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Space navigation has been a research topic for decades, with autonomy within its remit being a challenging area. Conventional space navigation approaches are based on utilizing the signal from ground facilities on the earth or using navigation sensors with sophisticated methodologies. Hence, this paper proposes a simple space autonomous navigation system that utilizes the angle measurements obtained by three image sensors. A sensitivity analysis is performed to rigorously investigate the relationship between the sensors’ performance and navigation error. A nonlinear Kalman filter is designed to improve navigation performance due to sensor errors. The performance of the proposed system is verified via nonlinear numerical simulations and Monte-Carlo simulation with sensor errors.

This paper aims to reveal the hidden physical meaning or the working principle of the three-loop autopilot. First, the minimal control structure that ensures the desired dynamic characteristics is analyzed. The results show that the three-loop autopilot has a redundant feedback loop from the minimal control structure standpoint. Based on this observation, its physical meaning is interpreted by utilizing the closed-loop tracking error dynamics in the presence of aerodynamic uncertainties. It turns out that the three-loop autopilot has the minimal control structure with an additional command which is based on the instantaneous direct model reference adaptive control. A clear mechanism of how the aerodynamic uncertainties are compensated is provided in the three-loop autopilot. Finally, numerical simulations are performed to validate our findings. The analysis results in this paper show the beauty of the three-loop autopilot: it can be considered as the most concise form to achieve desired dynamic characteristics and counteract model uncertainties. The potential importance of the results obtained is that they allow us to properly design the three-loop autopilot by reflecting its physical meaning and have potential directions to improve the performance of the three-loop autopilot as well as the adaptive control.

The increasing use of Unmanned Aerial Vehicles (UAVs) in safety-critical missions in both civilian and military areas demands accurate and reliable navigation, where one of the key sources of navigation information is presented by Global Navigation Satellite Systems (GNSS). In challenging conditions, for example, in urban areas, the accuracy of GNSS-based navigation may degrade significantly due to user-satellite geometry and obscuration issues without being noticed by the user. Therefore, considering the essentially dynamic rate of change in this type of environment, integrity monitoring is of critical importance for understanding the level of trust we have in positioning and timing data. In this paper, the dilution of precision (DOP) coefficients under nominal and challenging conditions were investigated for the purpose of integrity monitoring in urban environments. By analyzing positioning information in a simulated urban environment using a software-based GNSS receiver, the integrity monitoring approach based on joint consideration of GNSS observables and environmental parameters has been proposed. It was shown that DOP coefficients, when considered together with a number of visible satellites and cut-off elevations specific to the urban environment carry valuable integrity information that is difficult to get using existing integrity monitoring approaches. This has allowed generating indirect integrity measures based on cut-off elevation and satellite visibility that can be used for UAV path planning and guidance in urban environments.

An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches. The aim of the proposed system is that an ally UAV can either avoid or track an unexpected enemy UAV with a net to protect itself. In this point of view, a simple and robust target detection, tracking and avoidance system is designed. Two open-source tools were used for the aim: a state-of-the-art object detection technique called SSD and an API for MAVLink compatible systems called MAVSDK. The MAVSDK performs velocity control when a UAV is detected so that the manoeuvre is done simply and efficiently. The proposed system was verified with Software in the loop (SITL) and Hardware in the loop (HITL) simulators. The simplicity of this algorithm makes it innovative, and therefore it should be used in future applications needing robust performances with low-cost hardware such as delivery drone applications., IEEE RED-UAS 2019 Conference

This paper aims to propose a short course with a commercial off the shelf unmanned aerial vehicle (COTS UAV) system. From the short course, students can have a conception of a UAV system, and they would have not only the knowledge about the hardware integration but also the theoretical background of the guidance, navigation, and control (GNC) and the situation awareness system. The proposed course consists of two parts; the GNC system for holding the position and the situation awareness system with the marker detection and tracking. A Pixhawk is selected for a flight controller with an open source autopilot, i.e. px 4, and a Raspberry Pi with a downward camera is utilised for the visual navigation of the situation awareness system. The Pixhawk and Raspberry Pi are integrated into the robot operating system (ROS) via the WIFI network, and the MAVROS is adopted for the communication between the Pixhawk and the Raspberry Pi. The first part of the course is designed as the hands-on based lectures, and the second part of the course is adopted the problem-based project.

There has been a recent surge in interest regarding small Unmanned Aerial Vehicles (UAV) that operate in indoor environments. A crucial part of such platforms is the navigation system that allows them to navigate in Global Navigation Satellite System (GNSS) denied environments. The two objectives of this work were to develop an Optical Flow (OF) based method for navigation and a high-fidelity simulation for testing and verification. This paper presents the Visual-Inertial Navigation System (VINS) developed that utilises sparse and dense Optical Flow patterns within an Extended Kalman Filter (EKF) framework for autonomous navigation in GNSS denied environments. The principal novelty is the use of dense Optical Flow measurements to estimate the velocity of the UAV.

