多様な複雑度環境におけるクアッドロータ向け動作計画器の設計と評価
Design and Evaluation of Motion Planners for Quadrotors in Environments with Varying Complexities
クアッドロータの動作計画において、環境の複雑度に応じた計画器設計の指針を得るため、パラメータ化環境の構築手法・難易度指標・二段階計画器を統合するOSSを開発し、シミュレーションと実機で評価した。
著者: Yifei Simon Shao, Yuwei Wu, Laura Jarin-Lipschitz, Pratik Chaudhari, Vijay Kumar
分類: cs.RO
原文アブストラクト
Motion planning techniques for quadrotors have advanced significantly over the past decade. Most successful planners have two stages: a front-end that determines a path that incorporates geometric (or kinematic or input) constraints and specifies the homotopy class of the trajectory, and a back-end that optimizes this path to respect dynamics and input constraints. While there are many different choices for each stage, the eventual performance depends critically not only on these choices, but also on the environment. Given a new environment, it is difficult to decide a priori how one should design a motion planner. In this work, we develop (i) a procedure to construct parametrized environments, (ii) metrics that characterize the difficulty of motion planning in these environments, and (iii) an open-source software stack that can be used to combine a wide variety of two-stage planners seamlessly. We perform experiments in simulations and a real platform. We find, somewhat conveniently, that geometric front-ends are sufficient for environments with varying complexities if combined with dynamics-aware backends. The metrics we designed faithfully capture the planning difficulty in a given environment. All code is available at https://github.com/KumarRobotics/kr_mp_design
