August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Steering through multiple waypoints without model-based trajectory planning
Author Affiliations & Notes
  • Brett Fajen
    Rensselaer Polytechnic Institute
  • A.J. Jansen
    Rensselaer Polytechnic Institute
  • Footnotes
    Acknowledgements  NSF 2218220
Journal of Vision August 2023, Vol.23, 5019. doi:
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      Brett Fajen, A.J. Jansen; Steering through multiple waypoints without model-based trajectory planning. Journal of Vision 2023;23(9):5019.

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      © ARVO (1962-2015); The Authors (2016-present)

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Humans and other animals are capable of moving 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, it is often not enough to focus entirely on the most proximal goal. Skillful navigation also relies on the ability to look farther ahead and anticipate the need to reach goals that lie just beyond the immediate future. For example, when steering through multiple waypoints, the actor may adapt how they approach the first waypoint in anticipation of having to steer through the second waypoint. Such behavior is often described as path planning and assumed to rely on forward and inverse internal models. However, the possibility that such behavior could also be captured by information-based control without resorting to internal models has not been seriously considered. In this study, we introduce a new information-based control strategy for steering through multiple waypoints that captures anticipation. The strategy relies on currently available information about the curvature and tangent direction of the constant radius path that passes through the next two waypoints. Agents that follow this strategy move so as to null the difference between the current heading direction and the optically specified tangent direction of the constant-radius path. In simulations of this strategy, we found that the agent adapts how it approaches the upcoming waypoint in anticipation of the subsequent waypoint. We also compared model trajectories with those generated by humans in a waypoint steering task as well as those generated by other models of steering. Taken together, the findings provide an existence proof that human-like anticipatory steering is possible based on information-based control without resorting to internal models.


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