Helicopter Autonomous Ship Landing System

This research focuses on developing a helicopter autonomous ship landing algorithm based on the real helicopter ship landing procedure which is already proven and currently used by Navy pilots. It encompasses the entire ship landing procedure from approach to landing using a pilot-inspired vision-based navigation system. The present thesis focuses on the first step towards achieving this overarching objective, which involves modeling the flight dynamics and control of a helicopter and some preliminary simulations of a UH-60 (Blackhawk) helicopter landing on a ship. The airframe of the helicopter is modeled as a rigid body along with rotating articulated blades that can undergo flap, lag and pitch motions about the root. A UH-60 helicopter is used for a representative model due to its ample simulation and flight test data. Modeling a UH-60 helicopter is based on Blade Element Momentum Theory (BEMT), rotor aerodynamics with the Pitt-Peters linear inflow model, empennage aerodynamics and rigid body dynamics for fuselage. For the blade dynamics, the cyclic (1/rev) and collective pitch motions are prescribed and the blade (1/rev) flap and lag motions are obtained as a response to the aerodynamic and inertial forces. The helicopter control inputs and translational and attitude dynamics obtained from the model are validated with flight test data at various speeds and attitude. A linearized model is extracted based on a first-order Taylor series expansion of the nonlinear system about an equilibrium point for the purpose of determining the stability of the dynamic system, investigating sensitivity to gusts, and designing a model-based flight control system. Combined vision-based navigation and Linear Quadratic Regulator (LQR) for set-point tracking is used for disturbance rejection and tracking states. A rotatable camera is used for identifying the relative position of the helicopter with respect to the ship. Based on the position, a corresponding trajectory is computed. Considering the trade-off between transient responses and control efforts, gains for the LQR controller are chosen carefully and realistically. A fully autonomous flight is simulated from approach to landing on a ship. It consists of initial descent, steady forward flight, steady coordinated turn, deceleration, and final landing. Corresponding to each maneuver, relevant linearized model is used and gains are tuned. By using X-plane flight simulator program, the simulation data which include fuselage attitude and position at each time step are visualized with a single flight deck ship. This method allows an aircraft to land on a ship autonomously while maintaining high level of safety and accuracy without the need to capture the ship deck motions, however, by using a camera, and any other additional sensors, which will provide the accurate location of the ship relative to the helicopter. This method is not only relevant for a particular helicopter, but for any types of VTOL aircraft, manned or unmanned. Hence, it can improve the level of safety by preventing human errors that may occur during landing on a ship

Helicopter Autonomous Ship Landing System

This research focuses on developing a helicopter autonomous ship landing algorithm based on the real helicopter ship landing procedure which is already proven and currently used by Navy pilots. It encompasses the entire ship landing procedure from approach to landing using a pilot-inspired vision-based navigation system. The present thesis focuses on the first step towards achieving this overarching objective, which involves modeling the flight dynamics and control of a helicopter and some preliminary simulations of a UH-60 (Blackhawk) helicopter landing on a ship. The airframe of the helicopter is modeled as a rigid body along with rotating articulated blades that can undergo flap, lag and pitch motions about the root. A UH-60 helicopter is used for a representative model due to its ample simulation and flight test data. Modeling a UH-60 helicopter is based on Blade Element Momentum Theory (BEMT), rotor aerodynamics with the Pitt-Peters linear inflow model, empennage aerodynamics and rigid body dynamics for fuselage. For the blade dynamics, the cyclic (1/rev) and collective pitch motions are prescribed and the blade (1/rev) flap and lag motions are obtained as a response to the aerodynamic and inertial forces. The helicopter control inputs and translational and attitude dynamics obtained from the model are validated with flight test data at various speeds and attitude. A linearized model is extracted based on a first-order Taylor series expansion of the nonlinear system about an equilibrium point for the purpose of determining the stability of the dynamic system, investigating sensitivity to gusts, and designing a model-based flight control system. Combined vision-based navigation and Linear Quadratic Regulator (LQR) for set-point tracking is used for disturbance rejection and tracking states. A rotatable camera is used for identifying the relative position of the helicopter with respect to the ship. Based on the position, a corresponding trajectory is computed. Considering the trade-off between transient responses and control efforts, gains for the LQR controller are chosen carefully and realistically. A fully autonomous flight is simulated from approach to landing on a ship. It consists of initial descent, steady forward flight, steady coordinated turn, deceleration, and final landing. Corresponding to each maneuver, relevant linearized model is used and gains are tuned. By using X-plane flight simulator program, the simulation data which include fuselage attitude and position at each time step are visualized with a single flight deck ship. This method allows an aircraft to land on a ship autonomously while maintaining high level of safety and accuracy without the need to capture the ship deck motions, however, by using a camera, and any other additional sensors, which will provide the accurate location of the ship relative to the helicopter. This method is not only relevant for a particular helicopter, but for any types of VTOL aircraft, manned or unmanned. Hence, it can improve the level of safety by preventing human errors that may occur during landing on a ship

