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Future exploration and human missions on large planetary bodies (e.g., moon, Mars) will require advanced guidance navigation and control algorithms for the powered descent phase, which must be capa...

This survey paper presents a holistic perspective on the state-of-the-art in the design of guidance, navigation, and control systems for autonomous multi-rotor small unmanned aerial systems (sUAS). By citing more than 300 publications, this work recalls fundamental results that enabled the design of these systems, describes some of the latest advances, and compares the performance of several techniques. This paper also lists some techniques that, although already employed by different classes of mobile robots, have not been employed yet on sUAS, but may lead to satisfactory results. Furthermore, this publication highlights some limitations in the theoretical and technological solutions underlying existing guidance, navigation, and control systems for sUAS and places special emphasis on some of the most relevant gaps that hinder the integration of these three systems. In light of the surveyed results, this paper provides recommendations for macro-research areas that would improve the overall quality of autopilots for autonomous sUAS and would facilitate the transition of existing results from sUAS to larger autonomous aircraft for payload delivery and commercial transportation.

Disaster management has become a research area in its own right, with few reported cases of successful installation of robots in real disaster areas. Most of these disasters are using air, ground, or underwater platforms. However, studies involving private boats or Unmanned Surface Vehicles (USV’s) for Disaster Management (DM) are presently being distributed in many publications, of varying depths, and focuses mainly on single-seat vehicles - usually under the banned Ocean umbrella. Therefore, the current value of USVs in the DM process at its different stages is unclear. This paper presents the first comprehensive study on the use of USV DMs, as far as we know. This work shows that with few current issues in disaster situations, most research in the area focuses on USV Hardware and software platforms, such as Guidance Navigation and Control, do not focus on their actual DM value. Finally, to guide future researches, this paper also summarizes our contributions towards it, the lessons learned by everyone, the guidelines made by us, and the research gaps.

Saturn’s moon Enceladus is one of the most promising candidates in the solar system for hosting microbial life. Plumes emanating from its south pole are ejecting water directly from the ocean to sp...

Hayabusa2 is a Japanese sample return mission from the asteroid Ryugu. The Hayabusa2 spacecraft was launched on 3 December 2014 and arrived at Ryugu on 27 June 2018. It stayed there until December 2019 for in situ observation and soil sample collection, and will return to the Earth in November or December 2020. During the stay, the spacecraft performed the first touchdown operation on 22 February 2019 and the second touchdown on 11 July 2019, which were both completed successfully. Because the surface of Ryugu is rough and covered with boulders, it was not easy to find target areas for touchdown. There were several technical challenges to overcome, including demanding guidance, navigation, and control accuracy, to realize the touchdown operation. In this paper, strategies and technical details of the guidance, navigation, and control systems are presented. The flight results prove that the performance of the systems was satisfactory and largely contributed to the success of the operation.

The Solar System features thousands of Near-Earth Asteroids that could be at collision risk with our planet in the future. Scientists are investigating the possibility of deflecting asteroids from their trajectory by means of a hyper-velocity impactor spacecraft. The aim of this research is to develop and simulate a GNC strategy to control the spacecraft towards its impact with the asteroid. The navigation is based on the use of a camera to estimate the relative position through image analysis and a filtering process. A zero-effort error strategy is adopted for the control. A simulator has been developed to render the simulated images online and test the GNC algorithms. The simulator is used to assess the performance of the strategy on different scenarios and to perform a sensitivity analysis with respect to the environmental and design parameters.

In this paper, comprehensive pulsar-based Guidance, Navigation and Control (GNC) system is designed and applied to satellites formation flying. The complete autonomy of the X-ray pulsar navigation technology provides both absolute and relative positioning information for spacecraft in or even beyond the solar system, and provides an interesting alternative solution to classical navigation techniques such as Global Navigation Satellite System (GNSS). The navigation measurements are studied using a relative time of arrival (RTOA) estimating method, which takes advantage of the noise-filtered pulsar profiles in the cross-correlation estimator to achieve robustness. An adaptive Kalman filter is exploited to minimise the effect of processing noises caused by the primary satellite. A linear Quadratic Gaussian regulator is then applied to control the formation. Two different configurations are studied in high Earth orbits (HEOs). Simulation results show that the designed GNC algorithms can fulfill the required accuracy (10% of the baseline) of the Magnetospheric Multiscale Mission, with the position root-mean-square error of ~ 1.6 km. . The excellent robustness of the designed GNC system on the positioning errors of the primary satellite suggests a role for X-ray pulsar-based formation flying technique in HEOs and the solar system.

