Complex Missions Research Articles

Fixed-Time Path-Following-Based Underactuated Unmanned Surface Vehicle Dynamic Positioning Control

The development of dynamic positioning (DP) algorithms for an unmanned surface vehicle (USV) is attracting great interest, especially in support of complex missions such as sea rescue. In order to improve the simplicity of the algorithm, a DP algorithm based on its own path following control ability is proposed. The algorithm divides the DP problem into two parts: path generation and path following. The key contribution is that the DP ability can be realized only by designing the path generation method, rather than a whole complex independent DP controller. This saves the computing power of the USV onboard computer and can effectively reduce the complexity of the algorithm. In addition, the fixed-time LOS guidance law is designed to improve the convergence rate of the system state in path-following control. The reasonable selection of speed and a heading controller ensures that the number of design parameters to be determined is at a low level. The above algorithms have been thoroughly evaluated and validated through extensive computer simulations, demonstrating their effectiveness in simulated and real marine environments. The simulation results verify the ability of the proposed algorithm to realize the dynamic positioning of USVs, and provide a practical scheme for the design of the dynamic positioning controller of USVs.

Digital twin method and application practice of spacecraft system driven by mechanism data

Spacecrafts are large-scale systems characterized by various on-orbit configurations, multi-disciplinary coupling, and complex mission modes. Research and exploration on the data-driven spacecraft digital twins development methods must be carried out to satisfy various requirements such as spacecraft on-orbit condition monitoring and risk warning, fast flight conditions predictions, intelligent failure location, and virtual verification of failure. In this paper, significant progress is made in multiple key technologies, such as cyber-physical system modeling and simulation, hybrid modeling and model evolution through mechanism-data fusion, and interactive virtual-reality perception and mapping. The spacecraft digital twins’ model is constructed, and the spacecraft digital twin’s platform is designed and developed. Multiple digital twins’ application scenarios, such as on-orbit mission simulation and emulation, real-time interactive monitoring, and fast operating condition prediction, are supported. The research results are applied to the key on-orbit operation tasks, such as entering orbit, rendezvous and docking, position conversion, and astronaut exiting, enabling system-level digital operation for the sub-systems of spacecraft such as energy, power, control, and communication sub-systems.

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to “ad hoc” solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMSS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system.

A New Multi-Target Three-Way Threat Assessment Method with Heterogeneous Information and Attribute Relevance

Target threat assessment provides support for combat decision making. The multi-target threat assessment method based on a three-way decision can obtain threat classification while receiving threat ranking, thus avoiding the limitation of traditional two-way decisions. However, the heterogeneous situation information, attribute relevance, and adaptive information processing needs in complex battlefield environment bring challenges to existing methods. Therefore, this paper proposes a new multi-target three-way threat assessment method with heterogeneous information and attribute relevance. Firstly, dynamic assessment information is represented by heterogeneous information, and attribute weights are calculated by heterogeneous Criteria Importance Through Intercriteria Correlation (CRITIC). Then, the conditional probability is calculated by the heterogeneous weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the adaptive risk avoidance coefficients are constructed by calculating the uncertainty of the assessment value, and then the relative loss function matrices are constructed. Finally, the comprehensive loss function matrices are obtained by the weighted Heronian mean (HM) operator, and the comprehensive thresholds are calculated to obtain the three-way rules. The case study shows that compared with the existing methods, the proposed method can effectively handle the heterogeneous information and attribute relevance, and obtain the risk avoidance coefficients without presetting or field subjective settings, which is more suitable for the complex mission environment.

