Stochastic Extended LQR for Optimization-based Motion Planning Under Uncertainty.
Stochastic Extended LQR for Optimization-based Motion Planning Under Uncertainty.
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DOI：
10.1109/tase.2016.2517124
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发表时间：
2016-04
期刊：
影响因子：
--
通讯作者：
Alterovitz R
中科院分区：
文献类型：
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作者：
Sun W;van den Berg J;Alterovitz R
我们介绍了一种新的基于优化的运动规划，随机扩展LQR（SELQR），它计算的轨迹和相关的线性控制策略的目标是最小化的期望值的用户定义的成本函数。SELQR适用于具有随机非线性动力学的机器人系统，其运动不确定性由状态和控制相关的高斯分布建模。在每次迭代中，SELQR使用前向和后向值迭代的组合来估计沿轨迹沿着的每个状态的来成本和去成本。然后，SELQR在每次迭代时沿轨迹局部优化每个状态沿着，以最小化预期的总成本，这导致用于动态线性化和成本函数二次化的平滑状态。SELQR逐步改进预期总成本的近似值，从而产生更高质量的计划。对于不完美的传感应用，我们扩展SELQR计划在机器人的信念空间。我们表明，我们的迭代方法在多个模拟场景中实现了快速可靠的高质量计划收敛，这些场景涉及汽车式机器人，四旋翼机和执行肝脏活检程序的医疗可操纵针。
We introduce a novel optimization-based motion planner, Stochastic Extended LQR (SELQR), which computes a trajectory and associated linear control policy with the objective of minimizing the expected value of a user-defined cost function. SELQR applies to robotic systems that have stochastic non-linear dynamics with motion uncertainty modeled by Gaussian distributions that can be state- and control-dependent. In each iteration, SELQR uses a combination of forward and backward value iteration to estimate the cost-to-come and the cost-to-go for each state along a trajectory. SELQR then locally optimizes each state along the trajectory at each iteration to minimize the expected total cost, which results in smoothed states that are used for dynamics linearization and cost function quadratization. SELQR progressively improves the approximation of the expected total cost, resulting in higher quality plans. For applications with imperfect sensing, we extend SELQR to plan in the robot's belief space. We show that our iterative approach achieves fast and reliable convergence to high-quality plans in multiple simulated scenarios involving a car-like robot, a quadrotor, and a medical steerable needle performing a liver biopsy procedure.