Wind-Aware Aerial Deployment and Control Strategy for Precision Landing of Single-Actuator Autorotating Wing
Shane Kyi Hla Win, Luke Soe Thura Win, Danial Sufiyan, Shaohui Foong
AI summary
Problem
Conventional remote sensor deployment lacks precision and guidance under dynamic wind conditions, limiting access to hazardous or remote areas.
Approach
The authors combine pure pursuit cyclic control with an active dive mode to mitigate wind drift, while using a wind-correlated simulation model to predict optimal UAV release regions for targeted landing.
Key results
- Pure pursuit control with dive mode activation reduces lateral drift during final approach
- Tuned simulation model accurately predicts landing zones under varying wind speeds and directions
- 56 field trials from 60 m altitude achieved 10 m landing accuracy in 89% of low-wind and 57% of high-wind conditions
- Release region prediction enables up to 120 m upwind deployment in high winds while maintaining precision
Why it matters
Provides a scalable, low-cost method for precise aerial sensor scattering in search and rescue, wildfire monitoring, and environmental tracking.
Abstract
The Samara Autorotating Wing (SAW) is a bio- inspired autorotating glider capable of both controlled autorota- tion and diving modes. This work presents control and deploy- ment strategies that enable precision landing of the platform from low altitude. The proposed control approach leverages cyclic control and a dive maneuver to improve landing accuracy. The deployment strategy is developed by updating parameters in a simulated model to reflect real-world performance under varying wind conditions, and then using the model to predict feasible release regions for specified wind direction, speed, and altitude. A total of 56 deployments were conducted from 60 m altitude in both low-wind (< 5 ms−1) and high-wind (> 5 ms−1) conditions representative of the local climate. The platform achieved landings within 10 m of the target in 89% of low- wind trials and 57% of high-wind trials. These results highlight the potential of SAW platform for applications requiring high- precision remote sensor deployment.