Manufacturing and mechanical properties of adjustable stiffness hierarchical orthogrid stiffened cylinders

AbstractModular spacecraft offer promising prospects in the aerospace industry due to their ability to adapt to complex missions and diverse space environments. Nevertheless, such spacecraft may face load‐bearing structure adaptation issues caused by the changes in load demand when replacing sub‐modules for various missions. This necessitates the re‐design and manufacturing of load‐bearing structures, resulting in significant increases in product cycles and costs. To overcome this challenge, innovative composite hierarchical orthogrid stiffened cylinders (CHOSCs) which offer adjustable stiffness and strength to suit various mission requirements are proposed. To investigate the stiffness and strength adjustability, high‐precision composite orthogrid stiffened cylinder (COSC), CHOSC with three primary ribs (T‐CHOSC), and CHOSC with four primary ribs (F‐CHOSC) were manufactured and tested. The results demonstrate that CHOSCs exhibit stiffness and strength adjustability and significantly improve the cylinder's load‐bearing efficiency. Furthermore, a finite element model based on the Hashin damage criterion was employed to capture the end failure of the primary ribs and the local basic rib fractures near the cylinder end. CHOSCs present a feasible solution to the load‐bearing structure adaptation issues caused by the changes in load demand when replacing sub‐modules for various missions and hold significant potential for advancing the aerospace industry.Highlights Composite hierarchical orthogrid stiffened cylinders (CHOSCs) are proposed. High‐precision CHOSCs are manufactured by combined metal molds. Load‐bearing capacity and failure behavior are investigated. CHOSCs exhibit stiffness adjustability and load‐bearing efficiency. CHOSCs could solve the structure adaptation issues in modular spacecraft.

Hierarchical Motion Planning Under Probabilistic Temporal Tasks and Safe-Return Constraints

Safety is crucial for robotic missions within an uncertain environment. Common safety requirements such as collision avoidance are only state-dependent, which can be restrictive for complex missions. In this work, we address a more general formulation as safe-return constraints, which require the existence of a return-policy to drive the system back to a set of safe states with high probability. The robot motion is modeled as a Markov Decision Process (MDP) with probabilistic labels, which can be highly non-ergodic. The robotic task is specified as Linear Temporal Logic (LTL) formulas over these labels, such as surveillance and transportation. We first provide theoretical guarantees on the re-formulation of such safe-return constraints, and a baseline solution based on computing two complete product automata. Furthermore, to tackle the computational complexity, we propose a hierarchical planning algorithm that combines the feature-based symbolic and temporal abstraction with constrained optimization. It synthesizes simultaneously two dependent motion policies: the outbound policy minimizes the overall cost of satisfying the task with a high probability, while the return policy ensures the safe-return constraints. The problem formulation is versatile regarding the robot model, task specifications and safety constraints. The proposed hierarchical algorithm is more efficient and can solve much larger problems than the baseline solution, with only a slight loss of optimality. Numerical validations include simulations and hardware experiments of a search-and-rescue mission and a planetary exploration mission over various system sizes.

A Consistent Round-Up Strategy Based on PPO Path Optimization for the Leader-Follower Tracking Problem.

Single UAVs have limited capabilities for complex missions, so suitable solutions are needed to improve the mission success rate, as well as the UAVs' survivability. A cooperative multi-UAV formation offers great advantages in this regard; however, for large and complex systems, the traditional control methods will be invalid when faced with unstable and changing environments. To deal with the poor self-adaptability and high requirements for the environmental state information of traditional control methods for a multi-UAV cluster, this paper proposes a consistent round-up strategy based on PPO path optimization to track targets. In this strategy, the leader is trained using PPO for obstacle avoidance and target tracking, while the followers are expected to establish a communication network with the leader to obtain environmental information. In this way, the tracking control law can be designed, based on the consistency protocol and the Apollonian circle, to realize the round-up of the target and obstacle avoidance. The experimental results show that the proposed strategy can achieve the round-up of the target UAV and guide the pursuing multi-UAV group to avoid obstacles in the absence of the initial detection of the target. In multiple simulated scenarios, the success rates of the pursuit multi-UAV cluster for rounding up the target are maintained above 80%.

New reliability model for power SiC MOSFET technologies under static and dynamic gate stress

This work investigates the Vth degradation of commercial power SiC MOSFET technologies under HTGB and AC stresses with Gate Switching Stress conditions by using relatively accelerated tests whose stressors do not exceed the datasheet specifications. A new model that takes into consideration the variability of the exponent of power-law for Vth drift has been proposed and experimentally verified on planar and trench technologies. The proposed model is useful to estimate accurately the lifetime for any condition or complex mission profile.

