Multiresolutional models of uncertainty generation and reduction

Kolmogorov's axiomatic principles of the probability theory, are reconsidered in the scope of their applicability to the processes of knowledge acquisition and interpretation. The model of uncertainty generation is modified in order to reflect the reality of engineering problems, particularly in the area of intelligent control. This model implies algorithms of learning which are organized in three groups which reflect the degree of conceptualization of the knowledge the system is dealing with. It is essential that these algorithms are motivated by and consistent with the multiresolutional model of knowledge representation which is reflected in the structure of models and the algorithms of learning

Techniques and potential capabilities of multi-resolutional information (knowledge) processing

A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions

A genetic technique for planning a control sequence to navigate the state space with a quasi-minimum-cost output trajectory for a non-linear multi-dimnensional system

There are many multi-stage optimization problems that are not easily solved through any known direct method when the stages are coupled. For instance, we have investigated the problem of planning a vehicle's control sequence to negotiate obstacles and reach a goal in minimum time. The vehicle has a known mass, and the controlling forces have finite limits. We have developed a technique that finds admissible control trajectories which tend to minimize the vehicle's transit time through the obstacle field. The immediate applications is that of a space robot which must rapidly traverse around 2-or-3 dimensional structures via application of a rotating thruster or non-rotating on-off for such vehicles is located at the Marshall Space Flight Center in Huntsville Alabama. However, it appears that the development method is applicable to a general set of optimization problems in which the cost function and the multi-dimensional multi-state system can be any nonlinear functions, which are continuous in the operating regions. Other applications included the planning of optimal navigation pathways through a transversability graph; the planning of control input for under-water maneuvering vehicles which have complex control state-space relationships; the planning of control sequences for milling and manufacturing robots; the planning of control and trajectories for automated delivery vehicles; and the optimization and athletic training in slalom sports

Autonomous mobile robots : vehicles with cognitive control

xix, 580 p. : ill. ; 23 cm

Autonomous mobile robots : vehicles with cognitive control

xix, 580 p. : ill. ; 23 cm

1 A Sketch of Multiresolutional Decision Support Systems Theory

Multiresolutional Decision Support Systems gain better performance and higher accuracy by the virtue of building highly efficient multiresolutional representation and employing multiscale Behavior Generation Subsystem (Planning and Control). The latter are equipped by devices for unsupervised learning that adjust their functioning to the results of self-identification. We show planning and learning to be joint processes. Keyswords: behavior generation, control, decision support, generalization, knowledge, learning, instantiation, multiresolutional, multiscale, planning, randomized, representation, resolution, searc

Tessellating and Searching Uncertain State Spaces

Multiresolutional S 3-search generated a need to properly tessellate spaces and efficiently searching them. Drexel University has introduced MR-methodology such as uniform and non-uniform space tessellation and efficient algorithms of searching within ressellated state space. This methodology for solving planning and control problems is successfully applied in autonomous vehicles, industrial robots and power stations. This paper focuses on computational phenomena characteristic for randomized tessellation and affecting the results of S 3-search