Visual Control of Steering, Obstacle Avoidance, and Path Following

在视觉的基础上引导一个人在世界上运动的能力在许多日常活动中起着至关重要的作用，从日常行为（如在拥挤的空间中行走）到熟练的任务（如沿着沿着山路驾驶拖拉机拖车）。 通常，人类和其他动物必须在密集杂乱的环境中高速移动，避开障碍物，挤过狭窄的开口，沿着蜿蜒的路径到达目的地。 为了安全有效地执行这些任务，完全专注于最近的物体是不够的。 熟练的导航还依赖于看得更远的能力，并预测达到近期目标的需要。 这个项目将研究人们如何在视觉上引导他们的运动，以满足多个时间范围内的目标。 它的新颖之处在于它专注于将人类视觉运动性能的极限推向极致的任务，并且可以为半自动驾驶员和飞行员辅助技术的发展提供信息。 这项研究将通过揭示新的视觉控制策略来造福社会，这些策略可以适用于机器人，以产生更熟练和更像人类的行为。 在预期未来目标时调整行为的能力通常被认为反映了复杂的认知过程，例如依赖于内部模型的路径规划。 然而，有很少的实证研究预期转向行为和可能性，这种行为可以被捕获，而不调用内部模型尚未得到认真考虑。 该项目将人类转向和凝视行为的实验与数学建模相结合，以探索人类如何引导他们的运动，以满足他们的轨迹上的即时和未来的约束。 使用定制设计的虚拟现实无人机驾驶模拟器，研究人员将研究人类用于导航通过一系列航点的转向和凝视策略，并将进行实验，以揭示人类如何以及在何种条件下适应未来目标的运动。 他们还将使用建模和仿真来确定在没有基于模型的路径规划的情况下是否会出现预期的转向行为。 最后，将开发并向科学界提供一套用于虚拟现实实验以及处理、分析和可视化凝视数据的研究工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The ability to guide one’s movement through the world on the basis of vision plays an essential role in many daily activities, from routine behaviors such as walking through crowded spaces to skilled tasks such as steering a tractor-trailer along a mountain road. Oftentimes, humans and other animals must move at high speeds through densely cluttered environments, avoiding obstacles, squeezing through narrow openings, and following winding paths to reach their goals. To perform such tasks safely and efficiently, it is not enough to focus entirely on the most nearby objects. Skillful navigation also relies on the ability to look farther ahead and anticipate the need to reach goals that lie beyond the immediate future. This project will investigate how people visually guide their movement to satisfy goals over multiple time horizons. It is novel in its focus on tasks that push the limits of human visual-motor performance to the extreme and that could inform the development of semiautonomous driver- and pilot-assist technologies. The research will benefit society by revealing new visual control strategies that could be adapted for use on robots to produce more skillful and human-like behavior. The ability to adapt behavior in anticipation of future goals is often assumed to reflect elaborate cognitive processes such as path planning that rely on internal models. However, there are very few empirical studies of anticipatory steering behavior and the possibility that such behavior could be captured without invoking internal models has not yet been seriously considered. This project combines experiments on steering and gaze behavior in humans with mathematical modeling to explore how humans guide their movements to satisfy both immediate and future constraints on their trajectory. Using a custom-designed, virtual reality drone piloting simulator, the researchers will study the steering and gaze strategies that humans use to navigate through a series of waypoints and will conduct experiments to reveal how and in what conditions humans adapt their movements in anticipation of future goals. They will also use modeling and simulation to determine whether anticipatory steering behavior can emerge without model-based path planning. Lastly, a suite of research tools for conducting experiments in virtual reality and for processing, analyzing, and visualizing gaze data will be developed and made available to the scientific community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.