Helicopter Autonomous Ship Landing System

This research focuses on developing a helicopter autonomous ship landing algorithm based on the real helicopter ship landing procedure which is already proven and currently used by Navy pilots. It encompasses the entire ship landing procedure from approach to landing using a pilot-inspired vision-based navigation system. The present thesis focuses on the first step towards achieving this overarching objective, which involves modeling the flight dynamics and control of a helicopter and some preliminary simulations of a UH-60 (Blackhawk) helicopter landing on a ship. The airframe of the helicopter is modeled as a rigid body along with rotating articulated blades that can undergo flap, lag and pitch motions about the root. A UH-60 helicopter is used for a representative model due to its ample simulation and flight test data. Modeling a UH-60 helicopter is based on Blade Element Momentum Theory (BEMT), rotor aerodynamics with the Pitt-Peters linear inflow model, empennage aerodynamics and rigid body dynamics for fuselage. For the blade dynamics, the cyclic (1/rev) and collective pitch motions are prescribed and the blade (1/rev) flap and lag motions are obtained as a response to the aerodynamic and inertial forces. The helicopter control inputs and translational and attitude dynamics obtained from the model are validated with flight test data at various speeds and attitude. A linearized model is extracted based on a first-order Taylor series expansion of the nonlinear system about an equilibrium point for the purpose of determining the stability of the dynamic system, investigating sensitivity to gusts, and designing a model-based flight control system. Combined vision-based navigation and Linear Quadratic Regulator (LQR) for set-point tracking is used for disturbance rejection and tracking states. A rotatable camera is used for identifying the relative position of the helicopter with respect to the ship. Based on the position, a corresponding trajectory is computed. Considering the trade-off between transient responses and control efforts, gains for the LQR controller are chosen carefully and realistically. A fully autonomous flight is simulated from approach to landing on a ship. It consists of initial descent, steady forward flight, steady coordinated turn, deceleration, and final landing. Corresponding to each maneuver, relevant linearized model is used and gains are tuned. By using X-plane flight simulator program, the simulation data which include fuselage attitude and position at each time step are visualized with a single flight deck ship. This method allows an aircraft to land on a ship autonomously while maintaining high level of safety and accuracy without the need to capture the ship deck motions, however, by using a camera, and any other additional sensors, which will provide the accurate location of the ship relative to the helicopter. This method is not only relevant for a particular helicopter, but for any types of VTOL aircraft, manned or unmanned. Hence, it can improve the level of safety by preventing human errors that may occur during landing on a ship

