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Example of scanning lidar data. The right image shows the data overlaid with the ship deck ground truth model.
Source publication
Landing rotorcraft on a ship deck is a difficult and dangerous task. The US Navy is interested in expanding landing capabilities in degraded visual environments, with impaired or no GPS signal, and in autonomous operations, while at the same time reducing the cost of guidance infrastructure on the ship deck. This paper describes how a suite of mult...
Context in source publication
Context 1
... will be able to provide wide field of view range and bearing measurements to reflective deck markers with centimeter-level accuracy. A sample lidar output is shown in Figure 3. Most lidars operate in the near-IR band, which allows them better performance then visible light sensors in DVE conditions. ...
Similar publications
A system for rotorcraft dual lift was developed and flight-tested using two Yamaha RMAX helicopters equipped with customized avionics and sling–load hardware. Important features of the system were tested, including precision dual-lift maneuvering, load tension equalization, and load motion damping. Flight-test results are presented that include aut...
A system for dual-lift research was developed and flight tested using two Yamaha RMAX helicopters equipped with customized avionics and sling-load hardware. Important features of the system were tested including precision dual-lift maneuvering, load tension equalization, and load motion damping. Result are presented from flight testing that include...
Citations
... Helicopter maritime operations, especially deck landings differ from land-based ones (Horn and Bridges, 2007;Grocholsky et al., 2016;Frost et al., 2021) and are performed according to the preselected procedures (Arora et al., 2013). According to Anonymous (2003), six navy helicopter-ship operations can be distinguished: fore/aft procedure, relative wind or into wind procedure, cross-deck procedure, aft/fore or facing astern procedure, astern procedure and oblique procedure. ...
Purpose
The purpose of this study is to test the performance of the designed automatic control system based on the Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) algorithms during landing of the helicopter on the ship deck. This paper is a further development of the series based on Topczewski et al. (2020).
Design/methodology/approach
The system consists of two automatic control algorithms based on LQR and the LQG. It is integrated with the ship motion prediction system based on autoregressive algorithm with parameters calculated using Burg’s method. It is assumed that the source of necessary navigation data is integrated Inertial Navigation System with Global Positioning System. Landing of the helicopter on the ship deck is performed in automatic way, based on the preselected procedure. Performance of the control system is analyzed when all necessary navigation data is available for the system and in case when one of the parameters is unavailable during performing the procedure.
Findings
In this paper, description of the designed control system developed for performing the approach and landing of the helicopter using selected procedure is presented. Helicopter dynamic model is validated using the manufacturer data and by test pilots, overview is presented. Necessary information about ship motion model is also included. Tests showing mission performance while using LQR and LQG algorithms applied to the control system are presented and analyzed, taking into account both situations when full navigation data is available/unavailable for the control system.
Practical implications
Results of the system performance analyses can be used for selection of the proper control methodology for prospective helicopters autopilots. Furthermore, the system can be used to analyze the mission safety when information about one of the navigation parameters is identified by the navigation system as unavailable or incorrect and therefore unavailable during landing on the ship deck.
Originality/value
In this paper, control system dedicated for the automatic landing of the helicopter on the ship deck, based on two different control algorithms is presented. Influence of lack of information about one of the navigation parameters on the mission performance is analyzed.
... In particular, V achieved was partly determined by the future state of the ship at helicopter touchdown. The future state of the ship can be estimated based on the current state 41 . Under realistic conditions, expert pilots combined the vertical displacements of their helicopter with the heave motion of the ship prior to deck-landing. ...
When attempting to land on a ship deck tossed by the sea, helicopter pilots must make sure that the helicopter can develop sufficient lift to be able to safely touchdown. This reminder of affordance theory led us to model and study the affordance of deck‑landing‑ability, which defines whether it is possible to land safely on a ship deck depending on the helicopter’s available lift and the ship’s deck heave movements. Two groups of participants with no piloting experience using a laptop helicopter simulator attempted to land either a low‑lifter or a heavy‑lifter helicopter on a virtual ship deck by either triggering a pre‑programmed lift serving as the descent law if it was deemed possible to land, or aborting the deck‑landing maneuver. The deck‑landing‑ability was manipulated by varying the helicopter’s initial altitude and the ship’s heave phase between trials. We designed a visual augmentation making visible the deck‑landing‑ability, and thus enabling participants to maximize the safety of their deck‑landing attempts and reduce the number of unsafe deck‑landing. The visual augmentation presented here was perceived by participants as a means of facilitating this decision‑making process. The benefits were found to have originated from the clear‑cut distinction it helped them to make between safe and unsafe deck‑landing windows and the display of the optimal time for initiating the landing
... From the extracted feature points the relative pose of the UAV is estimated [17]. Though this method of landing with the help of markers is efficient and reliable, they can only be applied when the desired landing areas/spots are known beforehand, such as in case when landing is to be made on ship deck [13] or on a moving mobile robot platform [10] In [3], [19], for extracting planar region boundaries, primarily the Euclidean 3D geometry of the place is recovered and then evaluated. This work mainly relies on the calculation of the basic matrix and, for refining the scene reconstruction, there is often requirement of a batch process. ...
