The paper discusses an intelligent vision-based control solution for autonomous tracking and landing of Vertical Take-Off and Landing (VTOL) capable Unmanned Aerial Vehicles (UAVs) on ships without utilizing GPS signal. The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking; however, refers to a standardized visual cue installed on most Navy ships called the ”horizon bar” for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning based object detection for long-range ship tracking and classical computer vision for the estimation of aircraft relative position and orientation utilizing the horizon bar during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system was implemented on a quad-rotor UAV equipped with an onboard camera, and approach and landing were successfully demonstrated on a moving deck, which
imitates realistic ship deck motions. Extensive simulations and flight tests were conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy. The video of the real-world experiments and demonstrations is available at this URL.
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A Deep Reinforcement Learning Control Strategy for Vision-based Ship Landing of Vertical Flight Aircraft
The paper discusses a deep reinforcement learning (RL) control strategy for fully autonomous vision-based approach and landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs) on ships in the presence of disturbances such as wind gusts. The automation closely follows the Navy helicopter ship landing procedure and therefore, it detects a horizon bar that is installed on most Navy ships as a visual aid for pilots by applying
uniquely developed computer vision techniques. The vision system utilizes the detected corners of the horizon bar and its known dimensions to estimate the relative position and heading angle of the aircraft. A deep RL-based controller was coupled with the vision system to ensure a safe and robust approach and landing at the proximity of the ship where the airflow is highly turbulent. The vision and RL-based control system was implemented on a quadrotor UAV and
flight tests were conducted where the UAV approached and landed on a sub-scale ship platform undergoing 6 degrees of freedom deck motions in the presence of wind gusts. Simulations and flight tests confirmed the superior disturbance rejection capability of the RL controller when subjected to sudden 5 m/s wind gusts in different directions. Specifically, it was observed during flight tests that the deep RL controller demonstrated a 50% reduction in lateral drift
from the flight path and 3 times faster disturbance rejection in comparison to a nonlinear proportional-integral-derivative controller.
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- Award ID(s):
- 1946890
- NSF-PAR ID:
- 10318624
- Date Published:
- Journal Name:
- AIAA AVIATION 2021 FORUM
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The paper discusses a machine learning vision and nonlinear control approach for autonomous ship landing of vertical flight aircraft without utilizing GPS signal. The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking, but refers to a standardized visual cue installed on most Navy ships called the ”horizon bar” for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning based object detection for long-range ship tracking, and classical computer vision for object detection and the estimation of aircraft relative position and orientation during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system is implemented on a quad-rotor vertical take-off and landing (VTOL) capable unmanned aerial vehicle (UAV) equipped with an onboard camera and was demonstrated on a moving deck, which imitates realistic ship deck motions using a Stewart platform and a visual cue equivalent to the horizon bar. Extensive simulations and flight tests are conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy while the deck is in motion.more » « less
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Summary Robust control of parameter‐dependent input delay linear parameter‐varying (LPV) systems via gain‐scheduled dynamic output‐feedback control is considered in this paper. The controller is designed to provide disturbance rejection in the context of the induced
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2514/6.2021-3218
