NDLI: All-weather and all-terrain intelligent vehicle for law enforcement applications

Content Provider	IEEE Xplore Digital Library
Author	Fujalkowski, B.
Copyright Year	1994
Description	Author affiliation: Cracow Univ. of Technol., Krakow, Poland (Fujalkowski, B.)
Abstract	The author selected neuro-fuzzy reasoning based motion control that is propulsion, dispulsion, suspension and conversion control algorithms to the mechatronically controlled intelligent vehicle. Fuzzy logic was chosen because of the following characteristics. It is easier to implement driver skills using neuro-fuzzy reasoning based motion control than using conventional motion control algorithms. It filters even the uncertainty of the precision of the sensors. Therefore, the intelligent vehicle may move as smoothly as a well-skilled or experienced human being along straight and curved routes on the hostile location or battlefield. Motion control for an intelligent vehicle requires many of the same elements that the military command, control communication and intelligence (C/sup 3/I) systems use. The all-weather and all-terrain intelligent vehicle will have two types of moving mode, IR vision-camera mode and US sonar mode, originating from the differing methods used to detect the intelligent vehicle position.
Starting Page	509
Ending Page	514
File Size	667488
Page Count	6
File Format	PDF
ISBN	0780321359
DOI	10.1109/IVS.1994.639570
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-10-24
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent vehicles Law enforcement Motion control Intelligent sensors Sonar detection Propulsion Fuzzy logic Filters Uncertainty Sensor phenomena and characterization
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Sensor offset compensation for a vehicle yaw rate sensor using fuzzy logic

A Hybrid Method in Driver and Multisensor Data Fusion, Using a Fuzzy Logic Supervisor for Vehicle Intelligence

Autonomous driving in a time-varying environment

A tracking sonar sensor for vehicle guidance

Error correction using fuzzy logic in vehicle load measurement

Proper selection of sonar and visual sensors for vehicle detection and avoidance

An intelligent vehicle based on “follow-up” infrared sensors

Probabilistic representation of the uncertainty of stereo-vision and application to obstacle detection

Intelligent fuzzy motion control of mobile robot for service use

All-weather and all-terrain intelligent vehicle for law enforcement applications

Similar Documents

Sensor offset compensation for a vehicle yaw rate sensor using fuzzy logic

A Hybrid Method in Driver and Multisensor Data Fusion, Using a Fuzzy Logic Supervisor for Vehicle Intelligence

Autonomous driving in a time-varying environment

A tracking sonar sensor for vehicle guidance

Error correction using fuzzy logic in vehicle load measurement

Proper selection of sonar and visual sensors for vehicle detection and avoidance

An intelligent vehicle based on “follow-up” infrared sensors

Probabilistic representation of the uncertainty of stereo-vision and application to obstacle detection

Intelligent fuzzy motion control of mobile robot for service use

All-weather and all-terrain intelligent vehicle for law enforcement applications