Importance of Path Planning Variability: A Simulation Study
Importance of Path Planning Variability: A Simulation Study
复制标题
路径规划可变性的重要性：模拟研究
DOI：
10.1111/tops.12568
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发表时间：
2021
影响因子：
3
通讯作者：
He, Chuanxiuyue
中科院分区：
文献类型：
--
作者：
Krichmar, Jeffrey L.;He, Chuanxiuyue
每个人在空间中航行的方式各不相同。有些人走新奇的捷径，而另一些人则依靠已知的路线来找到自己的路。我们想知道为什么以及如何在人口中有这么多的变化。为了解决这个问题，我们首先将368名人类受试者在虚拟迷宫中导航的轨迹与模拟轨迹进行了比较。模拟轨迹由机器人技术中基于策略的路径规划算法生成。基于人类轨迹和基于不同策略的模拟轨迹之间的相似性，我们发现个体应用于导航空间的策略类型存在差异，以及个体内部在逐个试验的基础上的差异。此外，当受试者偶尔在轨迹中途切换导航策略时，我们观察到试验中的变化。在这些情况下，受试者从一个路线策略开始，在这个策略中，他们沿着一条熟悉的路径走，然后切换到一个调查策略，在这个策略中，他们通过考虑环境的布局走捷径。然后，我们使用五种不同但具有可比性的人工地图模拟了第二组轨迹。这些轨迹在试验内和试验之间产生了类似的策略变化模式。此外，我们改变了相对成本，即假设的心理努力或所需的时间步长，以选择一个学习的路线替代路径。当学习的路线相对昂贵时，模拟代理人倾向于走捷径。相反，当学到的路线是成本较低，模拟代理人表现出对路线策略的偏好。我们认为，成本或假设的精神努力可能是为什么在以前的研究中，受试者使用调查的知识时，指示采取最短的paths.We建议，这种变化，我们在人类中观察到的可能是有益的机器人集群或收集的自主代理在信息收集。
Individuals vary in the way they navigate through space. Some take novel shortcuts, while others rely on known routes to find their way around. We wondered how and why there is so much variation in the population. To address this, we first compared the trajectories of 368 human subjects navigating a virtual maze with simulated trajectories. The simulated trajectories were generated by strategy‐based path planning algorithms from robotics. Based on the similarities between human trajectories and different strategy‐based simulated trajectories, we found that there is a variation in the type of strategy individuals apply to navigate space, as well as variation within individuals on a trial‐by‐trial basis. Moreover, we observed variation within a trial when subjects occasionally switched the navigation strategies halfway through a trajectory. In these cases, subjects started with a route strategy, in which they followed a familiar path, and then switched to a survey strategy, in which they took shortcuts by considering the layout of the environment. Then we simulated a second set of trajectories using five different but comparable artificial maps. These trajectories produced the similar pattern of strategy variation within and between trials. Furthermore, we varied the relative cost, that is, the assumed mental effort or required timesteps to choose a learned route over alternative paths. When the learned route was relatively costly, the simulated agents tended to take shortcuts. Conversely, when the learned route was less costly, the simulated agents showed preference toward a route strategy. We suggest that cost or assumed mental effort may be the reason why in previous studies, subjects used survey knowledge when instructed to take the shortest path. We suggest that this variation we observe in humans may be beneficial for robotic swarms or collections of autonomous agents during information gathering.
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