Stable nullspace adaptive parameter identification of 6 degree-of-freedom plant and actuator models for underactuated vehicles: Theory and experimental evaluation

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles.

Multi-functional locomotion of collectively assembled shape-reconfigurable electronics

Beyond typical flexible electronics, stimuli-responsive shape-reconfigurable electronics are great candidates for the next-generation miniaturized electronics, carrying out complex missions by morphing their geometry and transferring their bodies to the desired positions. To achieve shape-reconfigurable electronics, it is essential to give shape-programmability as well as high electrical conductivity for polymeric materials. However, this is difficult to achieve due to a trade-off relationship between the shape-reconfigurability and the electrical conductivity of polymer composites that embed conductive fillers. Herein, we introduced highly conductive and physical intelligence-encoded polymeric composites based on MXene/liquid crystal elastomer (LCE) bilayer (MLB), for shape-reconfigurable electronics performing on-demand and reversible 3D morphing and locomotion. The MLB demonstrated remarkable photo-/electro-thermally driven bending and twisting performance under near-infrared (NIR) light with 0.4 W cm−2 and voltage application of 3.5 V without delamination due to both high photothermal conversion efficiency and electrical conductivity of ∼5,300 S cm−1. The MLB also proved structural durability after 1,000 cycles of bending originating from the stable interface induced by hydrogen bonding between MXene and LCE. Importantly, macroscopic collective assembly of the MLBs enabled dynamic locomotion inspired by nature. Collectively assembled MLBs demonstrated crawling, rotating, jumping, and sling-shooting by engineering the symmetricity of assembled structure and introducing snap-through instability.

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

A Unified Multimodal Interface for the RELAX High-Payload Collaborative Robot.

This manuscript introduces a mobile cobot equipped with a custom-designed high payload arm called RELAX combined with a novel unified multimodal interface that facilitates Human-Robot Collaboration (HRC) tasks requiring high-level interaction forces on a real-world scale. The proposed multimodal framework is capable of combining physical interaction, Ultra Wide-Band (UWB) radio sensing, a Graphical User Interface (GUI), verbal control, and gesture interfaces, combining the benefits of all these different modalities and allowing humans to accurately and efficiently command the RELAX mobile cobot and collaborate with it. The effectiveness of the multimodal interface is evaluated in scenarios where the operator guides RELAX to reach designated locations in the environment while avoiding obstacles and performing high-payload transportation tasks, again in a collaborative fashion. The results demonstrate that a human co-worker can productively complete complex missions and command the RELAX mobile cobot using the proposed multimodal interaction framework.

A Software-Defined Distributed Architecture for Controlling Unmanned Swarm Systems

An unmanned swarm is usually composed of a group of homogeneous or heterogeneous hardware platforms, software control systems, and interfaces for human–computer interaction that operate collectively to achieve a specific goal by information interaction. They exhibit robustness and fault tolerance when facing complex missions, making it crucial in military, transportation, intelligent traffic, and other fields. However, the coupling between the hardware and software of a heterogeneous unmanned swarm can indeed have significant implications for system flexibility, software development and deployment, and hardware maintenance. Over the years, there has been a significant shift from traditional hardware-focused control systems to a greater emphasis on the core software layer. In this paper, a distributed network architecture is proposed to solve this problem, in which hardware resources are abstracted and represented to accomplish standardization and unification by defining a consistent and uniform set of data formats, and a resource pool of hardware data is constructed to realize the function that the number and scale of platforms is irrelevant, the task module can be plug-and-play at any time, and the software can be configured on demand. The resource scheduling of a single platform is achieved through process and thread communication using shared memory, while the resource scheduling of a cross platform is achieved through a network using request and response and subscription and notification. As a result, it can satisfy the development of functional modules in a software-defined mode and gradually improve the intelligence capability of an unmanned swarm. Based on the above architecture, the overall framework of the autonomous navigation system and the collaborative control system has been successfully established. Finally, a hardware-in-the-loop simulation environment is constructed, and the integration and verification of the proposed distributed architecture is carried out by the cooperative formation experiment, which proves the feasibility of this proposal.

