Let's Make Robots!

Robust Autonomous Robot Localization Using Interval Analysis


i'm gonna do my project on autonomous robot.... and i got a ieee abstract.....

here goes....

Abstract :

This paper deals with the determination of the position and orientation of a mobile robot from distance measurements provided by a belt of onboard ultrasonic sensors. The environment is assumed to be two-dimensional, and a map of its landmarks is available to the robot. In this context, classical localization methods have three main limitations. First, each data point provided by a sensor must be associated with a given landmark. This data-association step turns out to be extremely complex and time-consuming, and its results can usually not be guaranteed. The second limitation is that these methods are based on linearization, which makes them inherently local. The third limitation is their lack of robustness to outliers due, e.g., to sensor malfunctions or outdated maps. By contrast, the method proposed here, based on interval analysis, bypasses the data-association step, handles the problem as nonlinear and in a global way and is (extraordinarily) robust to outliers.

i need help in Interval analysis concepts and algorithm concepts...

give some ideas.... plz...thx in advance....

i have article based on tis project.if you need i'll mail that....plz clarify me with those calculation and equational concepts.....plz...thx...
paper_kieffer_robot_reliable_comp.pdf2.18 MB

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It is based on SIVIA algorithm & Inclusion Fuctions test in "Interval analysis"...... those Inclusion test fuctions are fully based on Conditions give like conitions det(ab,am) <0  & det(ab,bm)>0..........scalar product <ab,am>=0 like this.. all these are vector geometry... and i don t understand what these conditions represent generally according to those Figures in paper...



plz help on that....fully vector geometry i think..

The methods are decribed pretty clearly in the article, the variables you listed above are:
• 'm' is a generalised point in the W (map) 2D plane.
• 'aj' and 'bj' are two points on W describing the ends of an object partition boundary segment. The map area to the right of the line aj->bj is inside the object partition.

If you come across a variable that you're unfamiliar with, check the notation section, just before the appendix. The conditions you listed above appear to just be from the 'prior information' section of the article, which only deals with analysing the existing map, i.e. not much to do with the robot or its sensors.