论文标题
基于视觉机器人的信念空间中不确定性受限的差异动态编程
Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots
论文作者
论文摘要
大多数移动机器人都遵循模块化的感觉式系统体系结构，由于缺乏功能匹配的轨迹，视觉惯性导航系统的性能甚至灾难性失败。信仰空间中的规划提供了一种统一的方法，可以紧密融合感知，计划和控制模块，从而导致轨迹对嘈杂的测量和干扰非常强大。但是，现有方法处理不确定性是需要为不同环境和硬件进行手动调整的成本。因此，我们提出了一种新颖的轨迹优化公式，该公式在信念空间中纳入了不平等性的不平等约束和一种新颖的增强拉格朗日的随机差异动态编程方法。此外，我们开发了一个概率可见性模型，该模型由于特征性限制而导致不连续性。我们的仿真测试表明，我们的方法可以在不同环境中处理不平等限制，对于自动和非体力运动模型，而无需手动调整不确定性成本。由于我们的可见性模型，我们还显示了信念空间中的优化性能的提高。
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.
