ヘビの動きに着想を得たハイブリッド学習による移動ロボットの測位
Snake-Inspired Mobile Robot Positioning with Hybrid Learning
ヘビのような蛇行運動を行う移動ロボットの慣性センサデータから、ニューラルネットワークで移動距離を推定し、測位誤差を33%改善するフレームワークMoRPINetを提案した。
著者: Aviad Etzion, Nadav Cohen, Orzion Levy, Zeev Yampolsky, Itzik Klein
分類: cs.RO, eess.SP
原文アブストラクト
Mobile robots are used in various fields, from deliveries to search and rescue applications. Different types of sensors are mounted on the robot to provide accurate navigation and, thus, allow successful completion of its task. In real-world scenarios, due to environmental constraints, the robot frequently relies only on its inertial sensors. Therefore, due to noises and other error terms associated with the inertial readings, the navigation solution drifts in time. To mitigate the inertial solution drift, we propose the MoRPINet framework consisting of a neural network to regress the robot's travelled distance. To this end, we require the mobile robot to maneuver in a snake-like slithering motion to encourage nonlinear behavior. MoRPINet was evaluated using a dataset of 290 minutes of inertial recordings during field experiments and showed an improvement of 33% in the positioning error over other state-of-the-art methods for pure inertial navigation.
