.png){kind=icon}
返回
Data-Driven Polytopic Approximation for an n-Dimensional Probabilistic Reachable Set
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2024.3399921.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, we first propose an algorithm to find a probabilistic reachable set (PRS) that bounds system states given a prescribed confidence level. Then, we establish an optimization framework using mixed integer linear programming, where the solution identifies a convex polytope that approximates the PRS. Utilizing this formulation, we have devised a heuristic algorithm aimed at efficiently determining its solution without compromising significant accuracy. Through case studies, we have tested this heuristic algorithm, showcasing its simultaneous benefits in terms of efficiency, accuracy, near-optimality, and robustness. The positive outcomes of this research lay the foundation for potential applications in the real-time, safety-critical motion planning of dynamic systems under uncertainties.
Keyword:
Approximation algorithms
Heuristic algorithms
Uncertainty
Probabilistic logic
Dynamical systems
Safety
Optimization
Convex approximation
mixed integer linear programming (MILP)
probabilistic reachable set (PRS)
uncertain dynamic system
期刊
IF:
9.9
论文数:
8.5K
被引数:
6.0W
机构
引用论文
Research on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles城市低空物流无人机公共航路网络规划研究.png)
SUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
OAAI分享收藏
Uncertainty-Aware Ensemble Combination Method for Quality Monitoring Fault Diagnosis in Safety-Related Products安全相关产品质量监控故障诊断的不确定性感知集成组合方法
PREAI
分享收藏Motion planning of a free-flying space robot system under end effector task constraintsACTA ASTRONAUTICA
ACTA ASTRONAUTICAIF3.4
PREAI
分享收藏
查看更多内容