Oligodendrocyte progenitor cells differentiation induction with MAPK/ERK inhibitor fails to support repair processes in the chronically demyelinated CNS.

Remyelination failure is considered a major obstacle in treating chronic-progressive multiple sclerosis (MS). Studies have shown blockage in the differentiation of resident oligodendrocyte progenitor cells (OPC) into myelin-forming cells, suggesting that pushing OPC into a differentiation program might be sufficient to overcome remyelination failure. Others stressed the need for a permissive environment to allow proper activation, migration, and differentiation of OPC. PD0325901, a MAPK/ERK inhibitor, was previously shown to induce OPC differentiation, non-specific immunosuppression, and a significant therapeutic effect in acute demyelinating MS models. We examined PD0325901 effects in the chronically inflamed central nervous system. Treatment with PD0325901 induced OPC differentiation into mature oligodendrocytes with high morphological complexity. However, treatment of Biozzi mice with chronic-progressive experimental autoimmune encephalomyelitis with PD0325901 showed no clinical improvement in comparison to the control group, no reduction in demyelination, nor induction of OPC migration into foci of demyelination. PD0325901 induced a direct general immunosuppressive effect on various cell populations, leading to a diminished phagocytic capability of microglia and less activation of lymph-node cells. It also significantly impaired the immune-modulatory functions of OPC. Our findings suggest OPC regenerative function depends on a permissive environment, which may include pro-regenerative inflammatory elements. Furthermore, they indicate that maintaining a delicate balance between the pro-myelinating and immune functions of OPC is of importance. Thus, the highly complex mission of creating a pro-regenerative environment depends upon an appropriate immune response controlled in time, place, and intensity. We suggest the need to employ a multi-systematic therapeutic approach, which cannot be achieved through a single molecule-based therapy.

Federated learning based on asynchronous and adjusted client training

Machine learning in small-scale unmanned aerial vehicle (UAV) deployment fails to accomplish complex missions of swarm intelligence. Federated learning (FL) tackles it by being able to handle large-scale learning, while keeping data private. The client data distribution involved in FL is often non-independent and identically distributed (Non-IID) and the amount of data is unbalanced. This may seriously affect the model’s convergence speed, and may even lead to nonconvergence. To tackle the above problems, we propose a new asynchronous FL scheme that combines adjusting client training. The scheme determines the weight of aggregation based on the staleness of client model parameters and the amount of data owned by the client, and dynamically adjusts the local training of each client using average training loss. Experiments on public datasets verify the effectiveness of our method, which can alleviate the impact of data Non-IID and data imbalance in asynchronous FL and accelerate the training of FL.

Transfers Between Moons with Escape and Capture Patterns via Lyapunov Exponent Maps

This contribution focuses on the design of low-energy transfers between planetary moons and presents an efficient technique to compute trajectories characterized by desirable behaviors in the vicinities of the departure and destination bodies. The method utilizes finite-time Lyapunov exponent maps in combination with the moon-to-moon analytical transfer method previously proposed by the authors. The integration of these two components facilitates the design of direct transfers between moons within the context of the circular restricted three-body problem, and allows the inclusion of a variety of trajectory patterns, such as captures, landings, transits, and takeoffs, at the two ends of a transfer. The foundations and properties of the technique are illustrated through an application based on impulsive direct transfers between Ganymede and Europa. However, the methodology can be employed to assist in the design of more complex mission scenarios, such as moon tours.

