Purpose of Review: The article provides an extensive overview on the resilient autonomy advances made across various missions, orbital or deep-space, that captures the current research approaches while investigating the possible future direction of resiliency in space autonomy.

Recent Findings: In recent years, the need for several automated operations in space applications has been rising, that ranges from the following: spacecraft proximity operations, navigation and some station keeping applications, entry, decent and landing, planetary surface exploration, etc. Also, with the rise of miniaturization concepts in spacecraft, advanced missions with multiple spacecraft platforms introduce more complex behaviours and interactions within the agents, which drives the need for higher levels of autonomy and accommodating collaborative behaviour coupled with robustness to counter unforeseen uncertainties. This collective behaviour is now referred to as resiliency in autonomy. As space missions are getting more and more complex, for example applications where a platform physically interacts with non-cooperative space objects (debris) or planetary bodies coupled with hostile, unpredictable, and extreme environments, there is a rising need for resilient autonomy solutions.

Summary: Resilience with its key attributes of robustness, redundancy and resourcefulness will lead toward new and enhanced mission paradigms of space missions.

In this letter, a floating robotic emulation platform is presented with an autonomous maneuverability for a virtual demonstration of a satellite motion. Such a robotic platform design is characterized by its friction-less, levitating, yet planar motion over a hyper-smooth surface. The design of the robotic platform, integrated with the sensor and actuator units, is briefly described, including the related component specification along with the mathematical model, describing its dynamic motion. Additionally, the article establishes a nonlinear optimal control architecture consisting of a unified model predictive approach for the overall manoeuvre tracking. The efficacy of the proposed modeling and control scheme is demonstrated in multiple experimental studies, where it is depicted that the proposed controller has the potential to address a precise point-to-point manoeuvre with terminal objectives, as well as an excellent path following capability. The proposed design is validated with extensive experimental studies, and it is supported with related results.

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

The Auroral Acceleration Region (AAR) is a key region in understanding the Magnetosphere-Ionosphere interaction. To understand the physical, spatial and temporal features of the region, multi-point measurements are required. Distributed small-satellite missions such as constellations of multiple nano satellites (for example multi-unit CubeSats) would enable such type of measurements. The capabilities of such a mission will highly depend on the number of satellites - one reason that makes low-cost platforms like CubeSats a very promising choice. In a previous study, the state-of-the-art of miniaturized payloads for AAR measurements was analyzed and evaluated and capabilities of different multi-CubeSat configurations equipped with such payloads in addressing different open questions in AAR were discussed. In this paper the mission analysis and possible mission design, as well as necessary technology developments of such multi-CubeSat mission are identified and presented.

This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions. It focuses specifically on investigating the capabilities of Unmanned Aerial Vehicles (UAV) equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments. The proposed approach, referred to as First Look Inspect-Explore (FLIE), employs a flexible bifurcated behavior tree that leverages real-time RGB image and depth cloud data. By employing a recursive and reactive synthesis of safe view pose within the inspection module, FLIE incorporates a novel active visual guidance scheme for identifying previously inspected surfaces. Furthermore, the integration of a tiered hierarchical exploration module with the visual guidance system enables the UAV to navigate towards new and unexplored structures without relying on a map. This decoupling reduces memory overhead and computational effort by eliminating the need to plan based on an incrementally built, error-prone global map. The proposed autonomy is extensively evaluated through simulation and experimental verification under various scenarios and compared against state-of-art approaches, demonstrating its performance and effectiveness.

