By Bob Stat
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Extra resources for Altogether Fun and Absolutely Understanding Rubik's Pocket Cube
In a robot ﬁxed coordinate system the coordinates of radar points (ϕi , ri ) and (ϕi+j , ri+j ) are xi = ri sin ϕi yi = ri cos ϕi Localization and On-Line Map Building for an Autonomous Mobile Robot xi+j = ri+j sin ϕi+j 39 yi+j = ri+j cos ϕi+j as shown in ﬁgure 4. ,719 . To eliminate noise j = 5 should be taken, as shown in ﬁgure 5. Taking successive points with j = 1 ri+j γi ϕi+j yi+j yi ri ϕi xi+j xi i+j Fig. 4. Angle between radar points i Fig. 5. Noise in angle measurement any measurement errors are exaggerated: on a ﬂat wall three points in sucession will show rather diﬀerent angles γi and γi+1 .
In this paper, we show how a modular design can not alone support the robust execution of tasks but also how it can be directly exploited for teaching of the representations needed for the planning and execution of these tasks. In Section 2 some more background in terms of related work and project speciﬁc constraints are outlined. In Section 3 the architecture we have developed to support the modular and teachable robot system is explained. In Section 4 it is explained how the representations necessary for the planning and execution of tasks are taught.
Two individual landmarks li and lj , automatically generates a partial map match µi,j . Neglecting geometric aspects of the map building process, the best match is then given by |µ0 | = min(ν i,j |li ∈ La , lj ∈ Lb ) (13) However each map match µi,j yields a geometrical transformation g i,j with which the landmarks of the one set are projected onto the corresponding landmarks of the other set. Some geometrical transformations are more plausible than others (a 10m translation is more unlikely after a path length of 30m, than a 50cm translation).