijskih segmenata prema Pfisteru, koji uzima u obzir utjecaje zašumljenosti senzora i nesigurnosti položaja mobilnog robota. Linijska reprezentacija podataka udaljenosti koristi se za stvaranje poligona koji predstavlja istraženo područje iz svakog mjernog položaja. Bridovi poligona koji se ne podudaraju sa stvarnim značajkama prostora su kandidati za novi mjerni položaj. Algoritam općenitog isijecanja poligona korišten je za dobivanje ukupnog istraženog područja kao unija poligona iz različitih mjernih položaja. Razvijeni algoritam testiran je i uspoređen s izvornim Ekmanovim algoritmom simulacijski i eksperimentalno na mobilnom robotu Pioneer 3DX opremljenim laserskim senzorom udaljenosti SICK LMS-200

Aspects of the Rover Problem

The basic task of a rover is to move about automonously in an unknown environment. A working rover must have the following three subsystems which interact in various ways: 1) locomotion--the ability to move, 2) perception--the ability to determine the three-dimensional structure of the environment, and 3) navigation--the ability to negotiate the environment. This paper will elucidate the nature of the problem in these areas and survey approaches to solving them while paying attention to real-world issues.MIT Artificial Intelligence Laborator

Earplug

Earplug have been created since a long time ago, the earliest patent earplug was made in 1884. Human tend to use finger to cover their ears to blocking the noise absorb by the ear. It was surprisingly effective but human unable to sustain for a long period of time and while using finger to decrease the volume, human unable to do other work in such condition