Autonomous Vehicle Technologies for Small Fixed-Wing UAVs

TLDR

Autonomous unmanned air vehicle in‑flight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchical approach for real‑time motion planning of small autonomous fixed‑wing UAVs. The method decomposes trajectory generation into waypoint planning, kinematic smoothing, tracking, and autopilot compensation, with a smoother matching vehicle kinematics, a novel tracking algorithm, and Monte‑Carlo simulations confirming real‑time feasibility on a custom UAV. The approach reduces the path‑search space, enabling complex paths that handle dynamic threats, and a planar version has been successfully implemented on a low‑cost micro‑controller.

Abstract

Autonomous unmanned air vehicle ∞ight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous flxed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle’s route without regard for the dynamic constraints of the vehicle. This results in a signiflcant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfled using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.