Non-Gaussian SLAP: Simultaneous Localization and Planning Under Non-Gaussian Uncertainty in Static and Dynamic Environments
Non-Gaussian SLAP: Simultaneous Localization and Planning Under Non-Gaussian Uncertainty in Static and Dynamic Environments
复制标题
非高斯SLAP：静态和动态环境中非高斯不确定性下的同步定位和规划
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
P. Kumar
中科院分区：
文献类型：
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作者：
Mohammadhussein Rafieisakhaei;S. Chakravorty;P. Kumar
在过程和测量不确定性下的同时定位和规划（SNOW）是一个挑战。它涉及到解决一个随机控制问题建模为部分观测马尔可夫决策过程（POMDP）的一般框架。对于凸环境，我们提出了一个基于优化的开环最优控制问题，再加上滚动时域控制策略，规划高品质的轨迹沿着的状态局部化的不确定性减少，而系统达到目标状态，以最小的控制努力。在非凸状态约束的静态环境中，通过定义障碍函数来修改优化，以获得无碰撞路径，同时保持先前的目标。通过初始化优化轨迹在不同的同伦类和比较所得的成本，我们提高了质量的解决方案中存在的行动和测量的不确定性。在具有时变约束的动态环境中，如移动障碍物或禁区，该方法被扩展到寻找无碰撞轨迹。在本文中，底层空间是连续的，信念是非高斯的。在没有障碍物的情况下，优化是一个全局凸问题，而在存在障碍物的情况下，它变成局部凸的。我们证明了该方法在不同情况下的性能。
Simultaneous Localization and Planning (SLAP) under process and measurement uncertainties is a challenge. It involves solving a stochastic control problem modeled as a Partially Observed Markov Decision Process (POMDP) in a general framework. For a convex environment, we propose an optimization-based open-loop optimal control problem coupled with receding horizon control strategy to plan for high quality trajectories along which the uncertainty of the state localization is reduced while the system reaches to a goal state with minimum control effort. In a static environment with non-convex state constraints, the optimization is modified by defining barrier functions to obtain collision-free paths while maintaining the previous goals. By initializing the optimization with trajectories in different homotopy classes and comparing the resultant costs, we improve the quality of the solution in the presence of action and measurement uncertainties. In dynamic environments with time-varying constraints such as moving obstacles or banned areas, the approach is extended to find collision-free trajectories. In this paper, the underlying spaces are continuous, and beliefs are non-Gaussian. Without obstacles, the optimization is a globally convex problem, while in the presence of obstacles it becomes locally convex. We demonstrate the performance of the method on different scenarios.