Abstract
Motion planning is a major topic in robotics . Divergent paths have been taken by practical roboticists and theoretical motion planners. Our goal is to produce algorithms that are practical and have strong theoretical guarantees. Recently, we have proposed a subdivision approach based on soft predicates Wang, C., Chiang, Y.-J., Yap, C.: On soft predicates in subdivision motion planning. In: 29th ACM Symposium on Computational Geometry (SoCG’13), pp. 349–358 (2013). To appear CGTA, Special Issue for SoCG’13 [20], but with a new notion of correctness called resolution-exactness. Unlike mos ques for planar link robots . The technical contributions of this paper are the design of soft predicates for link robots, a novel “T/R splitting method” for subdivision, and feature-based search strategies. The T/R idea is to give primacy to the translational (T) components, and perform splitting of rotational components (R) only at the leaves of a subdivision tree. We implemented our algorithm for a 2-link robot with 4 degrees of freedom (DOFs). Our implementation achieves real-time performance on a variety of nontrivial scenarios. For comparison, our method outperforms sampling-based methods significantly. We extend our 2-link planner to thick link robots with little impact on performance. Note that there are no known exact algorithms for thick link robots.
This work is supported by NSF Grants CCF-0917093, IIS-096053, CNS-1205260, EFRI-1240459, and DOE Grant DE-SC0004874.
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All constants of Physics have at most 8 digits of accuracy. The speed of light is an exception: it is exact, by definition.
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It is standard to identify \(C_{space}(R_0)\) with a subset \(X\subseteq {\mathbb R}^d\). The topology of \(C_{space}(R_0)\) is generally different from that of \(X\). In the case of \(k=2\), the correct topology is easy to simulate since \(S^1\) may be regarded as an interval with the endpoints identified.
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Note that a random strategy is available, but it is never competitive.
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Appendices
Appendices
The full paper [15] has 2 appendices: Appendix I describes the experimental setup including screen shots of the obstacle sets in our experiments. Appendix II provides the basic theory of our Soft Subdivision Search (SSS) framework.
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Luo, Z., Chiang, YJ., Lien, JM., Yap, C. (2015). Resolution-Exact Algorithms for Link Robots. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_21
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