Pose estimation is critical for mobile robots to fulfill various tasks, such as path following or mapping the environment. This is usually accomplished by simultaneous localization and mapping (SLAM). However, computationally constrained systems, such as planetary rovers, rely on less intensive guidance navigation and control (GNC) solutions generally derived solely from visual odometry (VO), wheel odometry, and the onboard inertial measurement unit. Although providing adequate localization performances, the drift accumulated overtime is not compensated by loop closing capabilities, typical of SLAM. Usually, rovers send surface images to the ground station, and these images are used for multiple purposes, such as scientific and operational planning. The number of images is constrained by the communication bandwidth and power budget. The set of transmitted images can be used as a means to correct the robot’s trajectory in an off-line manner. In this work, a solution is presented to the problem of selecting the optimal set of viewpoints belonging to the planned path from which to capture and transmit images: 1) it guarantees accurate trajectory correction and 2) complies with the maximum number of images that can be transmitted to ground control given the available data budget. To this end, it is proposed: 1) a delocalized/decentralized sensor fusion approach based on pose graph optimization and structure from motion and 2) a strategy to select a minimal set of viewpoints along the trajectory that, given a tentative geometry of the environment and the global path that the rover must follow, minimizes the uncertainty of all the robot poses. Optimal camera viewpoint positions are selected as a function of the planned trajectory, the approximate scene geometry, and the maximum transmittable number of images. The proposed method has been tested on a dataset of stereo-images collected in a representative Martian environment, the ALTEC Mars Terrain Simulator (MTS), with the ExoMars testing rover (ExoTeR—European Space Agency, Paris, France, property). Rover stereo-images ground truth was given with millimetric accuracy by a motion capture (MC) system.

A Modelica model of a of a rocket's first stage is developed, designed to be representative of the launch vehicles in use in the United States in the late 2010s. The model uses initial conditions similar to those observed immediately after a second stage separation at 166km altitude. A control system is developed enabling the rocket first stage to land back on Earth's surface at a predetermined landing pad in a controlled manner. The control system is evaluated based on its ability to compensate for altered initial conditions, as well as its ability to minimize acceleration forces and fuel consumption. The flight path of the simulated first stage rocket is compared to real-life telemetry data from a first stage rocket landing showing a similar trajectory.

Aerospace vehicles are vulnerable to hardware and software faults that lead to mission-critical failures. Advances in onboard fault protection capability are necessary as both terrestrial and space...

Performing rendezvous in close-proximity of a non-cooperative target is a challenging task, especially if certain performance requirements have to be met. When maneuvering in hazardous scenarios in order to accomplish either refuelling, inspection, repair or dismissal tasks, optimality and safety are relevant aspects to be sought. Additionally, autonomy is essential in unmanned missions. All these aspects can be achieved through purposely intended Guidance Navigation and Control (GNC) architectures. Optimality is obtained by means of the guidance system, that is by a proper optimal trajectory calculation. Safety and accuracy are guaranteed by evaluation of target's relative pose and shape, which is exploited by the navigation system. Among all the possible choices, optical hardware is becoming widely studied for its high accuracy and capability to reconstruct three-dimensional properties of the observed scene. The present work investigates the reciprocal influences between the guidance and navigation subsystems, putting higher focus on how the performance of an optimal rendezvous maneuver with a non-cooperative target can be influenced by the accuracy reached in the pose and shape estimation process. To this purpose, the chaser, or maneuvering spacecraft is considered to be equipped with a single camera and a distance sensor, as this architecture both provides high accuracy and meets mass, power and volume requirements to be implemented on-board a small platform. An algorithm including the optimization process and a purposely built filter, capable of receiving images as inputs and providing evaluation of target's relative pose and shape as output, is implemented and tested in several different scenarios to validate the feasibility of the maneuver.

