Mission Time Research Articles

Efficient Trajectory Planning for Optimizing Energy Consumption and Completion Time in UAV-Assisted IoT Networks

Quadrotor unmanned aerial vehicles (UAVs) have emerged as ubiquitous and agile robots and data carriers within the framework of the future Internet of Things (IoT) and mobile wireless networks. Yet, the insufficient onboard battery necessitates the optimization of energy consumption for both the UAV and IoT devices while ensuring that communication requirements are met. This paper therefore proposes a more accurate and mathematically tractable model for characterizing a UAV’s energy consumption concerning desired trajectories. This nonlinear model takes into account the UAV’s dynamics, brushless direct current (BLDC) motor dynamics, and aerodynamics. To optimize the communication time between IoT devices and the UAV, IoT devices are clustered using a modified GAK-means algorithm, with dynamically optimized communication coverage radii. Subsequently, a fly–circle–communicate (FCC) trajectory design algorithm is introduced and derived to conserve energy and save mission time. Under the FCC approach, the UAV sequentially visits the cluster centers and performs circular flight and communication. Transitions between cluster centers are smoothed via 3D Dubins curves, which provide physically achievable trajectories. Comprehensive numerical studies indicate that the proposed trajectory planning method reduces overall communication time and preserves UAV battery energy compared to other benchmark schemes.

COMPASSO mission and its iodine clock: outline of the clock design

One of the limiting factors for GNSS geolocation capabilities is the clock technology deployed on the satellites and the knowledge of the satellite position. Consequently, there are numerous ongoing efforts to improve the stability of space-deployable clocks for next-generation GNSS. The COMPASSO mission is a German Aerospace Center (DLR) project to demonstrate high-performance quantum optical technologies in space with two laser-based absolute frequency references, a frequency comb and a laser communication and ranging terminal establishing a link with the ground station located in Oberpfaffenhofen, Germany. A successful mission will strongly improve the timing stability of space-deployable clocks, demonstrate time transfer between different clocks and allow for ranging in the mm-range. Thus, the technology is a strong candidate for future GNSS satellite clocks and offers possibilities for novel satellite system architectures and can improve the performance of scientific instruments as well. The COMPASSO payload will be delivered to the international space station in 2025 for a mission time of 2 years. In this article, we will highlight the key systems and functionalities of COMPASSO, with the focus set to the absolute frequency references.

3D Gas Sensing with Multiple Nano Aerial Vehicles: Interference Analysis, Algorithms and Experimental Validation.

Within the scope of the ongoing efforts to fight climate change, the application of multi-robot systems to environmental mapping and monitoring missions is a prominent approach aimed at increasing exploration efficiency. However, the application of such systems to gas sensing missions has yet to be extensively explored and presents some unique challenges, mainly due to the hard-to-sense and expensive-to-model nature of gas dispersion. For this paper, we explored the application of a multi-robot system composed of rotary-winged nano aerial vehicles to a gas sensing mission. We qualitatively and quantitatively analyzed the interference between different robots and the effect on their sensing performance. We then assessed this effect, by deploying several algorithms for 3D gas sensing with increasing levels of coordination in a state-of-the-art wind tunnel facility. The results show that multi-robot gas sensing missions can be robust against documented interference and degradation in their sensing performance. We additionally highlight the competitiveness of multi-robot strategies in gas source location performance with tight mission time constraints.

Think like a team: Shared mental models predict creativity and problem-solving in space analogs

As long-distance space exploration missions move beyond low Earth orbit, and crews become more Earth-independent, it is essential to identify predictors of team performance - properties of teams that can be monitored during space flight to anticipate performance decrements. The most robust team state predicting performance in the team effectiveness literature is shared mental models. Shared mental models are properties of a group reflecting how members organize knowledge and understanding about the purpose of the team, the nature of the work, and how members work together. In this study we developed a measure of shared mental models for use in ground-based analogs. It was administered in the National Aeronautics and Space Agency (NASA)'s Human Exploration Research Analog (HERA) Campaign 4, Campaign 5, and the Nazemnyy Eksperimental'nyy Kompleks (NEK) SIRIUS-19 mission. HERA included eight 4-member crews in isolation for 45 days; NEK SIRIUS-19 included a 6-member crew in isolation for 120 days. To track performance variations, we administered two team tasks: a creative thinking task and a problem-solving task. We found substantial positive correlations between shared mental models and both dimensions of team performance in HERA and in NEK. Though shared mental models are a strong predictor of team performance across mission stages, we found some nuanced shifts. First, mental model sharedness in HERA is associated with crews generating fewer ideas in the third quarter than in other quarters, but also generating more novel, original ideas. Second, in the NEK mission we observed a third quarter effect with problem-solving, and the nature of the effect was that the effect of the shared mental model was most important in all quarters except the third. These results suggest that mission timing but also mission duration are important factors that condition relations between team process variables like shared mental models and team performance indicators.

Ubiquitous Unmanned Aerial Vehicles (UAVs): A Comprehensive Review

Unmanned aerial vehicles (UAVs) or drones have found applications in various fields including military operations, construction, parcel delivery, mapping, medical, search and rescue missions, exploration of hidden areas, monitoring power lines and oil rigs, precision farming, aerial surveillance, and wireless communication. Due to their modular flexibility and programmability, drone technologies have become a strategic industrial/commercial sector that has gained significant attention as it redefines and combines different emerging domains, such as: service and delivery convergence, lean manufacturing, environmental monitoring and security. The use of swarms of interconnected UAV systems has gained popularity in providing innovative approaches to tasks requiring wide-area multiple collaborative sensors. However, despite advances in maneuverability, user interface, and cost-effectiveness, drones still face limitations in flight autonomy due to restricted flight time for continuous missions. Battery endurance, drone weight, and payload are critical development vectors. This review highlights the importance of drones, their future development priorities and functionality. Specifically, it focuses on UAV classification, swarms, and charging. Moreover, it explores UAV applications, challenges, and security issues based on recent research and development. Additionally, the study delves into anti-drone technology that can assist countries in controlling and ensuring the safe utilization of drones.

