Multitasking collision-free motion planning algorithms in Euclidean spaces
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We present optimal motion planning algorithms which can be used in designing practical systems controlling objects moving in Euclidean space without collisions. Our algorithms are motivated by those presented by Mas-Ku and Torres-Giese (as streamlined by Farber), and are developed within the more general context of the multitasking (a.k.a. higher) motion planning problem. In addition, our implementation works more efficiently than previous ones when applied to systems with a large number of moving objects.
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