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We present our findings on the first application of motion planning methodologies to the recently introduced Sprawl Tuned Autonomous Robot (STAR). The reported results provide a first glimpse on the capabilities of this novel, 3D-printed robot in performing autonomously non-trivial motion planning tasks in environments populated with obstacles. We employ methods from sampling-based motion planning under nonholonomic constraints, and implement in open loop the generated path on the physical robot for various environments of increasing complexity.
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Karydis et al. (2014) studied this question.
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