Design of stable fuzzy controllers for an AGV

Fuzzy logic control is a relatively new technology and hence it needs rigorous comparative analyses with other well-established conventional control schemes. Further, fuzzy controller stability analysis is a major hindrance for its popularity among control engineers. This paper shows how stable fuzzy controllers may be synthesized for a typical AGV from the perspective of variable structure systems (VSS) theory. VSS or sliding model control (SMC) is an established robust non-linear control methodology. The AGV is characterized by highly non-linear, coupled and configuration dependent dynamics, with uncertainty in model parameters. Similarity in performance of the fuzzy controllers to the SMC controller is demonstrated through experimental results obtained for steer control of the AGV

Laser-camera composite sensing for road detection and tracing

An important feature in most urban roads and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is the existence of pavements or curbs on either side de?ning the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, vision-alone methods for extraction of such curbs or road edge features with accurate depth information is a formidable task, as the curb is not conspicuous in the vision image and also requires the use of stereo images. Further, bad lighting, adverse weather conditions, nonlinear lens aberrations, or lens glare due to sun and other bright light sources can severely impair the road image quality and thus the operation of vision-alone methods. In this paper an alternative and novel approach involving the fusion of 2D laser range and monochrome vision image data is proposed to improve the robustness and reliability. Experimental results are presented to demonstrate the viability and effectiveness of the proposed methodology and its robustness to different road configurations and shadows

Road curb and intersection detection using A 2D LMS

In most urban roads, and similar environments such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be hardnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favorable ambient lighting. In our approach, road curbs are extracted speedily using range data provided by a 2D Laser range Measurement System (LMS). Experimental results are presented to demonstrate the viability, and effectiveness, of the proposed methodology and its robustness to different road configurations including road intersections

Road edge and lane boundary detection using laser and vision

This paper presents a methodology for extracting road edge and lane information for smart and intelligent navigation of vehicles. The range information provided by a fast laser range-measuring device is processed by an extended Kalman filter to extract the road edge or curb information. The resultant road edge information is used to aid in the extraction of the lane boundary from a CCD camera image. Hough Transform (HT) is used to extract the candidate lane boundary edges, and the most probable lane boundary is determined using an Active Line Model based on minimizing an appropriate Energy function. Experimental results are presented to demonstrate the effectiveness of the combined Laser and Vision strategy for road-edge and lane boundary detection

Road curb tracking in an urban environment

Road detection and tracking is very useful in the synthesis of driver assistance and intelligent transportation systems. In this paper a methodology is proposed based on the extended Kalman filer for robust road curb detection and tracking using a combination of onboard active and passive sensors. The problem is formulated as detecting and tracking a maneuvering target in clutter using onboard sensors on a moving platform. The primary sensors utilized are a 2 dimensional SICK laser scanner, five encoders and a gyroscope, together with an image sensor (CCD camera). Compared to the active 20 laser scanner the CCD camera is capable of providing observations over an extended horizon, thus making available much useful information about the curb trend, which is exploited in mainly the laser based tracking algorithm. The advantage of the proposed image enhanced laser detection/tracking method, over laser alone detection/tracking, is illustrated using simulations and its robustness to varied road curvatures, branching, turns and scenarios, is demonstrated through experimental results. © 2003 ISlF

CuTE: Curb Tracking and Estimation

The number of road accident related fatalities and damages are reduced substantially by improving road infrastructure and enacting and imposing laws. Further reduction is possible through embedding intelligence onto the vehicles for safe decision making. Road boundary information plays a major role in developing such intelligent vehicles. A prominent feature of roads in urban, semi-urban, and similar environments, is curbs on either side defining the road's boundary. In this brief, a novel methodology of tracking curbs is proposed. The problem of tracking a curb from a moving vehicle is formulated as tracking of a maneuvering target in clutter from a mobile platform using onboard sensors. A curb segment is presumed to be the maneuvering target, and is modeled as a nonlinear Markov switching process. The target's (curb's) orientation and location measurements are simultaneously obtained using a two-dimensional (2-D) scanning laser radar (LADAR) and a charge-coupled device (CCD) monocular camera, and are modeled as traditional base state observations. Camera images are also used to estimate the target's mode, which is modeled as a discrete-time point process. An effective curb tracking algorithm, known as Curb Tracking and Estimation (CuTE) using multiple modal sensor information is, thus, synthesized in an image enhanced interactive multiple model filtering framework. The use and fusion of camera vision and LADAR within this frame provide for efficient, effective, and robust tracking of curbs. Extensive experiments conducted in a campus road network demonstrate the viability, effectiveness, and robustness of the proposed method. © 2006 IEEE

Multimedia Message Distribution in a Constrained Environment

Currently there are standards governing message handling, in particular MIME (Multipurpose Internet Mail Extensions)[1,2], for telecommuting mail messages encompassing a multitude of media, such as graphics images, voice data and motion video apart from plain text. However, the MIME standard (and also work in progress[3]) presupposes certain minimum technical capabilities amongst interconnected and participating mailservers for distribution of such multimedia mail. In particular, the interconnecting channels between mailservers should be of sufficient bandwidth to conduct the large amount of data in MIME messages at 'reasonable' rates and also the nodes must have adequate storage capacity for same. This requirement for bandwidth of channels and storage of mailservers for MIME capability prevents users connected to 'underprivileged'mail nodes from enjoying the benefits brought about by multi-media information and messaging. This may be in spite of the end users owning or having access to resource rich machines.In this paper a smart approach to routing of multimedia messages in an internetwork of mailservers, disparate in storage capacity, performance and network bandwidth, is presented

An Analysis of the bias correction problem in simultaneous localization and mapping

Unmodeled systematic and nonsystematic errors in robot kinematics and measurement processes often cause adverse effects in several autonomous navigation tasks. In particular, accumulated sensor biases can render simultaneous localization and mapping (SLAM) algorithms of autonomous vehicles to perform very poorly especially in large unexplored terrains including cycles, as a result of the estimator divergence and inconsistency. One way to deal with this problem is the accurate modeling and precise calibration of sensors. However this may add up to longer setup and calibration times. Even after accurate calibration and modeling, sensor calibration may often subject to drifts, rendering the efforts ineffective. Therefore, the correct and effective way to deal with this problem is explicit estimation of these parameters with other states. In this work we address the estimation theoretic sensor bias correction problem in SLAM using a simple unified framework and establish theoretically, the behavior and properties of the solution with special consideration to diminishing uncertainty, rates of convergence and observabilit