能動的ロボット源探索のための高速でスケーラブルな信号推論
Fast and Scalable Signal Inference for Active Robotic Source Seeking
ロボットが未知環境で信号源を探索する際に、大規模な計測データと障害物を扱えるグローバル・ローカル因子グラフモデルを提案し、スケーラブルなプランナーと組み合わせてシミュレーションと実機で性能向上を示した。
著者: Christopher E. Denniston, Oriana Peltzer, Joshua Ott, Sangwoo Moon, Sung-Kyun Kim, Gaurav S. Sukhatme, Mykel J. Kochenderfer, Mac Schwager, Ali-akbar Agha-mohammadi
分類: cs.RO
原文アブストラクト
In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. %In this work, We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments.
