Robot Motion Generation by Time-series Inverse Kinematics Optimization Considering Time-variance/Time-invariance and Adjacency of Configuration

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  • MUROOKA Masaki
    Graduate School of Information Science and Technology, The University of Tokyo
  • KAKIUCHI Yohei
    Graduate School of Information Science and Technology, The University of Tokyo
  • OKADA Kei
    Graduate School of Information Science and Technology, The University of Tokyo
  • INABA Masayuki
    Graduate School of Information Science and Technology, The University of Tokyo

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  • コンフィギュレーションの時変·時不変性と隣接性を考慮した時系列逆運動学最適化計算によるロボット運動生成
  • コンフィギュレーションの時変・時不変性と隣接性を考慮した時系列逆運動学最適化計算によるロボット運動生成
  • コンフィギュレーション ノ ジヘン ・ トキ フヘンセイ ト リンセツセイ オ コウリョ シタ ジケイレツ ギャクウンドウガク サイテキカ ケイサン ニ ヨル ロボット ウンドウ セイセイ

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<p>We propose an optimization-based time-series inverse kinematics for robot motion generation. In this method, the design variables are the combination of time-variant configuration, which is time-series joint position, and time-invariant configuration, which is the grasping point or the robot location. The inverse kinematics problem is regarded as an optimization problem, and the sequential quadratic programming is applied by describing the target motion as a task function and deriving its gradient. The generated motion is smooth because of the regularization of adjacent joint displacement. We show various robot motions generated by the proposed time-series inverse kinematics.</p>

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