The autonomous guidance, navigation, and control (GNC) system is an essential component of a spacecraft. It serves the purposes of determining the attitude and orbital parameters of the spacecraft, guiding the spacecraft to move on the intended orbit or towards the desired target, and controlling the attitude and orbit of the spacecraft according to mission requirements without ground-station support.

Some problems on in-flight verification of the navigation, guidance and control systems for the information satellites and space robots are considered. Digital algorithms for reliable control of these spacecraft and obtained results are presented.

Objectives Intraprocedural deployment of endovascular devices during complex aortic repair with 2-dimensional (2D) x-ray fluoroscopic guidance poses challenges in terms of accurate delivery system positioning and increased risk of x-ray radiation exposure with prolonged fluoroscopy times, particularly in unfavorable anatomy. The objective of this study was to assess feasibility of using an augmented reality (AR) system to position and orient a modified aortic endograft delivery system in comparison with standard fluoroscopy. Materials and Methods The 3-dimensional guidance, navigation, and control (3D-GNC) prototype system was developed for eventual integration with the Intra-Operative Positioning System (IOPS, Centerline Biomedical, Cleveland, OH) to project spatially registered 3D holographic representations of the subject-specific aorta for intraoperative guidance and coupled with an electromagnetically (EM) tracked delivery system for intravascular navigation. Numerical feedback for controlling the endograft landing zone distance and ostial alignment was holographically projected on the operative field. Visualization of the holograms was provided via a commercially available AR headset. A Zenith Spiral-Z AAA limb stent-graft was modified with a scallop, 6 degree-of-freedom EM sensor for tracking, and radiopaque markers for fluoroscopic visualization. In vivo, 10 interventionalists independently positioned and oriented the delivery system to the ostia of renal or visceral branch vessels in anesthetized swine via open femoral artery access using 3D-GNC and standard fluoroscopic guidance. Procedure time, fluoroscopy time, cumulative air kerma, and contrast material volume were recorded for each technique. Positioning and orientation accuracy was determined by measuring the target landing-zone distance error (δLZE) and the scallop-ostium angular alignment error (θSOE) using contrast-enhanced cone beam computed tomography imaging after each positioning for each technique. Mean, standard deviation, and standard error are reported for the performance variables, and Student’s t tests were used to evaluate statistically significant differences in performance mean values of 3D-GNC and fluoroscopy. Results Technical success for the use of 3D-GNC to orient and position the endovascular device at each renal-visceral branch ostium was 100%. 3D-GNC resulted in 56% decrease in procedure time in comparison with standard fluoroscopic guidance (pConclusions The holographic 3D-GNC system demonstrated improved accuracy of aortic stent-graft positioning with significant reductions in fluoroscopy time, contrast-dye administration, and procedure time.

Conventional spacecraft Guidance, Navigation, and Control (GNC) architectures have been designed to receive and execute commands from ground control with minimal automation and autonomy onboard spacecraft. In contrast, Artificial Intelligence (AI)-based systems can allow real-time decision-making by considering system information that is difficult to model and incorporate in the conventional decision-making process involving ground control or human operators. With growing interests in on-orbit services with manipulation, the conventional GNC faces numerous challenges in adapting to a wide range of possible scenarios, such as removing unknown debris, potentially addressed using emerging AI-enabled robotic technologies. However, a complete paradigm shift may need years' efforts. As an intermediate solution, we introduce a novel visual GNC system with two state-of-the-art AI modules to replace the corresponding functions in the conventional GNC system for on-orbit manipulation. The AI components are as follows: (i) A Deep Learning (DL)-based pose estimation algorithm that can estimate a target's pose from two-dimensional images using a pre-trained neural network without requiring any prior information on the dynamics or state of the target. (ii) A technique for modeling and controlling space robot manipulator trajectories using probabilistic modeling and reproduction to previously unseen situations to avoid complex trajectory optimizations on board. This also minimizes the attitude disturbances of spacecraft induced on it due to the motion of the robot arm. This architecture uses a centralized camera network as the main sensor, and the trajectory learning module of the 7 degrees of freedom robotic arm is integrated into the GNC system. The intelligent visual GNC system is demonstrated by simulation of a conceptual mission—AISAT. The mission is a micro-satellite to carry out on-orbit manipulation around a non-cooperative CubeSat. The simulation shows how the GNC system works in discrete-time simulation with the control and trajectory planning are generated in Matlab/Simulink. The physics rendering engine, Eevee, renders the whole simulation to provide a graphic realism for the DL pose estimation. In the end, the testbeds developed to evaluate and demonstrate the GNC system are also introduced. The novel intelligent GNC system can be a stepping stone toward future fully autonomous orbital robot systems.

