Abstract
This paper presents two distributed algorithms for enabling a swarm of robots with local sensing and local coordinates to estimate the dimensions and orientation of an unknown complex polygonal object, i.e., its minimum and maximum width and its main axis. Our first approach is based on a robust heuristic of distributed Principal Component Analysis (DPCA), while the second is based on turning the idea of Rotating Calipers into a distributed algorithm (DRC). We simulate DRC and DPCA methods and test DPCA on real robots. The result show our algorithms successfully estimate the dimension and orientation of convex or concave objects with a reasonable error in the presence of noisy data.
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