In this article, a 3D visual coverage problem of an asteroid by establishing a stereo-visual based sensor from monocular cameras is considered. The cameras are individually mounted on two small low-thrust spacecraft, which are maintained in a tight stereo formation using a nonlinear model predictive control scheme to maintain an overlapping field-of-view and a specific baseline distance. The proposed control algorithm adopts a leader-follower architecture to define the relative pose between the spacecraft. However, asteroids provide a challenging environment for such missions deriving from their slow rotation rate and irregular shape. As such, they generate a low-order but irregular gravitational field. Paired with the influence of the solar radiation pressure acting on the spacecraft, the dynamic environment near the asteroid is highly perturbed. In addition, gravitational torque generated by the rotating body is also accounted for as it has a coupling effect between the orbital and attitude dynamics of the spacecraft. As a result, this article considers a full-state nonlinear control approach to plan correction maneuvers to maintain the desired pose of the spacecraft. The efficacy of the proposed control scheme is demonstrated within a realistic simulation scenario where the results are visualized utilizing the GAZEBO simulation environment.

The task of establishing and maintaining situational awareness in an unknown environment is a critical step to fulfil in a mission related to the field of rescue robotics. Predominantly, the problem of visual inspection of urban structures is dealt with view-planning being addressed by map-based approaches. In this article, we propose a novel approach towards effective use of Micro Aerial Vehicles (MAVs) for obtaining a 3-D shape of an unknown structure of objects utilizing a map-independent planning framework. The problem is undertaken via a bifurcated approach to address the task of executing a closer inspection of detected structures with a wider exploration strategy to identify and locate nearby structures, while being equipped with limited sensing capability. The proposed framework is evaluated experimentally in a controlled indoor environment in presence of a mock-up environment validating the efficacy of the proposed inspect-explore policy.

This article aims to present an experimental evaluation of an offline, geometry-aware aerial visual inspection framework, specifically in constrained environment, established for geometrically fractured objects, by employing an autonomous unmanned aerial vehicle (UAV), equipped with on-board sensors. Based on a model-centric approach, the proposed inspection framework, generates inspection viewpoints around the geometrically fractured object, subject to the augmented static bounds to prevent collisions. The novel framework of visual inspection, presented in this article, aims to mitigate challenges arising due to the spatially-constrained environment, such as limited configuration space and collision with the object under inspection, by accounting for the geometrical information of the vehicle to be inspected. The efficacy of the proposed scheme is experimentally evaluated through large scale field trials with a mining machine.

In this article, the problem of an online autonomous aerial inspection, specifically for discontinuous and distributed objects is presented. The proposed approach imposes view culling and photogrammetric constraints based on a geometrically modeled three-dimensional view pyramid, a view cone to filter surfaces by desired observation angle, a framework-integrated passive collision-avoidance scheme with the object under inspection, and a dynamically enveloping bounding-box region to map the visited surfaces. Furthermore, the proposed inspect-explore framework is validated for the case of an unknown environment with no prior knowledge of the object model under inspection. The overall inspection scheme is based on the novel First-Look approach, enabling the UAV to progressively adapt its inspection path to match the profile of the structure autonomously. The implemented exploration strategy imposes a tiered policy enabling the UAV to search, identify and navigate towards the structure for inspection. The presented work utilizes a unified architecture of the aforementioned inspect-explore framework to improve situational awareness in a previously unknown environment by enabling the UAV to explore its surrounding space and identify structures to execute closer inspection tasks. Extended simulations to evaluate the efficacy of the proposed inspect-explore framework are presented with multiple structure scenarios.

In this article we propose a novel offline, model-based aerial visual inspection scheme for geometrically fractured large objects, based on fully autonomous Unmanned Aerial Ve-hicles (UAV s), while specifically targeting the case of constrained environments. The proposed framework enables a safe and collision free inspection mission, while guaranteeing a complete visual inspection of the object of interest. The proposed framework employs a novel First - Look approach to generate viewpoints satisfying specific photogrammetric requirements, as well as spatial constraints that are inherently applied by the UAV's state constraints. As it will be presented, i) the First - Look approach allows the UAV to first orient it's view vector towards the nearest available point detected by kd - tree based Nearest Neighbour search on the object, from it's current position, and ii) in the sequel, based on the orientation of the left vector of the camera and the overlap distance, the next viewpoint is projected. This approach is repeated throughout the whole inspection procedure, while the established framework has also the merit to ensure that the inspection path adapts to the shape of the object, which is highly advantageous for the cases of geometrically fractured objects. Multiple realistic and physics based simulation results are presented that prove the efficacy of the proposed scheme.

