継続学習を用いたコンパニオンロボットのジンバル式人物追跡
Gimbal-Based Human Tracking for Companion Robots Using Continual Learning
モバイルロボットに搭載したジンバルカメラを能動制御し、人物を視野内に維持する追跡手法を提案。継続学習で外見変化に適応し、歩行から走行まで安定追跡を実現。
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著者: Cong-Thanh Vu, Ching-Chieh Liu, Yen-Chen Liu
分類: cs.RO, eess.SY
原文アブストラクト
Reliable and continuous human tracking is essential for natural human-robot interaction, particularly for companion robots. However, many existing approaches rely on wearable tags or fixed cameras with limited fields of view, which reduces system flexibility and often causes tracking failures when the target moves outside the sensing range. In this paper, we present a human tracking approach based on a gimbal-mounted camera integrated into a mobile robot. By actively controlling the gimbal mechanism, the camera can dynamically adjust its viewing direction to maintain the target within the field of view, even under substantial relative motion between the robot and the human. Furthermore, a continual learning strategy is applied to the person re-identification (ReID) task to adapt to changes in appearance and environmental conditions during long-term tracking. Experimental results demonstrate that the proposed system significantly improves the stability and continuity of human tracking, enables real-time re-identification, and provides responsive feedback for reliable tracking of human motion from walking to running. User studies further indicate that the proposed approach enhances user comfort by eliminating the need for wearable tags.
