SMUGプランナ：過酷環境下で移動ロボットのための安全なマルチゴールプランナ
SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments
複数の目標地点を巡回する移動ロボットの経路計画において、シミュレーションで学習した走破性を活用した階層的な状態検証とLazyPRM*を組み合わせ、安全かつ高速に最適経路を生成するプランナを提案し、四足歩行ロボットANYmalで自律ミッションを実証した。
著者: Changan Chen, Jonas Frey, Philip Arm, Marco Hutter
分類: cs.RO
原文アブストラクト
Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments. Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints. In this work, we consider the multi-goal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations. To increase autonomy in these missions, we propose a safe multi-goal (SMUG) planner that generates an optimal motion path to visit those targets. To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation. We use LazyPRM* with an informed sampler to accelerate collision-free path generation. Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds. Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain.
