A New Rate-Allocation-Optimization Framework

2008 ◽  
Vol 23 (04) ◽  
pp. 448-457 ◽  
Author(s):  
Baris Guyaguler ◽  
Thomas James Byer
Keyword(s):  
Rate Allocation ◽  
Optimization Framework

scholarly journals Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

2017 ◽  
Vol 26 (1) ◽  
pp. 479-489 ◽  
Author(s):  
Aniello Fiengo ◽  
Giovanni Chierchia ◽  
Marco Cagnazzo ◽  
Beatrice Pesquet-Popescu
Keyword(s):  
Convex Optimization ◽  
Video Coding ◽  
Rate Allocation ◽  
Optimization Framework

scholarly journals Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman
Keyword(s):  
Real World ◽  
High Speed ◽  
Analytical Approximation ◽  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Quadrotor Aircraft ◽  
Time Optimal ◽  
Feasibility Constraints ◽  
Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

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