Chang’e-5 (CE-5) renewed the records of successful lunar return skip-reentry missions of China in December 2020, following the accomplishment of the first CE-5 mission in November 2014. The stable performance and exceptional precision of the two missions have proven the effectiveness of guidance, navigation, and control (GNC) designs for CE-5 skip-reentry, which is the first and unique adaptive numeric predictor-corrector guidance practice in the world. This paper reviews skip reentry trajectory features and the challenges of hyper speed skip-reentry problems to GNC. Furthermore, to tackle these problems, a series of approaches, including a numerical predictor-corrector guidance method, an on-line low computational consumption alignment method, and navigation and targeting philosophy, are investigated. In addition, the flight results and the post-flight analyses from CE-5 missions are presented to demonstrate the GNC performance.

The Chang’e-5 has carried out China’s first autonomous rendezvous and docking in lunar orbit. The guidance, navigation, and control system (GNC) plays an important role in this achievement. This paper introduces the composition of the GNC system and its working principles, in addition to rendezvous and docking (RVD) phase partition and the key technology involved. In addition, the first RVD flight result and its evaluation are presented.

An enabling technology platform, the Southwest Research Institute (SwRI) Integrated Avionics Unit (IAU) has been advanced from previous development efforts discussed in 2017 and continues to provide a number of functions to a host space vehicle in a compact form factor, tailored for the varied demands of Class D micro and mini satellite missions. In recent times, the SwRI IAU has been tailored for the recently awarded NASA Polarimeter to Unify the Corona and Heliosphere (PUNCH) mission, a constellation of four microsats designed to study solar interactions. In this configuration, the IAU is capable of interfacing with a solar array to efficiently couple energy to the power system and provides for charging and discharging of a battery, as well as monitoring its state of health. The IAU also provides appropriate power switching and distribution for enumerated spacecraft sensors, actuators, encryption, and communication loads as well as for internal power regulation for its electronics and for a spacecraft sensor network. The IAU provides processing for command & data handling (C&DH) tasks, guidance navigation and control (GNC) tasks, sub-system communication, and specific processing of payload sensor data. The IAU also houses volatile and non-volatile data storages and can process data from these storages into CCSDS compliant packets for downlink on multiple links. The IAU also supports appropriate signal/control/telemetry interfaces to enumerated spacecraft sensors, actuators, and communication devices. This paper describes the implementation of the IAU, as tailored to the PUNCH mission, opportunities for further efficiency gains, and potential future capability growth.

Guidance, Navigation, and Control (GNC) subsystem was one of key subsystems for the lunar lander. Major functions of GNC subsystem were: trajectory corrections maneuvers during LTO, LOI at perilune, orbit phase adjustment and de-orbit burn, soft-landing GNC during powered descent.

Tianwen-1, the first mission of China’s planetary exploration program, accomplished its goals of orbiting, landing, and roving on the Mars. The entry, descent, and landing (EDL) phase directly determines the success of the entire mission, of which the guidance, navigation, and control (GNC) system is crucial. This paper outlines the Tianwen-1 EDL GNC system design by introducing the GNC requirements followed by presenting the GNC system architecture and algorithms to meet such requirements. The actual flight results for the whole EDL phase are also provided in this paper.

