Vision-Assisted Landing Method for Unmanned Powered Parachute Vehicle Based on Lightweight Neural Network

Abstract

With the development of airdrop technology, the intelligence degree of unmanned powered parachute vehicles (UPPVs) need to be improved. To achieve the accurate landing of UPPVs in complex environments, a landing runway recognition model based on a deep learning algorithm is trained and five actual flight tests are conducted. A six-degree-of-freedom (6-DOF) mathematical model of an unmanned powered parachute vehicle is established, and a landing runway offset controller is designed. The lightweight landing runway recognition model was trained by combining the YOLOv4 framework and the lightweight neural network MobileNet-V3 (Large) and validated in various scenarios. The runway recognition model was transplanted into the airborne image processor, and an unmanned powered parachute vehicle test platform was built for actual flight testing. The test results showed that the comprehensive accuracy of the runway recognition was 97.81% during visual landing and the offset correction was completed within 15s.

Keywords

• Unmanned powered parachute vehicle
• visual landing
• YOLOv4
• lightweight neural network
• offset controller