This paper presents a multi-agent mission planning and task allocation framework designed to coordinate autonomous aerial vehicles engaged in a competition scenario. The development was a part of an inter-university UAV Swarm competition that was supported by BAE Systems. The proposed centralised system was developed with the main objectives of robustness and scalability. The system consists of a general mission planning module which decomposes the overall mission into identified sub-stages to achieve the overall mission goal. In order to enable autonomous defence actions a dynamic task allocation approach is proposed. The dynamic task allocation is using received information of detected enemies and utilises the information for a further combinatorial optimisation problem. In this work, we discuss the structure of the framework and present results obtained in a high-fidelity simulation environment. Moreover, a comparative study of the performance of three different optimization algorithms for the given combinatorial problem, namely Kuhn-Munkres, Jonker-Volgenant and Gale-Shapley, implemented in the system is included. The results demonstrate that the best allocation result performances, in terms of minimal costs, are obtained with utilising, both Kuhn-Munkres or Jonker-Volgenant methods, while the Gale-Shapley algorithms have benefits in terms of time efficiency for cases in which minimal costs are not the highest priority.

A novel design of amphibious UAV with a swimming-ring shaped floater that can inflate before landing and deflate after taking off is presented. An air pump system is developed to control the floater and is integrated in the whole flight control system. The mechanism of such system is discussed, while simulations in MATLAB and Simulink carried out for a UAV with simple missions and trajectories are shown. Results illustrate the feasibility of using the proposed design within the remit of water landing UAVs.

This paper presents an integration approach to deploy new mission planning methods for multi-vehicle systems within an open source ground control system (GCS), i.e. QGroundControl. As the autonomous mission planning features of QGroundControl consist in pre-planning waypoints within a geofence, there is no way to deal with scenarios where the agents of the swarm must counter moving targets of reduced size, or more generally any scenario where an algorithm of path planning is preferred instead of a manual planification. The proposed solution consists in extending the ”toolbox” of the application (QGCToolbox) with a new tool (QGCTool), in implementing a new controller and modifying views (FlightDisplayViewMap, PlanView) while enabling information to flow between components using Qt’s signals and slots. A test scenario is presented to verify new mission planning interface and path planning algorithm, that involves in this case eight UAVs operating in a zone to protect and an area to attack. Also a workaround is proposed to increase the default limitation of connections to QGroundControl. The proposed methodology is tested and validated with software in the loop and flight test.

This paper proposes an autopilot design for an interceptor with Thrust-Vector-Control (TVC) that operates in the endo- and exo-atmospheric regions. The main objective of the proposed autopilot design is to ensure control performance in both atmospheric regions, without changing the control mechanism. In this paper, the characteristics of the aerodynamic forces in both atmospheric regions are first investigated to examine the issue of the conventional control mechanism at various altitudes. And then, a control mechanism, which can be applied to both atmospheric regions, is determined based on the analysis results. An autopilot design is then followed by utilizing the control mechanism and the feedback linearization control (FBLC) method. Accordingly, the proposed autopilot does not rely on changing the control mechanism depending on flight condition unlike the conventional approach as well as it can adjust the control gains automatically according to the changes of flight operating conditions. In this paper, the robustness of the proposed autopilot is investigated through the tracking error analysis and the relative stability analysis in the presence of model uncertainties. The physical meaning of the proposed autopilot is also presented by comparing to the well-known three-loop control structure. Finally, numerical simulations are performed to show the performance of the proposed method.

A weapon control algorithm equipped on a fighter is closely related to the mission accomplishment and fighter survivability during the engagement. In the case of a air-to-surface missile, the weapon control algorithm typically provides a pilot the target shoot-down possible region known as launch acceptability region(LAR) in the multi function display(MFD). LAR is produced by the range table(RT) through computation of an engagement range. In this paper, the operation system of AGM-84 and AGM-88 air-to-surface missiles is introduced. And the engagement range computation and LAR algorithm based on the real-time pseudo 6-DOF simulation are proposed. In order to verify the performance of the algorithm, numerical engagement simulations of air-to-surface missiles to produce LAR have been done.

In this paper, a roll angle estimation method of a rolling airframe using a low grade GPS and a roll rate gyro is proposed. The strength of the received signal of the GPS antenna attached on the rolling airframe is maximized when the GPS satellite is placed on the plane determined by the x-axis of the rolling airframe and the GPS antenna axis. Under the assumption that the x-axis of the rolling airframe is coincident with its velocity vector, the roll angle of the rolling airframe is calculated from the relative position vector of the satellite to the GPS when the GPS signal strength becomes maximum. The Kalman filter combined with a roll rate gyro is introduced to increase the determination accuracy of the roll angle. The performance of the proposed method is verified via 6-DOF simulations.