The recent advances in space technology are focusing on fully autonomous, real-time, long-term orbit management and mission planning for large-scale satellite constellations in Low-Earth Orbit (LEO). Thus, a pioneering approach for autonomous orbital station-keeping has been introduced using a model-free Deep Policy Gradient-based Reinforcement Learning (DPGRL) strategy explicitly tailored for LEO. Addressing the critical need for more efficient and self-regulating orbit management in LEO satellite constellations, this work explores the potential synergy between Deep Reinforcement Learning (DRL) and Neuro-Evolution of Augmenting Topology (NEAT) to optimize station-keeping strategies with the primary goal to empower satellite to autonomously maintain their orbit in the presence of external perturbations within an allowable tolerance margin, thereby significantly reducing operational costs while maintaining precise and consistent station-keeping throughout their life cycle. The study specifically tailors DPGRL algorithms for LEO satellites, considering low-thrust constraints for maneuvers and integrating dense reward schemes and domain-based reward shaping techniques. By showcasing the adaptability and scalability of the combined NEAT and DRL framework in diverse operational scenarios, this approach holds immense promise for revolutionizing autonomous orbit management, paving the way for more efficient and adaptable satellite operations while incorporating the physical constraints of satellite, such as thruster limitations.

This chapter presents an overview of various navigation schemes used for ARWs and their application areas. Navigation schemes, in general, answer the question of how to move from the current position to the desired and optimally plan the path between them, which is a necessary step for almost all applications for autonomous flight. This chapter will go over reactive navigation schemes, such as the potential fields and Model Predictive Control with integrated obstacle avoidance, as well as global path-planning methods, such as map-based iterative planners like D^⁎, and planning for complete coverage of infrastructure to perform a visual inspection.

A decentralized multi-sensor fusion-based resilient pose estimation architecture for autonomous navigation of satellites around an asteroid is presented in this article. Navigation around an asteroid is challenging due to dynamic illumination conditions, which restricts the vision-based localization and is partially ineffective for a longer duration of the operation. Moreover, drift in sensor measurement and temporal sensor failure is often encountered in long-duration sustainable space missions. This is more so around a debris-prone region, where momentary obstruction leads to inaccurate sensor measurement for a temporary period of operation. In order to establish a resilient localization mechanism for satellites around an asteroid, the proposed framework embeds a unique automatic fault detection and isolation approach in a decentralized fusion formalism. Furthermore, a unified framework can operate autonomously during temporary and long-range inoperative periods. In the first stage, innovative fault detection is proposed, which operates based on the residual of a judiciously designed filter assembly. Secondly, a novel fault-resilient isolation fusion called the fault-resilient optimal information filter fusion (FR-OIF) technique is presented, enabling self-resiliency by embedding an inbuilt fault isolation mechanism. The proposed resilient asteroid navigation approach is demonstrated with a simulation study considering a satellite equipped with multiple onboard sensors such as an inertial measurement unit, star tracker, camera and 3D-Lidar in the proximity of the asteroid Ryugu. At the same time, its superiority is also demonstrated through a comparison with the centralized multi-sensorial fusion approach.

In this article, a novel adaptive trajectory tracking controller is designed for a payload-carrying spacecraft under full state constraints. The proposed controller can tackle state-dependent uncertainties without a priori knowledge of their structures and upper bounds. The controller ensures time-varying constraints on all states and their time derivatives. The closed-loop stability of the proposed scheme is verified analytically via the Lyapunov method, and real-life experiments using a robotic testbed validated the effectiveness of the proposed adaptive controller over the state-of-the-art.

