Importance of Path Planning Variability: A Simulation Study

• Published in: Topics in cognitive science Vol. 15; no. 1; pp. 139 - 162
• Main Authors: Krichmar, Jeffrey L., He, Chuanxiuyue
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2023
• Subjects: Adaptive behavior; Algorithms; Cognitive load; Computer Simulation; Computer simulations; Humans; Learning; Navigation; Neural networks; Path planning; Robotics; Surveys and Questionnaires; Navigation; Computer simulations; Neural networks; Cognitive load; Path planning; Robotics; Adaptive behavior
• ISSN: 1756-8757; 1756-8765; 1756-8765
• DOI: 10.1111/tops.12568

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. Krichmar and He took a unique approach to understanding variation in human navigation by using path planning algorithms from robotics to quantify when and where people apply route, survey or graph knowledge. They simulated individual differences in human navigation strategies that had been observed in prior studies and further showed that individuals switch strategies between trials or even within trials. They suggest that the choice of navigation strategies may be due to weighing the relative cost of mental effort when applying survey knowledge over utilizing less effortful strategies such as route knowledge.