Decision support systems gain better performance and higher accuracy by the virtue of building multiresolutional (multigranular, multiscale) 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 demonstrate that planning and learning are joint processes. The author's intention is to emphasize that the concepts of multiresolutional representation (MR) and multiresolutional decision support (MR-DSS) probably have in common a general significance that crosses the boundaries of particular domains of applications and disciplines. The paper explores this phenomenon. The ubiquity of a principle that somehow persistently delivers benefits to many areas of knowledge and technology seems to be more important than a habit to follow the pigeonhole principle of paper presentation.

The controller for an intelligent mobile autonomous system (IMAS), equipped with vision and low-level sensors to cope with unknown obstacles, is modeled as a hierarchy of decision-making for planning and control. One of the levels (pilot) deals with a distorted 'windshield' view of the world and provides the actuator controller with real-time decisions. This level of IMAS controller is treated as a linguistic controller with fuzzy variables that assume values from possible intervals. The decision-making process at this level of control are presented as a production system with a fuzzy database. The rules in the production system are derived from an analytical system model for minimum-time control. The choice of optimal motion execution commands is performed using fuzzy set operators. Also included is a temporal decision-making mechanism (reporter), which recognizes the persisting conflicts between successive levels of the hierarchy by observing the motion trajectory.>

A real-time system for the control of an autonomous vehicle consisting of a nested hierarchy of control modules is described. The focus is on the PILOT module which provdies the real-time guidance of the system. It is responsible for the generation and tracking of dynamically feasible trajectories which follow the generally planned path and avoid local obstacles. The PILOT operation is based on a combination of simple rules and heuristic search. The state space consists of two parts: the lower level state space which represents the model of the world as a set of relationships among the units of vehicle state variables representing steering, acceleration, and velocity, as well as positions of obstacles, and the upper level state space where the relationships among generalized objects are represented as rules. Rules provide simple elementary maneuvers, and are applied when the upper level situation is easily recognized. Otherwise the situations are handled by the lower level search. Example runs of the vehicle are simulated to demonstrate the complexity of behavior. The algorithm has also been tested on a gas powered dune buggy developed at Drexel University.

An algorithm NAVIGATOR for robot path planning in a 2D world with polygonal obstacles is presented. The method employs A* search in a subset of the visibility graph of obstacle vertices. A procedure is given for finding this subset of the visibility graph without computing line intersections. The NAVIGATOR module is part of a complete hierarchical system for control and world representation for a robot which operates in an unkown and unstructured environment.

In a 2-1/D world an isolines-based world representation is employed. An algorithm of navigation is proposed based upon polygonization of the isolines, and use of the vertices of the polygon as nodes in the graph search. Quanitative recommendations are given concerning the required density of isolines and the error of polygonization. When a physical model of mechanical motion is applied, this algorithm of navigation provides minimum-time trajectories of motion. The results of navigation are illustrated using a simulation system developed for an Intelligent Mobile Autonomous System (unmanned robot).

An Intelligent Mobile Autonomous System (IMAS), which is equipped with vision and low level sensors to cope with unknown obstacles, is modeled as a hierarchy of decision making for motion planning and control. The world description is based on linguistic variables that assume values from possible interval corresponds to the membership grade to a fuzzy set. The decision making is presented as a production system with a fuzzy database. The choice of optimal motion execution commands are performed using fuzzy set operators. Also included in the paper is the procedure of rule derivation and examples based on low level motion control.

A new algorithm of minimum time motion planning is proposed for robots operating in the obstacle strewn environment. Structure of the topological passageways is analyzed and represented using a model of slalom situations for which a number of rules is determined. Dynamical system of robot is described in a form of sequential machine. This enabled a merger between two kindred algorithms: A* search algorithm, and dynamic programming. An experimental analysis of the simulated mobile robot has confirmed the applicability of results.

This paper represents part of the ongoing research into an Intelligent Mobile Autonomous System (IMAS). An intermediate path planning subsystem (Navigator), for the hierarchy of IMAS, is presented which operates in completely known, partially known, and completely unknown environments. Information is passed from a sensor to the Navigator via the "Cartographer" which performs map updating. The interaction and performance of these subsystems are demonstrated in a simulation of the IMAS hierarchy.

A structure of intelligence is discussed and chosen as a conceptual background in a joint effort of a number of research teams working under supervision of the Belvoir Research and Development (R&D) Center. The structure is described as a multi-level hierarchy of production systems united by a single knowledge database. Some of the particular solutions for the components of this structure prove its generic character and trace a plan for the future research effort.

This study describes the theoretical and practical aspects of the design and computer simulation of a heuristic based navigation algorithm. An algorithm is developed which provides a convenient testing system for generalized navigation strategies on a fixed map which may be known or unknown to a system. A variety of maps are simulated and the navigation results are compared.

At the stage of conceptual design, methods of theory formation are shown to be applicable. A process of solution synthesis is described based upon a system of representation relevant to the engineering practice. A procedure for computer aided design is proposed based upon the relationship database given at the beginning of the design process. This procedure is applied to conceptual design in two broad fields of robotics: multilink manipulators, and mobile robots. Software documentation and description of the interactive design procedure are supplemented by illustrative examples.