視覚ロボットのための信念空間における不確実性制約付き微分動的計画法
Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots
信念空間での軌道最適化に不確実性の不等式制約を導入し、拡張ラグランジュ法に基づく確率的微分動的計画法と特徴可視性の不連続性を考慮した確率的可視性モデルを提案した論文。
著者: Shatil Rahman, Steven L. Waslander
分類: cs.RO
原文アブストラクト
Most mobile robots follow a modular sense-planact system architecture that can lead to poor performance or even catastrophic failure for visual inertial navigation systems due to trajectories devoid of feature matches. Planning in belief space provides a unified approach to tightly couple the perception, planning and control modules, leading to trajectories that are robust to noisy measurements and disturbances. However, existing methods handle uncertainties as costs that require manual tuning for varying environments and hardware. We therefore propose a novel trajectory optimization formulation that incorporates inequality constraints on uncertainty and a novel Augmented Lagrangian based stochastic differential dynamic programming method in belief space. Furthermore, we develop a probabilistic visibility model that accounts for discontinuities due to feature visibility limits. Our simulation tests demonstrate that our method can handle inequality constraints in different environments, for holonomic and nonholonomic motion models with no manual tuning of uncertainty costs involved. We also show the improved optimization performance in belief space due to our visibility model.
