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Importance of Path Planning Variability: A Simulation Study
Topics in Cognitive Science ( IF 3.265 ) Pub Date : 2021-08-26 , DOI: 10.1111/tops.12568
Jeffrey L Krichmar 1, 2 , Chuanxiuyue He 3
Affiliation  

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.

中文翻译:

路径规划可变性的重要性:模拟研究

每个人在太空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寻找出路。我们想知道人口如何以及为什么会有如此大的差异。为了解决这个问题,我们首先比较了 368 名人类受试者在虚拟迷宫中的轨迹与模拟轨迹。模拟轨迹是由机器人技术的基于策略的路径规划算法生成的。基于人类轨迹与不同基于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存在差异。此外,当受试者偶尔在轨迹的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,受试者从路线策略开始,他们遵循熟悉的路径,然后切换到调查策略,他们通过考虑环境布局走捷径。然后我们使用五个不同但可比较的人工地图模拟了第二组轨迹。这些轨迹在试验内部和试验之间产生了相似的策略变化模式。此外,我们改变了相对成本,即假设的精神努力或所需的时间步长来选择替代路径上的学习路线。当学习到的路线成本相对较高时,模拟代理倾向于走捷径。相反,当学习到的路线成本较低时,模拟代理表现出对路线策略的偏好。我们认为成本或假设的脑力劳动可能是之前研究中的原因,当指示走最短路径时,受试者使用调查知识。我们认为,我们在人类身上观察到的这种变化可能有利于信息收集过程中的机器人群或自主代理集合。
更新日期:2021-08-26
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