クアッドロータ軌道計画のための自動パラメータ適応
Automatic Parameter Adaptation for Quadrotor Trajectory Planning
クアッドロータの軌道計画におけるパラメータ調整を自動化するため、目的を2種類に分けて非同期に最適化するフレームワークを提案し、ベンチマークや実機飛行で有効性を示した。
著者: Xin Zhou, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
Online trajectory planners enable quadrotors to safely and smoothly navigate in unknown cluttered environments. However, tuning parameters is challenging since modern planners have become too complex to mathematically model and predict their interaction with unstructured environments. This work takes humans out of the loop by proposing a planner parameter adaptation framework that formulates objectives into two complementary categories and optimizes them asynchronously. Objectives evaluated with and without trajectory execution are optimized using Bayesian Optimization (BayesOpt) and Particle Swarm Optimization (PSO), respectively. By combining two kinds of objectives, the total convergence rate of the black-box optimization is accelerated while the dimension of optimized parameters can be increased. Benchmark comparisons demonstrate its superior performance over other strategies. Tests with changing obstacle densities validate its real-time environment adaption, which is difficult for prior manual tuning. Real-world flights with different drone platforms, environments, and planners show the proposed framework's scalability and effectiveness.
