Towards Robust Autonomous Landing Systems: Iterative Solutions and Key Lessons Learned

2025/05/18 by Sebastian Schroder, Schroder, Sebastian, Yao Deng +13 · 1 citation
Engineering · #Aerospace and Aviation Technology #FOS: Computer and information sciences #Robotics (cs.RO) #Robotics and Sensor-Based Localization #UAV Applications and Optimization

paper · pdf · doi:10.48550/arxiv.2505.12176

openalex publication_date 2025/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Uncrewed Aerial Vehicles (UAVs) have become a focal point of research, with both established companies and startups investing heavily in their development. This paper presents our iterative process in developing a robust autonomous marker-based landing system, highlighting the key challenges encountered and the solutions implemented. It reviews existing systems for autonomous landing processes, and through this aims to contribute to the community by sharing insights and challenges faced during development and testing.

Citations

• NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS
• TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios
• TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios
• EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors
• AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
• Sampling-based Algorithms for Optimal Motion Planning
• Incremental Sampling-based Algorithms for Optimal Motion Planning

Cited by

• A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline

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