.png){kind=icon}
Return
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}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
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
Journal
IF:
9.9
Papers:
8.5K
Citations:
6.0W
Organization
Cited Papers
ShareSaveResearch on Public Air Route Network Planning of Urban Low-Altitude Logistics Unmanned Aerial Vehicles.png)
SUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
OAAIShareSaveShareSave
ShareSaveShareSaveMotion planning of a free-flying space robot system under end effector task constraintsACTA ASTRONAUTICA
ACTA ASTRONAUTICAIF3.4
PREAI
ShareSave
View more