Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors
2026-07-27 • Robotics
Robotics
AI summaryⓘ
The authors developed a new method for planning drone paths in complicated city air routes that are not straightforward. Their approach uses a special type of math optimization combining tracking and control rules to make sure the drone stays inside allowed paths and moves smoothly. They also included a way to avoid getting stuck in tricky areas by focusing on the shortest path to the destination. Tests show their method plans valid and safe paths without needing separate global planning steps.
UAV navigationnon-convex corridorsmixed-integer model predictive controlmotion planningdynamic feasibilityshortest pathoptimizationlocal minimaurban airspace
Authors
Henrique Silva, Marcelo A. Santos, Guilherme V. Raffo
Abstract
This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.