The roll maneuvering of the missile is required in order for the two-axes gimbal seeker mounted on the nose cone platform of the missile to track the target continuously. If the missile performs roll maneuvering, the coupling of angular rate occurs along the longitudinal/lateral axis of the missile body, making it difficult to measure the Line-of-Sight(LOS) rate due to the distortion of the seeker image. To obtain accurate LOS rate, it is necessary to compensate the rotations of the gimbal seeker and body angular rate of the missile. In this paper, the LOS rate equations are derived considering the angular motions of the gimbal seeker and missile. It is verified that the LOS rate of derived equation is equal to the true LOS rate through the nonlinear Six-Degree-of-Freedom(6-DOF) simulation. From the simulation results, we analyzed the effect of roll maneuvering on the LOS rate when the gimbal seeker is mounted on the nose cone platform.

A weapon control algorithm equipped on a fighter is closely related to the mission accomplishment and fighter survivability during engagement. The weapon control algorithm typically provides a pilot the dynamic launch zone(DLZ), the target shoot-down range of air-to-air missiles, in the head-up display(HUD). DLZ is produced by an engagement range computation algorithm. In this paper, the components of DLZ for AIM-9 and AIM-120 air-to-air missiles are introduced. The real-time computation algorithm for DLZ based on the pseudo 6-DOF program is then addressed The operational aspects of DLZ algorithm for the air-to-air missiles to various engagement scenarios is investigated vis simulations.

For a missile with a strapdown seeker, line-of-sight rate for guidance is obtained by compensating the look angle rate from the strapdown seeker by the body angular rate from rate gyros. However, the body angular rate from rate gyros has different signal properties when it compared to the body angular rate implicitly included in the look angle rate. Typically this discrepancy causes instability of homing loop. In this paper, we propose a design method of homing loop where seeker delay is compulsively placed in the output signal of the rate gyros for accordance of both body rates. Also, PID control loop is considered for obtaining stabilized guidance command even though uncertainties of seeker delay is associated. The stability analysis for the linear homing loop before and after the compensation has been done. The stability and performance of the designed terminal homing loop is verified through full nonlinear 6-DOF simulations.

This paper proposes a determination method of waypoints to maximize the survivability of a UAV. Voronoi diagram which is used for the initial selection of waypoint candidates is the most widely used path planning technique to avoid the threat as far as possible when the location and strength of the threat are given. But if threat strength is different each other and flight path is constrained along with straight lines, Voronoi diagram has limitations in real applications. In this study, the initial waypoints obtained from Voronoi diagram are optimized considering the shape of each threat. Here, a waypoint is optimized while adjacent waypoints are fixed. By repeating this localized optimization until whole waypoints are converged, computation time for finding the best waypoints is greatly reduced.

In order to intercept anti-ship missiles, it is important to accurately predict the aiming point. The major factor for degrading the accuracy of the aiming point is the motions of the warships due to waves. Therefore, a stage of correcting the aiming point is required to compensate for such motions ...

Satellite orbits are usually estimated by employing a filter system model whose state variables are the position and the velocity defined on the earth-fixed inertial (ECI) rectangular coordinate system. However, this filter formulation causes estimation errors due to nonlinearity of the measurement if the two-line element (TLE) data in which satellite's information is provided by orbital parameters. In this paper, we propose a filtering method by adopting orbital parameters as state variables to use TLE data directly. Here, we introduce Kozai's equations of orbital motion including gravitation perturbation terms for the propagation model so that more precise orbit determination can be possible. The performance of the proposed filter is verified via numerical simulations.

In this paper, a satellite celestial navigation which estimates the satellite position on the orbit is proposed. It consists attitude sensor with star-tracker and earth sensor. The proposed algorithm is based on the theory that the position on the surface is determined in the case of having a different observation angle for each star at zenith. The performance of the proposed algorithm is examined via simulation considering error parameters of attitude sensor. The proposed satellite celestial navigation algorithm can estimate the satellite position on the orbit in real time, since it obtains the observation angle and altitude directly by using the attitude sensor.

Hand posture classification is popular in systems that require an effective human-machine interface. Previous classification algorithms suffer from inaccurate results when it is difficult to distinguish a hand from a wrist. To overcome this difficulty, this paper proposes a new algorithm for hand posture classification that uses a histogram of convex defects around the segment of a hand to be classified. As the characteristics of convex defects do not vary significantly depending on inclusion of a wrist, the proposed algorithm does not suffer substantially from a reduced classification accuracy. Furthermore, the proposed algorithm is also rotation-invariant. Experimental results show that the correct classification ratio is 97.06% on average.