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Owing to their high center of gravity (CoG) and small footprint, two-wheeled robots (TWRs) are expected to be used in collaborative tasks with humans. When a TWR uses its mounted arm to transport a load, its CoG shifts, deteriorating translational control performance. Conventionally, CoG shift is compensated using the torque applied to the arm by the load. However, because the load-weight information is not considered, gravity compensation is insufficient. This study aims to improve translational control performance through precise gravity compensation. The proposed method is a gravity compensator comprising a model-based compensator, which clarifies the physical characteristics, and a model-error compensator, which addresses errors caused by the load weight. A virtual point is also introduced to improve body stabilization and translational control performance. The proposed method enables precise gravity compensation for a TWR and improves its translational control performance. The experimental results demonstrate the robustness of the proposed method in maintaining translational control performance regardless of the load conditions. This compensation method significantly affects the practical deployment of TWRs in human collaborative environments, such as factories and warehouses.
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Kanazawa et al. (2025) studied this question.
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