论文标题
四轨轨迹计划的自动参数改编
Automatic Parameter Adaptation for Quadrotor Trajectory Planning
论文作者
论文摘要
在线轨迹规划师使四肢能够在未知的混乱环境中安全，平稳地导航。但是，调整参数具有挑战性，因为现代计划者已经变得太复杂了，无法在数学上建模并预测他们与非结构化环境的相互作用。这项工作通过提出一个计划者参数适应框架，将人类从循环中脱颖而出，该框架将目标构成两个互补类别并不同步。使用贝叶斯优化（Bayesopt）和粒子群优化（PSO）评估和没有轨迹执行的目标进行了评估。通过结合两种目标，可以加速黑盒优化的总收敛速率，而可以提高优化参数的尺寸。基准比较证明了其优于其他策略的表现。随着障碍物密度变化的测试验证了其实时环境的适应，这对于先前的手动调整很难。具有不同无人机平台，环境和计划人员的现实世界飞行显示了拟议的框架的可扩展性和有效性。
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.
