不整地対応モデル予測制御による人型・飛行ロボット協調の捜索救助
Terrain-Aware Model Predictive Control of Heterogeneous Bipedal and Aerial Robot Coordination for Search and Rescue Tasks
人型ロボットとドローンのチームで捜索救助を行うため、地形の起伏をガウス過程で学習し、不整地を安全に移動する経路を生成するモデル予測制御と、scLTLに基づくタスク割り当てを組み合わせた計画手法を提案した。
著者: Abdulaziz Shamsah, Jesse Jiang, Ziwon Yoon, Samuel Coogan, Ye Zhao
分類: cs.RO
原文アブストラクト
Humanoid robots offer significant advantages for search and rescue tasks, thanks to their capability to traverse rough terrains and perform transportation tasks. In this study, we present a task and motion planning framework for search and rescue operations using a heterogeneous robot team composed of humanoids and aerial robots. We propose a terrain-aware Model Predictive Controller (MPC) that incorporates terrain elevation gradients learned using Gaussian processes (GP). This terrain-aware MPC generates safe navigation paths for the bipedal robots to traverse rough terrain while minimizing terrain slopes, and it directs the quadrotors to perform aerial search and mapping tasks. The rescue subjects' locations are estimated by a target belief GP, which is updated online during the map exploration. A high-level planner for task allocation is designed by encoding the navigation tasks using syntactically cosafe Linear Temporal Logic (scLTL), and a consensus-based algorithm is designed for task assignment of individual robots. We evaluate the efficacy of our planning framework in simulation in an uncertain environment with various terrains and random rescue subject placements.