To achieve the goal of collecting lunar samples and return to the Earth for the Chang’E-5 spacecraft, the lander and ascender module (LAM) of the Chang’E-5 spacecraft successfully landed on the lunar surface on 1 Dec., 2020. The guidance, navigation, and control (GNC) system is one of the critical systems to perform this task. The GNC system of previous missions, Chang’E-3 and Chang’E-4, provides the baseline design for the Chang’E-5 LAM, and the new characteristics of the LAM, like larger mass and liquid sloshing, also bring new challenges for the GNC design. The GNC design for the descent and landing is presented in this paper. The guidance methods implemented in the powered descent are presented in detail for each phase. Propellant consumption and hazard avoidance should be particularly considered in the design. A reconfigurable attitude control is adopted which consists of the quaternion partition control, phase and gain stabilization filter, and dual observer. This controller could provide fast attitude maneuver and better system robustness. For the navigation, an intelligent heterogeneous sensor data fusion method is presented, and it is applied for the inertial measurement unit and velocimeter data. Finally, the flight results of the LAM are shown. Navigation sensors were able to provide valid measurement data during descent, and the thrusters and the main engine operated well as expected. Therefore, a successful soft lunar landing was achieved by the LAM.

The purpose of this paper is to develop a fixed-wing aircraft that has the abilities of both vertical take-off (VTOL) and a fixed-wing aircraft. To achieve this goal, a prototype of a fixed-wing gyroplane with two propellers is developed and a rotor can maneuver like a drone and also has the ability of vertical take-off and landing similar to a helicopter. This study provides guidance, navigation, and control algorithm for the gyrocopter. Firstly, this study describes the dynamics of the fixed-wing aircraft and its control inputs, i.e., throttle, blade pitch, and thrust vectors. Secondly, the inflow velocity, the forces acting on the rotor blade, and the factors affecting the rotor speed are analyzed. Afterward, the mathematical models of the rotor, dual engines, wings, and vertical and horizontal tails are presented. Later, the flight control strategy using a global processing system (GPS) module is designed. The parameters that are examined are attitude, speed, altitude, turn, and take-off control. Lastly, hardware in the loop (HWIL) based simulations proves the effectiveness and robustness of the navigation guidance and control mechanism. The simulations confirm that the proposed novel mechanism is robust and satisfies mission requirements. The gyrocopter remains stable during the whole flight and maneuvers the designated path efficiently.

The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems&rsquo, development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant, it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.

Despite the recent progress, guidance, navigation, and control (GNC) are largely unsolved for agile micro autonomous underwater vehicles (µAUVs). Hereby, robust and accurate self-localization systems which fit µAUVs play a key role and their absence constitutes a severe bottleneck in micro underwater robotics research. In this work we present, first, a small-size low-cost high performance vision-based self-localization module which solves this bottleneck even for the requirements of highly agile robot platforms. Second, we present its integration into a powerful GNC-framework which allows the deployment of µAUVs in fully autonomous mission. Finally, we critically evaluate the performance of the localization system and the GNC-framework in two experimental scenarios.

Traditional guidance, navigation, and control computing systems follow federated architecture with dedicated systems for each one. Whereas, the tactical missile requires highly integrated, small size, less weight modular avionics. The authors developed the low cost integrated navigation, guidance & control and telemetry system. The system is partitioned into inertial measurement unit and GNC computing platform with telemetry module. The computing system is developed based on an ARM multi-core cortex-A9 processor. The navigation, guidance & control and telemetry are configured on separate cores of the ARM processor. The data between cores exchange through shared memory with interrupt based synchronization. The proposed architecture improves the integration efficiency by optimally utilizing the resources and providing modularity and flexibility with less space, weight, and power.

This article studies the design, modeling, and implementation of a model-based Guidance, Navigation, and Control (GNC) architecture for an Autonomous Underwater Vehicle (AUV). First, effective simulation modeling is developed using a theoretical six-degree-of-freedom (6DoF) dynamic model. Then, this study considers two GNC algorithms (simple and advanced). The simple GNC algorithm considers three different kinds of PID controllers (velocity, velocity-position, and position), and the advanced GNC algorithm enables path-following and data acquisition and processing from an underwater sensor. The path following is based on the position control using a unique PID controller and obtains its waypoints from a wall detection algorithm. This wall detection algorithm uses a mechanical imaging sonar as the main perception sensor. Finally, an implementation challenge in two control scenarios is addressed to validate the designed GNC architecture and to carry out model-verification of the position PID controller. acceptedVersion

The guidance, navigation and control (GNC) technology of deep space exploration covers an extensive range of applications, including deep space orbital control, extraterrestrial celestial landing, extraterrestrial celestial patrol and high-velocity reentry in addition to the attitude and orbital control of conventional near-Earth spacecrafts.

