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Showing papers on "Autonomous system (mathematics) published in 1985"


Proceedings ArticleDOI
E. Koch1, C. Yeh1, G. Hillel, A. Meystel2, C. Isik1 
25 Mar 1985
TL;DR: An intermediate path planning subsystem (Navigator), for the hierarchy of IMAS, is presented which operates in completely known, partially known, and completely unknown environments.
Abstract: 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.

45 citations


01 Jan 1985
TL;DR: In this paper, 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 path planning and motion control.
Abstract: 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 path planning and motion control. This dissertation concentrates on the lower level of this hierarchy (Pilot) with a knowledge-based controller. The basis of a theory of knowledge-based controllers is established, using the example of the Pilot level motion control of IMAS. In this context, the knowledge-based controller with a linguistic world concept is shown to be adequate for the minimum time control of an autonomous mobile robot motion. The Pilot level motion control of IMAS is approached in the framework of production systems. The three major components of the knowledge-based control that are included here are the hierarchies of the database, the rule base and the rule evaluator. The database, which is the representation of the state of the world, is organized as a semantic network, using a concept of minimal admissible vocabulary. The hierarchy of rule base is derived from the analytical formulation of minimum-time control of IMAS motion. The procedure introduced for rule derivation, which is called analytical model verbalization, utilizes the concept of causalities to describe the system behavior. A realistic analytical system model is developed and the minimum-time motion control in an obstacle strewn environment is decomposed to a hierarchy of motion planning and control. The conditions for the validity of the hierarchical problem decomposition are established, and the consistency of operation is maintained by detecting the long term conflicting decisions of the levels of the hierarchy. The imprecision in the world description is modeled using the theory of fuzzy sets. The method developed for the choice of the rule that prescribes the minimum-time motion control among the redundant set of applicable rules is explained and the usage of fuzzy set operators is justified. Also included in the dissertation are the description of the computer simulation of Pilot within the hierarchy of IMAS control and the simulated experiments that demonstrate the theoretical work.

8 citations


01 Jan 1985
TL;DR: In this context, the knowledge-based controller with a linguistic world concept is shown to be adequate for the minimum time control of an autonomous mobile robot motion.
Abstract: 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 path planning and motion control. This dissertation concentrates on the lower level of this hierarchy (Pilot) with a knowledge-based controller. The basis of a theory of knowledge-based controllers is established, using the example of the Pilot level motion control of IMAS. In this context, the knowledge-based controller with a linguistic world concept is shown to be adequate for the minimum time control of an autonomous mobile robot motion. The Pilot level motion control of IMAS is approached in the framework of production systems. The three major components of the knowledge-based control that are included here are the hierarchies of the database, the rule base and the rule evaluator. The database, which is the representation of the state of the world, is organized as a semantic network, using a concept of minimal admissible vocabulary. The hierarchy of rule base is derived from the analytical formulation of minimum-time control of IMAS motion. The procedure introduced for rule derivation, which is called analytical model verbalization, utilizes the concept of causalities to describe the system behavior. A realistic analytical system model is developed and the minimum-time motion control in an obstacle strewn environment is decomposed to a hierarchy of motion planning and control. The conditions for the validity of the hierarchical problem decomposition are established, and the consistency of operation is maintained by detecting the long term conflicting decisions of the levels of the hierarchy. The imprecision in the world description is modeled using the theory of fuzzy sets. The method developed for the choice of the rule that prescribes the minimum-time motion control among the redundant set of applicable rules is explained and the usage of fuzzy set operators is justified. Also included in the dissertation are the description of the computer simulation of Pilot within the hierarchy of IMAS control and the simulated experiments that demonstrate the theoretical work.

4 citations


Proceedings ArticleDOI
19 Dec 1985
TL;DR: This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system that consists of five integrated processing stages.
Abstract: The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system The terrain analysis system consists of five integrated processing stages Each process is discussed in detail and supported by a number of mission oriented examples Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3] For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force Civilian applications for such a device entail the investigation of hazardous areas (eg nuclear reactors and toxic waste dumps), search over large areas (eg downed aircraft or lost campers) and monitoring of large facilities (eg robot security police) As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter programSystem OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate

2 citations