動的環境における推定・予測・計画の統合フレームワークDynoJEPP
DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments
動的環境でのロボットナビゲーションにおいて、状態推定・予測・計画を因子グラフで統合し、情報の流れを方向付ける新規因子を導入して推定の汚染を防ぐ手法を提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Mikolaj Kliniewski, Jesse Morris, Yiduo Wang, Ian R. Manchester, Viorela Ila
分類: cs.RO
原文アブストラクト
DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environments. In conventional factor-graph-based approaches that jointly formulate estimation, prediction, and planning, information from prediction and planning feeds back into state estimation, yielding corrupted estimates, undesired behaviors, and unsafe plans. To address this, DynoJEPP introduces a novel directed factor that enforces directional information flow within the factor graph, preventing prediction and planning from corrupting state estimation. We evaluate the impact of directed factors on inter-module interactions during navigation in both static and dynamic environments. Our results demonstrate that these factors are critical for safe operation, as without them, the robot collides in the majority of experiments. Building on this, we further introduce Cooperative DynoJEPP, which enables the ego robot to incorporate cooperative object behavior into its prediction and trajectory planning.