As large numbers of increasingly smaller spacecraft continue to be launched, means of efficient and reliable orbital maneuvering and orbit disposal have become increasingly necessary. For spacecraft that do not contain thrusters, aerodynamic drag modulation using a retractable drag device or attitude changes presents itself as an efficient way to perform orbital maneuvers and control the re-entry location. This paper introduces an aerodynamically based re-entry guidance generation algorithm for low Earth orbit spacecraft that exhibits significant accuracy, robustness, and efficiency. The paper also presents a novel guidance tracking algorithm whereby the drag device of a spacecraft is deployed or retracted relative to a nominal deployment profile (given in the guidance) based on the difference between the actual and desired state of the spacecraft. A full state feedback linear-quadratic-regulator control scheme is utilized with the Schweighart Sedgwick equations of relative motion to drive the relative position and velocity between the spacecraft and the guidance trajectory to zero. A problem-specific Extended Kalman Filter implementation is also introduced to remove noise from the GPS-derived relative motion estimate. One thousand Monte Carlo simulations of the guidance generation algorithm with randomized initial conditions and desired re-entry locations are conducted, resulting in an average guidance error of 12.5 k m and a maximum error below 106 k m . The tracking of these aerodynamic decay guidances with the aforementioned algorithms is also simulated with drag force uncertainties up to a factor of two and navigation errors (noise and bias) comparable to that expected from a CubeSat GPS unit. Despite these simulated errors and uncertainties, this approach provides guidance tracking down to a re-entry altitude of 120 k m with a final position error under 6 k m for all cases. The algorithms detailed in this paper provide a way for any spacecraft capable of modulating its drag area to autonomously perform orbital maneuvers and execute a precise re-entry.

The rise in small, distributed satellite applications has led to an increased interest in multi-agent, cooperative guidance, navigation, and control (GNC) strategies. In order to integrate and test algorithms associated with a multi-agent cooperative environment, the Cooperative Autonomous Networked Systems (CANS) Lab was developed. Multiple three-wheeled, omni-directional ground robots with on-board sensor suites enable hardware-in-the-loop testing while adhering to planar relative dynamics. A software framework has been established to enable accurate dynamics simulation using Clohessy-Wiltshire-Hill (CWH) dynamics. In this paper, the CANS lab is demonstrated through the use of an example formation flying application which incorporates uncertain scenario parameters.

For almost as long as the Western world has had rockets and missiles, people have been interested in incorporating guidance systems into them [1], [2]. Robert Goddard experimented with rudimentary gyroscopic systems as early as the 1930s. Under the leadership of Wernher Von Braun and his team in the 1940s, V2 rockets used gyroscopes and an accelerometer, along with a simple analog computer, to help adjust their azimuth. Advancements continued to be made after World War II, including the development of guidance systems that adhered as closely as possible to a preplanned reference trajectory [1]-[3]. Expanding on this, studies (such as Launius and Jenkins [4]) provide a broader perspective into the history of the launch vehicle control problem. In his doctrine, Friedberg touches upon the history of U.S. launch vehicle missions [5], while Bilstein sheds light on the Apollo/Saturn launch vehicle program [6]. Such systems, and their offshoots, used differences in the actual velocity vector vs. the expected velocity vector, as well as vector cross products and partial derivatives, to determine what corrective guidance actions to take.

In this work the autonomous flight of a drone for inspection of sensitive\ud environments is considered. Continuous monitoring, the possibility of override and\ud the minimisation of the on-board computational load are prioritized. The drone\ud is programmed with a Lyapunov vector guidance and nonlinear control to fly a\ud trajectory passed, leg after leg, by a remote ground station. GPS is the main\ud navigation tool used. Computational duties are split between the ground station\ud and the drone’s on board computer, with the latter dealing with the most time\ud critical tasks. This bipartite autonomous system marries recent advancements in\ud autonomous flight with the need for safe and reliable robotic systems to be used\ud for tasks such as inspection or structural health monitoring in industrial environments.\ud A test case and inspection data from a test over flat lead roof structure are\ud presented.

