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
- 2024.10
- 수록면
- 1,068 - 1,073 (6page)
이용수
초록· 키워드
오류제보하기
Nonlinear model predictive control is a reliable and effective approach for optimization-based motion planning. In safety-critical control systems, controllers must solve inequality-constrained optimization problems. However, scene understanding, which consists of perceiving the driving environment and designing safety constraints, may complicate the optimization problem and is a resource-intensive process. In this paper, we propose a unified scene understanding method that uses occupancy grid maps (OGMs) to design a single unified constraint. We also propose a novel method for designing OGMs that method accounts for noise and uncertainties. We use this OGM approach for scene understanding to design a single constraint that ensures that only cells with occupancy probability values less than a predefined threshold can be traversed. We embed this constraint into the optimization problem as a single unified discrete barrier state. In the experiments, we compare the performance of the proposed method with that of an augmented Lagrangian method. The motion planning results in a pop-up obstacle avoidance scenario using an unmanned mobile vehicle demonstrate the advantages of the proposed method, such as reduced time costs and improved safety.
목차
Abstract
1. INTRODUCTION
2. RELATED WORKS
3. COST MAP AS AN OCCUPANCY GRID MAP
4. GRID-BASED INTEGRATED MOTION PLANNING
5. EXPERIMENTAL RESULTS
6. DISCUSSION AND CONCLUSION
REFERENCES
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