论文标题
模型预测路径积分控制框架，用于部分可观察到的导航：四局案例研究
Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study
论文作者
论文摘要
最近，模型预测路径积分（MPPI）控制算法已广泛应用于自主导航任务，其中大多数假定成本图是已知的，并且仅执行2D导航任务。在本文中，我们提出了一个通用MPPI控制框架，该框架可用于完全或部分可观察到的环境中的2D或3D自主导航任务，这在机器人应用程序中最普遍。该框架直接利用从板载传感系统中获取的3D-VOXEL网格，用于执行无碰撞导航。我们在杂乱无章的环境中，在基于转子的仿真中测试了基于转子的仿真，以完全和部分可观察到的方案来测试框架。初步结果表明，在2D和3D杂乱的环境中，在部分可观察性下，所提出的框架完美地工作。
Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.