A general rule of flying is applicable when developing an aircraft system and the same rule is applicable on Unmanned Aerial Vehicles (UAVs) as well when developing an intelligent autonomous flying machine. The rule states, "Take-off is optional but landing is necessary and safe landing in emergency conditions is mandatory." However, in case of a manned aircraft, human intelligence plays a major role in safe landing of vehicle, but in case of autonomous UAV, it is the intelligence of the machine, which marks for safe landing. Hence, this work presents a UAV safe landing system using computer vision algorithm. We have proposed a novel tiny version of existing Speeded Up Robust Features (SURF) algorithm that reduces the computational complexity without compromising the detection of safe landing areas in a given frame. The observation suggests that results obtained from proposed Tiny-SURF are well suited for solving the problem.
... The system is integrated with other helicopter control systems. In [3] navigation and control of a helicopter in approach to landing is presented, focused on application of visual navigation assuming lack of the Global Positioning System (GPS) signal, but also taking into account poor visibility conditions. A three-phase trajectory planner is developed, which uses models of a helicopter and vessel dynamics. ...
... • helicopter position in the inertial coordinate system (3), • helicopter roll, pitch and yaw angles (3), • helicopter velocities in the body coordinate system (3), • helicopter roll, pitch and yaw rates (3). The rest of the states (main rotor induced velocity components, each blade flapping angles, each blade lag angles) are included in the linear model as uncontrolled variables, but they affect generating the LQR gain matrix K. ...
The paper presents a control algorithm for a helicopter automatic approach and landing on a moving confined platform. It discusses landing on a sea vessel deck as a representative case for a mobile confined area. The dynamic model of a single rotor helicopter with a control system, developed in the FLIGHTLAB environment, and validated against flight tests data, is used to investigate control efficiency. The developed control method is based on the Linear Quadratic Regulator combined with prediction of motion of the landing area. An important part of the research was analysis of availability of the data needed for controlling the rotorcraft. The simulations of approach and landing on a moving vessel in various environmental conditions confirmed the efficiency of the developed control methodology.
Autonomous or manual aerial vehicles should be able to land safely after conducting missions. While human pilots can determine safe landing spots for manned or remote-controlled aerial vehicles, unmanned aerial vehicles (UAVs) need to autonomously evaluate their surrounding environments to land safely. In this article, we present fully autonomous strategies for searching for safe landable spots and landing. This approach combines sensor readings from a camera with light detection and rangings (LiDARs) data. The class-wise complementary criteria enables safe landable regions to be determined, based on slope extraction from the LiDAR points cloud and semantic segmentation from deep learning using camera images. All the required components including algorithms, heterogeneous sensors, and processors were implemented on a multirotor UAV for standalone operation. Real-time outdoor experiments demonstrated fully autonomous search and landing on safe spots in various environments that included water, grass, trees, and shadows.
Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natural disasters such as earthquakes or gas explosions. However, these UAVs have to be able to autonomously navigate in disaster struck environments and land on debris piles in order to accurately locate the survivors. This problem is extremely challenging as pre-existing maps cannot be leveraged for navigation due to structural changes that may have occurred. Furthermore, existing landing site detection algorithms are not suitable to identify safe landing regions on debris piles. In this work, we present a computationally efficient system for autonomous UAV navigation and landing that does not require any prior knowledge about the environment. We propose a novel landing site detection algorithm that computes costmaps based on several hazard factors including terrain flatness, steepness, depth accuracy, and energy consumption information. We also introduce a first-of-a-kind synthetic dataset of over 1.2 million images of collapsed buildings with groundtruth depth, surface normals, semantics and camera pose information. We demonstrate the efficacy of our system using experiments from a city scale hyperrealistic simulation environment and in real-world scenarios with collapsed buildings.
