Autonomous Navigation, Exploration, and Recognition Using the HERMIES-IIB Robot

Burks, B.L.; Saussure, G. de; Weisbin, C.R.; Jones, J.P.; Hamel, W.R.

doi:10.1109/mex.1987.5006527

Cited by 44 publications

(6 citation statements)

References 11 publications

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“…As such an approach, there have been studies on creating expert systems by obtaining judgments on situations and navigational methods as some form of database, and carrying out navigation appropriate to various situations, which have achieved success [4][5][6][7][8][9][10]. This is considered a right direction in view of the complexity of the real world in which robots run.…”

Section: Introductionmentioning

confidence: 99%

“…It must be noted, however, that an expert system can be either top-down or bottom-up. The former systems involve analysis of tasks which a robot constructed mainly as a system is capable of and step-by-step arrangement of those tasks [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Some examples of these are a surveillance robot [4] and a maintenance robot of a nuclear plant [5] whose actions are created by means of hierarchical architecture to construct a series of actions.…”

Section: Introductionmentioning

confidence: 99%

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A method of autonomous mobile robot navigation by using fuzzy control

Ishikawa

1994

Advanced Robotics

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A method of autonomous mobile robot navigation by using fuzzy control SHIGEKI ISHIKAWA Abstract-It is necessary for an autonomous mobile robot (AMR) to describe schemes of monitoring the status and procedures of suitable processes. This paper presents a sensor-based navigation method using fuzzy control, of which the purpose is to construct an expert knowledge for efficient and better piloting of AMR. This method provides a function of tracing a planned path by sensing distances of an AMR and its difference angle from the planned path, and a function of avoiding stationary and moving obstacles by sensing free area distances ahead of the AMR. Fuzzy control is also used to select suitable rules (tracing a path/avoiding obstacles) according to a situation, which is derived from sensor information by using fuzzy control. The effectiveness of established rules and the effect of fuzzy control on AMR navigation is discussed through simulations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A method of autonomous mobile robot navigation by using fuzzy control

Ishikawa

1994

Advanced Robotics

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“…[2][3][4] Designed by Transitions Research Corporation in 1984 and first implemented in a hospital facility in 1988, 4 LabMate demonstrated benefits in food service, 5-7 nursing, 5 pharmacy, 6,8 laundry, 6,7 and mailroom 6 applications, as well as with the distribution of other supplies. 6,7,9 In 1997, Transitions Research Corporation went public, changed its name to HelpMate Robotics Inc., and introduced a descendant robot of Am J Health-Syst Pharm-Vol 66 Oct 1, 2009 LabMate-HelpMate-to the marketplace.…”

mentioning

confidence: 99%

Determining the feasibility of robotic courier medication delivery in a hospital setting

Kirschling

Rough

Ludwig

2009

American Journal of Health-System Pharmacy

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“…In robot control, for example [Burks87], sensor data acquisition (implemented in C), and image pmessing (implemented in Fortran) need to be integrated with diagnostic (implemented in OPS-5) and planning schemes (implemented in LISP). Likewise, in a real-time Speech Recognition System Woung881, the Word Matcher and Sentence Parser (implemented in C) need to be integrated with the Word Verifier and Word Displayer (implemented in LISP).…”

Section: Introductionmentioning

confidence: 99%

An efficient data interface for heterogeneous distributed environments

Lin¹,

Shirazi²,

Kavi³

[1992] Proceedings of the 12th International Conference on Distributed Computing Systems

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The development of heterogeneous distributed environments has provided opportunities for the development of complex applications requiring interactions among software modules written in different languages. This necessitates a data interface to allow transmission of suitable data types and data structures among different languages. This paper first introduces a novel multi-language based data interface system for heterogeneous distributed processing, then covers a prototyped environment based on this system, and finally presents an evaluation of the prototyped system. The focus of the paper is on the semantics of data structure transmission. By separating the primitive data values from the structure information of a data structure (descriptors), it is possible to perform the data type checking and data conversion operations in pipelined fashion. In addition, the re-generation of the structure information can be avoided by caching such data when they are generated for the fmt time.The current prototype environment supports C and Pascal languages. However, the model presented here can be extended to other languages as well.

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Autonomous Navigation, Exploration, and Recognition Using the HERMIES-IIB Robot

Cited by 44 publications

References 11 publications

A method of autonomous mobile robot navigation by using fuzzy control

A method of autonomous mobile robot navigation by using fuzzy control

Determining the feasibility of robotic courier medication delivery in a hospital setting

An efficient data interface for heterogeneous distributed environments

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