In this article, a ground-based floating platform setup that emulates the zero-gravity spacecraft's motion on the ground is utilized for an active debris removal mission scenario. There are various phases in an active debris removal mission, which could be listed as close-range rendezvous, attitude coordination in case of tumbling target, grasping of the debris spacecraft, and the post grasping (debris removal phase). This article focuses on the post-grasping phase of the debris removal mission, with the assumption that the floating platform has grabbed (grasped) a debris of unknown mass and mass distribution, which adds to the modeling uncertainty of the newly unified platform (floating platform+debris). In sequel of the debris grasping, the floating platform needs to follow the desired path to move the debris to a desired secure location. This post-grasping scenario introduces multiple challenges rising from various parametric uncertainties in the robot's dynamics rising from nonlinearities, inaccuracy in estimating its inertia, discretization of thruster inputs using PWMs, and external disturbances. Also, since the robot maneuvers and operates in eminently constrained environments, the controller must ensure impeccable accuracy. The state-of-the-art controllers ensuring a constrained control of space robots with parametric uncertainties use a strict barrier Lyapunov function (BLF), which demands the initial conditions of the states to be within a specified bound, which is impractical in many scenarios. Hence, we propose a robust time-varying BLF controller for space robots, which tackles uncertainties and external disturbances while avoiding strict initial conditions for the constrained states. The controller's stability is validated using a Lyapunov-like method, and its performance is verified using a simulated Slider model.

With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multi-dimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV.

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which an adaptive controller tracks to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.

In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs' motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV's nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets.

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.

Quadrotors find their roles in various sectors ranging from remote surveillance to autonomous delivery due to their capabilities of hovering, Vertical Take Off and Landing (VTOL) and rapid manoeuvring. They are a viable asset to humans in safety-critical and hazardous operations such as remote inspection and manipulation of tunnels and windmills. These applications induce external disturbances and noises along with the modelling uncertainties in the dynamics. Applications such as aerial manipulation require control from a ground station autonomously or semi-autonomously, which leads to unpredictable delays and lags. In this context, the quadrotor has to perform its goals of following the desired trajectory with minimal deviation and holding its position without any deviation while operating in the environment. So, this article analyses the existing control techniques for the quadrotor tracking problem, which also tackle parametric uncertainties, unknown time-varying delays and ensure safety.

This article proposes an adaptive controller for a quadrotor UAV for carrying unknown payloads while tracking any trajectory. The proposed adaptive controller is robust to modeling uncertainties and does not require any a priori knowledge of the bounds of the uncertainties. The controller is also robust to time-varying delays without any constraint on the derivative of the time delay. In addition, the stability of the closed-loop system is analyzed via a Lyapunov-like method. The controller’s performance is verified using a simulated quadrotor model in MATLAB in three different scenarios with varying time delays and parametric uncertainties. 

In this article, we present a control strategy for the problem of safe autonomous docking for a planar floating platform (Slider) that emulates the movement of a satellite. Employing the proposed strategy, Slider approaches a docking port with the right orientation, maintaining a safe distance, while always keeping a visual lock on the docking port throughout the docking maneuver. Control barrier functions are designed to impose the safety, direction of approach and visual locking constraints. Three control inputs of the Slider are shared among three barrier functions in enforcing the constraints. It is proved that the control inputs are shared in a conflict-free manner in rendering the sets defining safety and visual locking constraints forward invariant and in establishing finite-time convergence to the visual locking mode. The conflict-free input-sharing ensures the feasibility of a quadratic program that generates minimally-invasive corrections for a nominal controller, that is designed to track the docking port, so that the barrier constraints are respected throughout the docking maneuver. The efficacy of the proposed control design approach is validated through various simulations.

This paper details the development of a planning algorithm for multiple co-located geostationary satellites to perform station keeping and momentum unloading maneuvers concurrently. The objective is to minimize the overall fuel consumption while guaranteeing a safe separation distance between the satellites within a specific geostationary slot, as well as managing their stored angular momentum to maintain their nadir pointing orientation. The algorithm adopts the leader-follower architecture to define relative orbital elements of the satellites equipped with four gimbaled on-off electric thrusters, and solves a convex optimization problem with inequality constraints, including momentum unloading requirements, to determine the optimal maneuvers. The proposed algorithm is verified, in terms of fuel consumption, constraints enforcement and satellites performance, using numerical simulations that take into account dominant perturbations in the geostationary environment.

