지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2012.10
- 수록면
- 1,002 - 1,006 (5page)
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초록· 키워드
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Deterministic algorithms such as A<SUP>*</SUP> and D<SUP>*</SUP> have been applied with great success to autonomous robotic path planning. However, as search space size increases numerous problem domains will likely become intractable when reactive behavior is desired. This is extremely relevant when considering the exponential increase in search space sizes due to any linear addition of degrees of freedom. Over the last few decades, Evolutionary Algorithms (EA) have been shown to be particularly applicable to extremely large search spaces. However, it is often assumed that generational convergence is the only measure of quality for an EA. A novel combination of the Anytime Planning (AP) criteria with multi-resolution search spaces is explored for application to high-level semi-reactive path planning. Separate populations are evolved in parallel within different abstractions of the search space while low cost solutions from each population are exchanged among the populations. Generational evaluations in low-resolution search spaces can be evaluated quickly generating seed candidate solutions that are likely to speed convergence in the high-resolution search spaces. Convergence rates up to 4× were achieved along with modest decreases in path cost. Parallel GPU computation was then applied to allow reactive searching up to 40Hz in search grids up to 8192×8192 cells.
목차
Abstract
1. INTRODUCTION
2. GA PATH PLANNING
3. GA PATH REPRESENTATION
4. MULTI-RESOLUTION GRIDS
5. ANYTIME PLANNING WITH PARALLEL POPULATIONS
6. EVOLUTIONARY OPERATORS
7. EXPERIMENTAL RESUTLS
8. CONCLUSION
REFERENCES
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