Helicopter Autonomous Ship Landing System

This research focuses on developing a helicopter autonomous ship landing algorithm based on the real helicopter ship landing procedure which is already proven and currently used by Navy pilots. It encompasses the entire ship landing procedure from approach to landing using a pilot-inspired vision-based navigation system. The present thesis focuses on the first step towards achieving this overarching objective, which involves modeling the flight dynamics and control of a helicopter and some preliminary simulations of a UH-60 (Blackhawk) helicopter landing on a ship. The airframe of the helicopter is modeled as a rigid body along with rotating articulated blades that can undergo flap, lag and pitch motions about the root. A UH-60 helicopter is used for a representative model due to its ample simulation and flight test data. Modeling a UH-60 helicopter is based on Blade Element Momentum Theory (BEMT), rotor aerodynamics with the Pitt-Peters linear inflow model, empennage aerodynamics and rigid body dynamics for fuselage. For the blade dynamics, the cyclic (1/rev) and collective pitch motions are prescribed and the blade (1/rev) flap and lag motions are obtained as a response to the aerodynamic and inertial forces. The helicopter control inputs and translational and attitude dynamics obtained from the model are validated with flight test data at various speeds and attitude. A linearized model is extracted based on a first-order Taylor series expansion of the nonlinear system about an equilibrium point for the purpose of determining the stability of the dynamic system, investigating sensitivity to gusts, and designing a model-based flight control system. Combined vision-based navigation and Linear Quadratic Regulator (LQR) for set-point tracking is used for disturbance rejection and tracking states. A rotatable camera is used for identifying the relative position of the helicopter with respect to the ship. Based on the position, a corresponding trajectory is computed. Considering the trade-off between transient responses and control efforts, gains for the LQR controller are chosen carefully and realistically. A fully autonomous flight is simulated from approach to landing on a ship. It consists of initial descent, steady forward flight, steady coordinated turn, deceleration, and final landing. Corresponding to each maneuver, relevant linearized model is used and gains are tuned. By using X-plane flight simulator program, the simulation data which include fuselage attitude and position at each time step are visualized with a single flight deck ship. This method allows an aircraft to land on a ship autonomously while maintaining high level of safety and accuracy without the need to capture the ship deck motions, however, by using a camera, and any other additional sensors, which will provide the accurate location of the ship relative to the helicopter. This method is not only relevant for a particular helicopter, but for any types of VTOL aircraft, manned or unmanned. Hence, it can improve the level of safety by preventing human errors that may occur during landing on a ship

On the Complete Automation of Vertical Flight Aircraft Ship Landing

The current study focuses on developing an autonomous vertical flight aircraft ship landing system by directly automating the established Navy helicopter ship landing procedure. The central idea involves visually tracking a gyro-stabilized horizon bar installed on most Navy ships to approach and land vertically independent of deck motions. This was accomplished through the development of a rotorcraft flight dynamics modeling framework and vision-based control systems as well as conducting simulations and flight tests. The framework, named Texas A&M Rotorcraft Analysis Code (TRAC), was developed as a modular tool that could model any rotorcraft configuration at a low computational cost. A UH-60 helicopter was modeled as a baseline aircraft and validated using the US Army flight test data. A linear quadratic regulator (LQR) controller was utilized to stabilize and control the helicopter during autonomous ship landing simulations. The vision system was developed to obtain the visual information that a pilot perceives during ship approach and landing. It detects the ship at long-distance by utilizing machine/deep learning-based detection and at close range, it utilizes uniquely developed vision algorithms to detect the horizon bar to precisely estimate the aircraft position and orientation relative to the bar. It demonstrated 250 meters of detection range for a 6 x 6 ft sub-scale ship platform, which translates to a range of 17.3 kilometers for a full-scale 50 x 50 ft typical small ship. The distance and attitude estimations were validated using the measurements from an accurate 3D motion capture system (VICON), which demonstrated sub-centimeter and sub-degree accuracy. To control the aircraft based on the perceived visual information, both nonlinear control and deep reinforcement learning control strategies were developed. The nonlinear controller demonstrated robust tracking capability even with 0.5 seconds of time delay and estimation noise. When flight-tested in 5 m/s wind gust, the deep reinforcement learning control demonstrated superior disturbance rejection capability, with 50% reduced drift at a 3 times faster rate compared to conventional control systems. Both vision and control systems were implemented on a quadrotor unmanned aircraft and extensive flight tests were conducted to demonstrate accurate tracking in challenging conditions and safe vertical landing on a translating ship platform with 6 degrees of freedom motions

Intelligent Vision-based Autonomous Ship Landing of VTOL UAVs

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

Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs

This paper addresses the problem of developing an algorithm for autonomous ship landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs), using only a monocular camera in the UAV for tracking and localization. Ship landing is a challenging task due to the small landing space, six degrees of freedom ship deck motion, limited visual references for localization, and adversarial environmental conditions such as wind gusts. We first develop a computer vision algorithm which estimates the relative position of the UAV with respect to a horizon reference bar on the landing platform using the image stream from a monocular vision camera on the UAV. Our approach is motivated by the actual ship landing procedure followed by the Navy helicopter pilots in tracking the horizon reference bar as a visual cue. We then develop a robust reinforcement learning (RL) algorithm for controlling the UAV towards the landing platform even in the presence of adversarial environmental conditions such as wind gusts. We demonstrate the superior performance of our algorithm compared to a benchmark nonlinear PID control approach, both in the simulation experiments using the Gazebo environment and in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship platform undergoing 6 degrees of freedom (DOF) deck motion