In this paper, a planning method is developed using convex optimization for concurrent station keeping and momentum unloading maneuvers of geostationary satellites equipped with on-off electric thrusters. Prediction models for coupled orbital and attitude dynamics are used for generating concurrent maneuver plans. Since the satellite's attitude dynamics is fast compared to the orbital dynamics, a dual-rate model is proposed for addressing time scale differences of the two coupled systems. Based on such a model, a convex optimization problem is formulated and solved in a receding horizon form, which minimizes the fuel consumption and the number of required maneuvers. The proposed algorithm is verified using numerical simulations, taking into account major perturbations in the geostationary environment. The performance of the proposed method is analyzed in terms of fuel consumption and constraint enforcement.

This article discusses a convex-optimization-based planning method for a geostationary satellite to determine station keeping and momentum unloading maneuvers concurrently. The proposed optimization algorithm incorporates a dual-rate prediction model to address the time scaling difference between the coupled slow orbital and fast attitude dynamics. The use of combined prediction model in the optimization problem facilitates to include state constraints accounting for the desired orbital and momentum unloading requirements. Maneuver plans are determined by solving a convex optimization problem in a receding horizon control form. The main objective of the proposed algorithm is to minimize fuel consumption while managing the stored momentum, in order to maintain a satellite in a tight station keeping window and nadir pointing attitude configuration. Numerical simulations are performed to validate the proposed optimization algorithm in terms of fuel consumption and constraint enforcement.

This paper addresses the problem of determining optimal station keeping and momentum unloading maneuvers for a fleet of satellites co-located in a specific geostationary slot. A leader-follower architecture is implemented to control the motion of the follower satellites relative to the leader satellite. A convex optimizationbased algorithm is proposed to determine the concurrent maneuver planning for the satellites equipped with four on-off electric thrusters. The main objective of the maneuver planning algorithm is to minimize fuel consumption while guaranteeing a safe separation distance between the co-located satellites, as well as managing the stored angular momentum, in order to maintain nadir pointing orientation of each satellite. The proposed algorithm is verified in terms of fuel consumption and constraint enforcement, using numerical simulations that take into account dominant perturbations in the geostationary environment.

This article proposes a novel two-staged trajectory planning algorithm toward the cooperative visual coverage of multiple asteroid sites with the utilization of multiple spacecraft. The objective of the novel established planning scheme is to determine an observation tour for each spacecraft and the associated fuel optimal trajectories, such that each site of interest is observed only once during the entire mission. The completion of the observation task of an asteroid site is limited to the observation time window, referred as the period for which a site is illuminated by the Sun. The proposed planning algorithm divides the overall multi-site coverage problem into a nonlinear and integer optimization problem as: (i) a single target optimization and (ii) a multi-target multiple spacecraft optimal sequencing problem. The first part aims to generate fuel optimal trajectories, for all the initial-final imaging site location pairs. In the second part, the problem of distributing the observation task, among the fleet of spacecraft, is addressed by designing a feasible tour to observe a subset of the desired asteroid sites, for each spacecraft, while considering their individual fuel capacity. The efficacy of the proposed algorithm is evaluated in multiple simulation scenarios and while considering different asteroids.

