Collaborative Research: NSF-DST: RI: Small: Algorithms from wetware:A neural model of spat — NSF Award to Johns Hopkins University
When navigating in complex environments, fixed landmarks and moving obstacles are crucial features that influence efficient and robust path planning, optimal route finding, and minimization of navigational errors. Autonomous vehicles are severely limited by their inability to reliably anchor their navigation to landmar
| Award title | Collaborative Research: NSF-DST: RI: Small: Algorithms from wetware:A neural model of spat |
|---|---|
| Award ID | 2342867 |
| Awardee | Johns Hopkins University |
| City | BALTIMORE |
| State | MD |
| Amount obligated | $174,998 |
| Principal investigator | James Knierim |
| Program | Robust Intelligence |
| Start date | 04/15/2025 |
| Abstract | When navigating in complex environments, fixed landmarks and moving obstacles are crucial features that influence efficient and robust path planning, optimal route finding, and minimization of navigational errors. Autonomous vehicles are severely limited by their inability to reliably anchor their navigation to landmarks and predict and avoid the movement of others. The research team proposes to develop and refine a computational model of spatial navigation and spatial representation using neura |
| Source | NSF Awards |
$799/mo
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