Real-Time Detection of Nitric Oxide Release in Live Cells Utilizing Fluorinated Xerogel-Derived Nitric Oxide Sensor

Nitric oxide (NO) is an important signaling molecule that regulates a diverse range of physiological and cellular processes in many tissues. Therefore, the accurate detection of physiological NO concentration is crucial to the understanding of NO signaling and its biological role. There has been growing interest in the development of electrochemical sensors for direct and real-time monitoring of NO. As the direct electrooxidation of NO requires a relatively high working potential, further surface modification with permselective membranes is required to achieve the desired selectivity for NO via size exclusion or electrostatic repulsion. Here we reported a planar-type NO sensor with a fluorinated xerogel-derived gas permeable membrane for real-time detection of NO release in live cells. First, we evaluated the biocompatibility of xerogel-derived NO permeable membranes modified with fluorinated functional groups by growing RAW 264.7 macrophages on them. And we performed the AFM measurements to examine the morphology of RAW 264.7 macrophages on xerogel membrane. Finally, we successfully detected NO release in RAW 264.7 macrophages, using a planar-type xerogel-derived NO sensor. As a result, fluorinated xerogel-derived membrane could be utilized as both NO permeable and cell-adhesive membranes. Besides, planar-type xerogel-based NO sensors can be easily applied to the cellular sensing system, with a simple coating procedure

Helicopter Autonomous Ship Landing System

This research focuses on developing a helicopter autonomous ship landing algorithm based on the real helicopter ship landing procedure which is already proven and currently used by Navy pilots. It encompasses the entire ship landing procedure from approach to landing using a pilot-inspired vision-based navigation system. The present thesis focuses on the first step towards achieving this overarching objective, which involves modeling the flight dynamics and control of a helicopter and some preliminary simulations of a UH-60 (Blackhawk) helicopter landing on a ship. The airframe of the helicopter is modeled as a rigid body along with rotating articulated blades that can undergo flap, lag and pitch motions about the root. A UH-60 helicopter is used for a representative model due to its ample simulation and flight test data. Modeling a UH-60 helicopter is based on Blade Element Momentum Theory (BEMT), rotor aerodynamics with the Pitt-Peters linear inflow model, empennage aerodynamics and rigid body dynamics for fuselage. For the blade dynamics, the cyclic (1/rev) and collective pitch motions are prescribed and the blade (1/rev) flap and lag motions are obtained as a response to the aerodynamic and inertial forces. The helicopter control inputs and translational and attitude dynamics obtained from the model are validated with flight test data at various speeds and attitude. A linearized model is extracted based on a first-order Taylor series expansion of the nonlinear system about an equilibrium point for the purpose of determining the stability of the dynamic system, investigating sensitivity to gusts, and designing a model-based flight control system. Combined vision-based navigation and Linear Quadratic Regulator (LQR) for set-point tracking is used for disturbance rejection and tracking states. A rotatable camera is used for identifying the relative position of the helicopter with respect to the ship. Based on the position, a corresponding trajectory is computed. Considering the trade-off between transient responses and control efforts, gains for the LQR controller are chosen carefully and realistically. A fully autonomous flight is simulated from approach to landing on a ship. It consists of initial descent, steady forward flight, steady coordinated turn, deceleration, and final landing. Corresponding to each maneuver, relevant linearized model is used and gains are tuned. By using X-plane flight simulator program, the simulation data which include fuselage attitude and position at each time step are visualized with a single flight deck ship. This method allows an aircraft to land on a ship autonomously while maintaining high level of safety and accuracy without the need to capture the ship deck motions, however, by using a camera, and any other additional sensors, which will provide the accurate location of the ship relative to the helicopter. This method is not only relevant for a particular helicopter, but for any types of VTOL aircraft, manned or unmanned. Hence, it can improve the level of safety by preventing human errors that may occur during landing on a ship