In this article, an optimization based spacecraft trajectory planner for asteroid proximity missions is presented. In asteroid missions, it is of a specific interest to determine the surface and material properties of the target asteroid by obtaining high-resolution measurements from multiple sites over the asteroid surface. During this mission, an important problem to solve is the trajectory planning for the spacecraft, that results into a visual coverage problem for the asteroid surface. However, asteroids provide a challenging target for such missions since they are partially illuminated, rotating, irregular shaped bodies with a low (micro) but irregular gravity field. For addressing this challenging problem, this article will propose a novel optimization approach for the visual coverage of an asteroid. Thus, the proposed trajectory planner's objective is to determine the sequence of the areas to cover and the associated trajectories to achieve this coverage, while considering the motion of the spacecraft, the rotation dynamics of the asteroid, the illumination to each asteroid site and the irregular gravity constraints of the asteroid. The efficacy of the proposed optimal trajectory planner is evaluated through multiple simulation results, where it demonstrates successful optimal coverage of all the desired asteroid areas.

This article focuses on a spacecraft trajectory planning algorithm that allows observation of multiple site locations on the asteroid surface, while avoiding any collision with debris objects trapped in the asteroid’s gravity field. Asteroids provide a challenging target for satellite based visual coverage missions, since they are partially illuminated, rotating, irregular shaped celestial bodies with a low but also irregular gravity field. For addressing this problem, an optimization approach for visual coverage is proposed with an objective to determine the sequence of the imaging site locations and the associated safe and fuel efficient trajectories, while considering rotational dynamics of the asteroid, changing illumination condition for each site, irregular gravity constraints of the asteroid and the safe separation distance from the moving debris object. Numerical simulations are performed to demonstrate the ability of the trajectory planner to ensure successful optimal coverage of all the desired asteroid site locations.

Multiple spacecraft formation forms a distributed space system where multiple spacecraft cooperatively exhibit the functionality of a single monolithic spacecraft or work in a cooperative way towards the completion of a common goal. This possibility of distributing the workload between different spacecraft provides the ability to work in parallel, enable economic and simple solutions in terms of spacecraft design, while is redundant towards partial or complete failure of a spacecraft. Some of the challenges, associated to the guidance and control aspects of the spacecraft formation, are to maintain a safe separation distance between each spacecraft in the formation to avoid any collision, the distribution of workload between the spacecraft group, and designing fuel efficient trajectories for each spacecraft. This thesis focuses on studying these challenges through two respective mission scenarios, a) the co-location of geostationary satellites, and b) the cooperative visual coverage of an asteroid.

The first part of the thesis covers the development of guidance and control strategies to co-locate multiple geostationary satellites safely with a single operational slot. This part of the research is motivated by the growing demand to access the limited Earth resource of geostationary region; the growing use of low-thrust electric propulsion systems for geostationary satellite platforms; and the use of the same propulsion system for station keeping and momentum management of the reaction wheels to maintain a nadir pointing attitude for the satellites. Thus, to address the concurrent maneuver planning problem, a convex-optimization-based algorithm is proposed that incorporates a dual-rate prediction model to address the time scaling difference between the coupled slow orbital and fast attitude dynamics. The use of a combined prediction model in the optimization problem facilitates to include state constraints accounting for the desired orbital and momentum unloading requirements. This algorithm is then extended to the co-location problem, where a leader-follower architecture is adopted to define relative orbital elements of the satellites. The separation distance between the satellites in maintained by defining convex bounds on the relative orbital elements of the follower satellites. The algorithm is then implemented in receding horizon control form to introduce autonomy and to maintain tighter state constraints.

The second part covers the trajectory planning for a spacecraft group for the visual coverage mission of an asteroid. In asteroid missions, it is of a specific interest to determine the surface and material properties of the target asteroid by obtaining high-resolution measurements from multiple sites over the asteroid surface. This task can be accomplished using a group of spacecraft, equipped with optical sensors that are moving in proximity to the asteroid. During this mission, an important problem to solve is the to determine a fuel optimal observing sequence and the associated trajectories for a given set of target sites for each spacecraft. However, the completion of the observation task, for a specific site, is limited to the observation time window, usually referred as the period for which a site is illuminated by the Sun. Two trajectory planning algorithms are proposed, which are tested for a single spacecraft case to compare their performance in terms of finding an optimal solution. The travelling salesman based planning algorithm provided near-optimal results in terms of fuel consumption. This algorithm is then extended to the multiple spacecraft case to determine the near-optimal unique observing tour for each spacecraft.