Robust control of a dual-arm space robot to capture a non-cooperative target in 3D space

Non-cooperative objects in space, like defunct spinning satellites, are potentially dangerous to subsequent space missions. Thus, this paper proposes a new capture strategy by a dual-arm space robot to improve the efficiency of the capture operation. Compared with single-arm space robots, dual-arm space robots can carry out more complex missions, like capturing a spinning target in three-dimensional (3D) space, with a higher probability of success. Considering the possible model uncertainties of a space robot and a non-cooperative target, a sliding mode controller (SMC) with good robustness against the model uncertainties was developed. However, due to frequent switches in the controller, the SMC usually has chattering effects causing uncontrolled vibrations of the spacecraft structure. For comparison, a smoothed quasi-continuous second-order sliding mode controller (SQC2S) can provide the same high accuracy level as the SMC and remove the chattering effects caused by the SMC. In addition, the SQC2S can deliver better robust performance against the model uncertainties than the SMC. Finally, the proposed capture strategy is validated and compared in the numerical simulations by the SMC and the SQC2S.

The path towards increasing RAMS for novel complex missions based on CubeSat technology

The paper presents the initial outcomes of a project, currently ongoing under the supervision of the European Space Agency, having the main objective to specify and design a Fault Detection Isolation and Recovery (FDIR) system by making use of relevant RAMS (Reliability, Availability, Maintainability, Safety) analyses for missions in non-deterministic environment with limited resources. The initial project tasks have been to select a study case represented by a CubeSat complex mission, analyse in detail both its mission and system requirements and, based on them, define a set of relevant RAMS analyses to be carried out in the second phase of the project, as inputs for the development of a FDIR concept aimed at a careful balance of the limited spacecraft resources in case of critical failures. Two possible study cases have been identified: LUMIO, a 12U CubeSat mission for the observation of micro-meteoroid impacts on the Lunar farside, and M-ARGO, a 12U deep-space CubeSat which will rendezvous with a near-Earth asteroid and characterize its physical properties for the presence of in-situ resources. Although both missions are characterized by a high level of autonomy and complexity in a harsh environment, LUMIO has been eventually selected as study case for the project. In the paper, the challenges and features of this mission are shortly presented. The specificities of the RAMS analysis and FDIR concept for this specific class of small satellite missions (including the selected study case) are highlighted in the paper, looking in particular at aspects such as the improvement of reliability while maintaining the CubeSat philosophy, the tuning of mission and system requirements in view of facilitating the design and implementation of the FDIR concept, and the current gaps within the RAMS/FDIR body of knowledge. The conclusions drawn during this first project phase provide a real view of how systems engineering must work in tandem with RAMS analyses and FDIR to achieve a more robust and functional mission architecture, thus improving the mission reliability.

An Efficient Framework for Autonomous UAV Missions in Partially-Unknown GNSS-Denied Environments

Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied (GNSS-denied) environments. This paper presents hardware choices and software modules for localization, perception, global planning, local re-planning for obstacle avoidance, and a state machine to dictate the overall mission sequence. The entire software stack has been designed exploiting the Robot Operating System (ROS) middleware and has been extensively validated in both simulation and real environment tests. The proposed solution can run both in simulation and in real-world scenarios without modification thanks to a small sim-to-real gap with PX4 software-in-the-loop functionality. The overall system has competed successfully in the Leonardo Drone Contest, an annual competition between Italian Universities with a focus on low-level, resilient, and fully autonomous tasks for vision-based UAVs, proving the robustness of the entire system design.

Autonomica: Ontological Modeling and Analysis of Autonomous Behavior

AbstractModel‐based system autonomy is a complex integration of planning from high‐level goals to low‐level command sequences whose execution controls a system. The need for autonomy has accelerated in recent years to enable complex missions in automotive, space, and defense. During system development, understanding the relationship between system autonomy and the physical environment (including hardware) is critical to supporting trade studies, developing concepts of operations, characterizing risk, and performing testing. This paper describes the initial results of developing Autonomica, an ontology‐based methodology and a framework for autonomous behavior modeling and analysis. This methodology formalizes an architectural pattern for specifying model‐based autonomy as a vocabulary with description logic semantics and provides authoring and analysis capabilities (reasoning, querying, and simulation) for the architectures. The framework implements the methodology in an integrated workbench. A running example of a hypothetical spacecraft mission to a small space body illustrates the ideas.