Agent-Based Control for Fuzzy Behavior Programming in Robotic Excavation

Wang, Fei‐Yue

doi:10.1109/tfuzz.2004.832522

Cited by 23 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(a) CMV generation method in (19). (b) CMV generation method in (19) with clipper in (22). (c) Proposed method in (23).…”

Section: Resultsmentioning

confidence: 99%

“…ITS research involves four major issues: increasing the capacity of highways, improving safety, reducing fuel consumption, and reducing pollution. ITS can use some intelligent control strategies, such as agent-based control concepts [22], [23], to manage the transportation and traffic problems. In machine vision aspect, it can be used to detect land markings, vehicles, pedestrians, road signs, traffic conditions, traffic incidents, and even driver drowsiness or to assist the driver to get more information and reduce driving accidents.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Robust In-Car Digital Image Stabilization Technique

Hsu

Liang

Fan

et al. 2007

IEEE Trans. Syst., Man, Cybern. C

View full text Add to dashboard Cite

Machine vision is a key technology used in an intelligent transportation system (ITS) to augment human drivers' visual capabilities. For the in-car applications, additional motion components are usually induced by disturbances such as the bumpy ride of the vehicle or the steering effect, and they will affect the image interpretation processes that is required by the motion field (motion vector) detection in the image. In this paper, a novel robust in-car digital image stabilization (DIS) technique is proposed to stably remove the unwanted shaking phenomena in the image sequences captured by in-car video cameras without the influence caused by moving object (front vehicles) in the image or intentional motion of the car, etc. In the motion estimation process, the representative point matching (RPM) module combined with the inverse triangle method is used to determine and extract reliable motion vectors in plain images that lack features or contain a large low-contrast area to increase the robustness in different imaging conditions, since most of the images captured by in-car video cameras include large low-contrast sky areas. An adaptive background evaluation model is developed to deal with irregular images that contain large moving objects (front vehicles) or a low-contrast area above the skyline. In the motion compensation processing, a compensating motion vector (CMV) estimation method with an inner feedbackloop integrator is proposed to stably remove the unwanted shaking phenomena in the images without losing the effective area of the images with a constant motion condition. The proposed DIS technique was applied to the on-road captured video sequences with various irregular conditions for performance demonstrations.Index Terms-Adaptive background-based evaluation function, in-car digital image stabilizer (ICDIS), intelligent transportation system (ITS), inverse triangle method, representative point matching (RPM), smoothness index (SI).

show abstract

“…(a) CMV generation method in (19). (b) CMV generation method in (19) with clipper in (22). (c) Proposed method in (23).…”

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

A Robust In-Car Digital Image Stabilization Technique

Hsu

Liang

Fan

et al. 2007

IEEE Trans. Syst., Man, Cybern. C

View full text Add to dashboard Cite

show abstract

“…Variations of this approach are presented for example in the work of Singh (1995), Shi et al (1996), andWang (2004). Other approaches treat the reactive forces in digging as external disturbances.…”

Section: Related Researchmentioning

confidence: 99%

Experiments in Autonomous Earth Moving

Bonchis

Hillier

Ryde

et al. 2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

This paper presents a technology demonstrator currently under development and describes experiments carried out to date in autonomous bulk material handling using mobile equipment. Our primary platform is a Bobcat S185 skid-steer loader instrumented with an onboard computer, a sensor suite, and a communication link that support various levels of automation, from remote control to supervised autonomy. We present the main system components and discuss the autonomous cleaning of spillage and carryback, common bulk handling task in mining, currently executed exclusively using manually and/or remotely operated loaders. The system architecture is based on Spring, a Robotics Software Framework developed by CSIRO to support rapid development of new robotic systems, distributed as an Open Source package.

show abstract

“…2. The detectors are responsible for collecting the information of the road segment and sending the information to the segmentagent [6] . Each intersection-agent has a variable COUNT to record the traffic flow of each direction where COUNT = {Counte, Counts, Countw, Countn}.…”

Section: Coordination Of the Intersectionagentsmentioning

confidence: 99%

Step-coordination algorithm of traffic control based on multi-agent system

Zhang

2009

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Aiming at the deficiency of conventional traffic control method, this paper proposes a new method based on multi-agent technology for traffic control. Different from many existing methods, this paper distinguishes traffic control on the basis of the agent technology from conventional traffic control method. The composition and structure of a multi-agent system (MAS) is first discussed. Then, the step-coordination strategies of intersection-agent, segment-agent, and area-agent are put forward. The advantages of the algorithm are demonstrated by a simulation study.

show abstract

Agent-Based Control for Fuzzy Behavior Programming in Robotic Excavation

Cited by 23 publications

References 24 publications

A Robust In-Car Digital Image Stabilization Technique

A Robust In-Car Digital Image Stabilization Technique

Experiments in Autonomous Earth Moving

Step-coordination algorithm of traffic control based on multi-agent system

Contact Info

Product

Resources

About