The efficacy of the proposed algorithms are evaluated, in terms of fuel consumption and constraints enforcement using several numerical simulations that take into account dominant perturbations in the respective environment.

Edge Computing is a promising technology toprovide new capabilities in technological fields that require instantaneous data processing. Researchers in areas such asmachine and deep learning use extensively edge and cloud computing for their applications, mainly due to the significant computational and storage resources that they provide. Currently, Robotics is seeking to take advantage of these capabilities as well, and with the development of 5G networks, some existing limitations in the field can be overcome. In this context, it is important to know how to utilize the emerging edge architectures, what types of edge architectures and platforms exist today and which of them can and should be used based on each robotic application. In general, Edge platforms can be implemented and used differently, especially since there are several providers offering more or less the same set of serviceswith some essential differences. Thus, this study addresses these discussions for those who work in the development of the next generation robotic systems and will help to understand the advantages and disadvantages of each edge computing architecture in order to choose wisely the right one for each application.

In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.

With the wide deployment of autonomous multi-agent robotic systems, control solutions based on centralized algorithms have been developed. Even though these centralized algorithms can optimize the performance of the multi-agent robotic systems, they require a lot of computational effort, and a centralized unit to undertake the entire process. Yet, many robotic platforms like some ground robots and even more, aerial robots, do not have the computing capacity to execute this kind of frameworks on their onboard computers. While cloud computing has been used as a solution for offloading computationally demanding robotic applications, from the robots to the cloud servers, the latency they introduce to the system has made them unsuitable for time sensitive applications. To overcome these challenges, this article promotes an Edge computing-based Centralized Nonlinear Model Predictive Control (E-CNMPC) framework to control, and optimize, in swarm formation, the trajectory of multiple ground robotic agents, while taking under consideration potential collisions. The data processing procedure for the time critical application of controlling the robots in a centralized manner, is offloaded to the edge machine, thus the framework benefits from the provided edge resources, features, and centralized optimal performance, while the latency remains bounded in desired values. Besides, real experiments were conducted as a proof-of-concept of the proposed framework to evaluate the system’s performance and effectiveness.

Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its 'infinite' resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilized the edge resources, several technologies, with basic ones as containers and orchestrators like kubernetes, have been developed to provide an environment with many different features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC).

Edge computing is becoming more and more popular among researchers who seek to take advantage of the edge resources and the minimal time delays, in order to run their robotic applications more efficiently. Recently, many edge architectures have been proposed, each of them having their advantages and disadvantages, depending on each application. In this work, we present two different edge architectures for controlling the trajectory of an Unmanned Aerial Vehicle (UAV). The first architecture is based on docker containers and the second one is based on kubernetes, while the main framework for operating the robot is the Robotic Operating System (ROS). The efficiency of the overall proposed scheme is being evaluated through extended simulations for comparing the two architectures and the overall results obtained.

In this article the implementation of a controller and specifically of a Model Predictive Controller (MPC) on an Edge Computing device, for controlling the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC requires more computation power in comparison to other controllers, such as PID or LQR, since it use cost functions, optimization methods and iteratively predicts the output of the system and the control commands for some determined steps in the future (prediction horizon). Thus, the computation power required depends on the prediction horizon, the complexity of the cost functions and the optimization. The more steps determined for the horizon the more efficient the controller can be, but also more computation power is required. Since sometimes robots are not capable of managing all the computing process locally, it is important to offload some of the computing process from the robot to the cloud. But then some disadvantages may occur, such as latency and safety issues. Cloud computing may offer “infinity” computation power but the whole system suffers in latency. A solution to this is the use of Edge Computing, which will reduce time delays since the Edge device is much closer to the source of data. Moreover, by using the Edge we can offload the demanding controller from the UAV and set a longer prediction horizon and try to get a more efficient controller.