AGV navigation in complex environments: A cooperative trajectory optimization strategy based on SMF-MPC
Zonghe Ding, Ling Chen, Jintao Wang, Qi Qu, Liming Chang, Yajing Cheng · 2026
Read AGV navigation in complex environments: A cooperative trajectory optimization strategy based on SMF-MPC by Zonghe Ding, Ling Chen, Jintao Wang, Qi Qu, Liming Chang, Yajing Cheng (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/plos-pone-0355960.txt.
Description
Zonghe Ding and colleagues tackle a guidance problem for automated guided vehicles. These are the driverless carts that ferry goods around warehouses and factories. The problem is that sensors lie. Standard navigation math assumes the lies follow tidy patterns. Their 2026 paper pairs a predictive controller with a set-membership filter. Instead of guessing a probability spread, the filter fences the vehicle's true state inside a shrinking geometric boundary.
That boundary feeds directly into the controller. It acts as a safety margin that breathes with conditions. So the vehicle slows or swerves when uncertainty balloons. In simulations under three kinds of noise, the approach reached its targets more often. It also held up better than two established controllers. For warehouses that cannot afford collisions, guarantees beat averages.
Catalog record
| CREATOR | Zonghe Ding, Ling Chen, Jintao Wang, Qi Qu, Liming Chang, Yajing Cheng |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46505 |
| LICENSE | CC_BY |
| RIGHTS | CC BY |
| SOURCE | PLOS |