In this thesis, questions of navigation, planning and control of real-world mobile robotic systems are addressed. Chapter II contains the first contribution in this thesis, which is a modification of the canonical two-layer hybrid architecture: deliberative planning on top, with reactive behaviors underneath. Deliberative is used to describe higher-level reasoning that includes experiential memory and regional or global objectives. Alternatively, reactive describes low-level controllers that operate on information spatially and temporally immediate to the robot. In the traditional architecture, information is passed top down, with the deliberative layer dictating to the reactive layer. Chapter II presents our work on introducing feedback in the opposite direction, allowing the behaviors to provide information to the planning module(s). The path planning problem, particularly as it as solved by the visibility graph, is addressed first in Chapter III. Our so-called oriented visibility graph is a combinatorial planner with emphasis on dynamic re-planning in unknown environments at the expensive of guaranteed optimality at all times. An example of single source planning -- where the goal location is known and static -- this approach is compared to related approaches (e.g. the reduced visibility graph). The fourth chapter further develops the work presented in the Chapter III; the oriented visibility graph is extended to the hierarchical oriented visibility graph. This work directly addresses some of the limitations of the oriented visibility graph, particularly the loss of optimality in the case where obstacles are non-convex and where the convex hulls of obstacles overlap. This results in an approach that is a kind of middle-ground between the oriented visibility graph which was designed to handle dynamic updates very fast, and the reduced visibility graph, an old standard in path planning that guarantees optimality. Chapter V investigates path planning at a higher level of abstraction. Given is a weighted colored graph where vertices are assigned a color (or class) that indicates a feature or quality of the environment associated with that vertex. The question is then asked, ``what is the globally optimal path through this weighted colored graph?' We answer this question with a mapping from classes and edge weights to a real number, and use Dijkstra's Algorithm to compute the best path. Correctness is proven and an implementation is highlighted.

In recent years unmanned aircraft systems (UAS) have been successfully used in a wide variety of applications. Their value as surveillance platforms has been proven repeatedly in both military and civilian domains. As substitutes to human inhabited aircraft, they fulfill missions that are dull, dirty and dangerous. Representative examples of successful use of UAS are in areas including battlefield assessment, reconnaissance, port security, wildlife protection, wildfire detection, search and rescue missions, border security, resource exploration and oil spill detection. The reliance of almost every UAS application on the ability to sense, detect, see and avoid from a distance has motivated this thesis, attempting to further investigate this issue. In particular, among the various types of UAS, small scale unmanned rotorcraft or Vertically Take-off and Landing, (VTOL) vehicles have been chosen to serve as the sensor carrier platforms because of their operational flexibility. In this work we address the problem of object identification and tracking in a largely unknown dynamic environment under the additional constraint of real-time operation and limited computational power. In brief, the scope of this thesis can be stated as follows: Design a vision system for a small autonomous helicopter that will be able to: Identify arbitrary objects using a minimal description model and a-priori knowledge; Track objects of interest; Operate in real-time; Operate in a largely unknown, dynamically changing, outdoors environment under the following constraints: Limited processing power and payload; Low cost, off-the-shelf components. The main design directives remain that of real-time execution and low price, high availability components. It is in a sense an investigation for the minimum required hardware and algorithmic complexity to accomplish the desired tasks. After development, the system was evaluated as to its suitability in an array of applications. The ones that were chosen for that purpose were: Detection of semi-concealed objects; Detection of a group of ground robots; Traffic monitoring. Adequate performance was demonstrated in all of the above cases.

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No description available.

This thesis aims at detecting obstacles using a single camera in an unknown 3D world for 3D motion of the robot/UAV. Obstacle detection is a pre-requisite for collision-free motion of robots/UAVs. Most of the research in this area has been for 2D motion of the ground robots and with active sensors e.g Laser range finders, Ultrasonic sensors, SONAR, RADAR etc. The passive camera based research has mostly been done either using triangulation/stereo vision (using more than one camera), or, developing an expectation map pre-hand, of the world and comparing it with the new image data. In contrast, this thesis, aims at finding solution of the problem using just a single camera in a perfectly unknown world. This requirement is based on the fact that at least a single camera would be carried by almost all robots/UAVs anyway in foreseeable future. Hence the attempt is to use the same camera for obstacle detection and avoidance task as well, so as to come up with a low cost and light weight solution, in order to facilitate building miniature robots/UAVs.

Knowing the semantic category of a robot's current position not only facilitates the robot's navigation, but also greatly improves its ability to serve human needs and to interpret the scene. Visual Place Categorization (VPC) is addressed in this dissertation, which refers to the problem of predicting the semantic category of a place using visual information collected from an autonomous robot platform. Census Transform (CT) histogram and Histogram Intersection Kernel (HIK) based visual codebooks are proposed to represent an image. CT histogram encodes the stable spatial structure of an image that reflects the functionality of a location. It is suitable for categorizing places and has shown better performance than commonly used descriptors such as SIFT or Gist in the VPC task. HIK has been shown to work better than the Euclidean distance in classifying histograms. We extend it in an unsupervised manner to generate visual codebooks for the CT histogram descriptor. HIK codebooks help CT histogram to deal with the huge variations in VPC and improve system accuracy. A computational method is also proposed to generate HIK codebooks in an efficient way. The first significant VPC dataset in home environments is collected and is made publicly available, which is also used to evaluate the VPC system based on the proposed techniques. The VPC system achieves promising results for this challenging problem, especially for important categories such as bedroom, bathroom, and kitchen. The proposed techniques achieved higher accuracies than competing descriptors and visual codebook generation methods.

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Vela, Patricio; Committee Member: Collins, Thomas; Committee Member: Howard, Ayanna

Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 82-89). Also available in electronic format on the Internet.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2007. / Includes bibliographical references (p. 81-84).

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Includes bibliographical references (leaves 98-105). Also available online (PDF file) by a subscription to the set or by purchasing the individual file.