论文标题
Assistme：非独立工具的纵向控制的政策迭代
AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle
论文作者
论文摘要
在本文中，我们设计了一种基于物理启发的基于模型的辅助半自主控制（ASC）算法，以解决具有静态障碍物的环境中安全驾驶车辆（动力轮椅）的问题。一旦在线实施，提出的算法就需要有限的计算能力，并且依赖于预先计算的（离线）地图（查找表）。通过实施政策迭代，可以将这些迭代最小化（安全停止在障碍物附近），可以考虑到：（i）车辆动力学； （ii）驱动程序的意图以三个独立的随机过程建模。我们称他们为专家司机，顽皮的孩子和盲人驾驶员模特。与健康参与者的一项研究证实，ASC的表现超过了基线规则的控制（统计学上的显着结果）。
In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate stochastic processes. We call them the expert driver, the naughty child and the blind driver models. A study with healthy participants confirmed that ASC outperforms a baseline rule-based control (a statistically significant result).
