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This research advances autonomous flight by solving complex motion planning problems in non-convex urban environments using mixed-integer model predictive control. By embedding shortest-path offsets directly into the optimization, the framework ensures dynamic feasibility and convergence without relying on separate global planners. This development represents a significant step forward for Machine Learning applications in robotics where rigorous mathematical guarantees are essential for safety-critical navigation.
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Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

arXiv2 min read

arXiv:2607.24369v1 Announce Type: new 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.

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.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2607.24369 [cs.RO]

(or arXiv:2607.24369v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.24369 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 195 words

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