In many types of space mission there is a constant desire for larger and larger instrument apertures, primarily for the purposes of increased resolution or sensitivity. In the Radio Frequency domain, this is currently addressed by antennas that unfold or deploy on-orbit. However, in the optical and infrared domains, this is a significantly more challenging problem, and has up to now either been addressed by simply having large monolithic mirrors (which are fundamentally limited by the volume and mass lifting capacity of any launch vehicle) or by complex ‘semi-folding’ designs such as the James Webb Space Telescope. An alternative is to consider a fractionated instrument which is launched as a collection of individual smaller elements which are then assembled (or self-assemble) once in space, to form a much larger overall instrument. SSTL has been performing early concept assessment work on such systems for high resolution science observations from high orbits (potentially also for persistent surveillance of Earth). A point design of a 25 m sparse aperture (annular ring) telescope is presented. Key characteristics of 1) multiple small elements launched separately and 2) on-orbit assembly to form a larger instrument are included in the architecture. However, on-orbit assembly brings its own challenges in terms of guidance navigation and control, robotics, docking mechanisms, system control and data handling, optical alignment and stability, and many other elements. The number and type of launchers used, and the technologies and systems used heavily affect the outcome and general cost of the telescope. The paper describes one of the fractionated architecture concepts currently being studied by SSTL, including the key technologies and operational concepts that may be possible in the future.

The European Space Agency (ESA) is currently involved in several activities related to launch vehicle designs (Future Launcher Preparatory Program, Ariane 6, VEGA evolutions, etc.). Within these activities, ESA has identified the importance of developing a simulation infrastructure capable of supporting the multi-disciplinary design and preliminary guidance navigation and control (GNC) design of different launch vehicle configurations. Astos Solutions has developed the multi-disciplinary optimization and launcher GNC simulation and sizing tool (LGSST) under ESA contract. The functionality is integrated in the Analysis, Simulation and Trajectory Optimization Software for space applications (ASTOS) and is intended to be used from the early design phases up to phase B1 activities. ASTOS shall enable the user to perform detailed vehicle design tasks and assessment of GNC systems, covering all aspects of rapid configuration and scenario management, sizing of stages, trajectory-dependent estimation of structural masses, rigid and flexible body dynamics, navigation, guidance and control, worst case analysis, launch safety analysis, performance analysis, and reporting.

The design of a model-based Fault Tolerant Control (FTC) strategy based on Virtual Actuators (VA) in a built-in Guidance, Navigation and Control (GNC) setup is addressed for the e.Deorbit space mission. This mission, initiated by the European Space Agency (ESA), aims at removing a large defunct satellite from Earth orbit: ENVISAT. The goal of this paper is to promote academic solutions to add fault tolerance capacities against thruster faults without any change or new tuning of the already in-place GNC solution. The validation of the proposed FTC solution is assessed by a simulation campaign based on a high-fidelity nonlinear industrial simulator.

Nearly 3000 micro-nano satellites have been launched worldwide, and the resulting micro-nano clusters have attracted extensive attention from the international aerospace community. In order to better understand the development level of micro-nano clusters, this paper investigates some typical satellite swarm missions, summarizes the current status of guidance, navigation and control technologies for clusters, and looks forward to the development of China’s micro-nano cluster missions.

The paper proposes a technique to estimate the angular velocity of a rigid body from vector measurements. Compared to the approaches presented in the literature, it does not use attitude information nor rate gyros as inputs. Instead, vector measurements are directly filtered through a nonlinear observer estimating the angular velocity. Convergence is established using a detailed analysis of the linear-time varying dynamics appearing in the estimation error equation. This equation stems from the classic Euler equations and measurement equations. A high gain design allows to establish local uniform exponential convergence. Simulation results are provided to illustrate the method., 9 pages, 6 figures