The Influence of ENSO on the Long‐Term Water Storage Anomalies in the Middle‐Lower Reaches of the Yangtze River Basin: Evaluation and Analysis

AbstractRecent extreme events in the Middle‐Lower reaches of the Yangtze River basin (MLYRB) are proven to be possibly linked to the El Niño‐Southern Oscillation (ENSO) events as indicated by terrestrial water storage anomaly (TWSA). But the relatively short observation time of Gravity Recovery and Climate Experiment series missions (2002–2017; 2018–present) affects the robustness of the evaluation of TWSA. Here, the applicability of four long‐term TWSA data sets (since 1979) in the MLYRB is evaluated first using an evaluation framework including two completely independent tests. After selecting the optimal one, we investigate the effects of ENSO on TWSA in the MLYRB at the basin, subbasin, and grid cell scales, respectively. Results show that ENSO, especially the Eastern Pacific type ENSO has had a significant impact on TWSA variations in the MLYRB and its two subbasins (the Dongting Lake basin and the Poyang Lake basin) since 1979 with correlation coefficients at 0.56–0.65 and time lags at 5–6 months. However, TWSAs in the other two subbasins (the Hanjiang River basin and the Mainstream River basin) have almost no correlation with ENSO. Further analysis reveals that compared with human activity that has a limited impact on TWSA, precipitation is one of the key inducements for regional water storage changes in these two subbasins, and the no correlation between ENSO and TWSA is mainly caused by the weak link between ENSO and precipitation.

MW-MADDPG: a meta-learning based decision-making method for collaborative UAV swarm.

Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space.

Comparison of event tree/fault tree and convolution approaches in calculating station blackout risk in a nuclear power plant

Station blackout (SBO) risk is one of the most significant contributors to nuclear power plant risk. In this paper, the sequence probability formulas derived by the convolution approach are compared with those derived by the conventional event tree/fault tree (ET/FT) approach for the SBO situation in which emergency diesel generators fail to start. The comparison identifies what makes the ET/FT approach more conservative and raises the issue regarding the mission time of a turbine-driven auxiliary feedwater pump (TDP), which suggests a possible modeling improvement in the ET/FT approach. Monte Carlo simulations with up-to-date component reliability data validate the convolution approach. The sequence probability of an alternative alternating current diesel generator (AAC DG) failing to start and the TDP failing to operate owing to battery depletion contributes most to the SBO risk. The probability overestimation of the scenario in which the AAC DG fails to run and the TDP fails to operate owing to battery depletion contributes most to the SBO risk overestimation determined by the ET/FT approach. The modification of the TDP mission time renders the sequence probabilities determined by the ET/FT approach more consistent with those determined by the convolution approach.

Task Assignment Optimization in Multi-UAV-Assisted WSNs Considering Energy Budget and Sensor Distribution Characteristics.

In emergency situations, such as disaster area monitoring, deadlines for data collection are strict. The task time minimization problem concerning multi-UAV-assisted data collection in wireless sensor networks (WSNs), with different distribution characteristics, such as the geographical or importance of the information of the sensors, is studied. Our goal is to minimize the mission time for UAVs by optimizing their assignment, trajectory, and deployment locations, while the UAV energy constraint is taken into account. For the coupling relationship between the task assignment, trajectory, and hover position, it is not easy to solve the mixed integer non-convex problem directly. The problem is divided into two sub-problems: (1) UAV task assignment problem and (2) trajectory and hover position optimization problem. To solve this problem, an assignment algorithm, based on sensor distribution characteristics (AASDC), is proposed. The simulation results show that the collection time of our scheme is shorter than that of existing comparison schemes when using the same data size.

Reliability and availability simulation for vessel cooling system of the high temperature engineering test reactor

The article deals with the problem of risk analysis for new and unique nuclear facilities where safety-related systems are continuously operated during normal exploitation. A non-standard approach to system modeling was applied which goes beyond traditional method suitable for on-demand safety systems with a fixed mission time. As an example, the Vessel Cooling System (VCS) of the Japanese High Temperature Engineering Test Reactor (HTTR) has been analyzed in terms of life-cycle reliability and availability. Normal operation and emergency conditions have been considered, differing in the requirements for the system performance. In the former, the VCS reliability for cooling the biological shield around the Reactor Pressure Vessel (RPV) has been examined. In the latter, the VCS availability to remove the residual heat from the reactor core and RPV has been simulated. The results are of high importance for the safety and profitability of the HTTR-based electricity-hydrogen cogeneration plant (HTTR-GT/H2). The high reliability of the VCS under emergency conditions has been confirmed, thus contributing to the probabilistic safety assessment (PSA). Moreover, the fraction of the Forced Outage Rate (FOR) caused by VCS failures has been determined, thus contributing to the plant profitability studies.

Analysis using a Model-Based Approach of Path Planning for Multiple Uavs in the Context of Surveying Building Damage Following a Disaster

In the wake of disasters, efficient and timely assessment of building damage is of paramount importance for effective disaster response and recovery efforts. Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as indispensable tools for postdisaster surveying due to their ability to rapidly cover large areas and provide high-resolution imagery. The effective utilization of multiple UAVs in such scenarios necessitates optimal path planning to maximize coverage, minimize mission time, and ensure safe navigation. This paper presents a comprehensive model-based analysis of multi-UAV path planning strategies tailored for surveying building damage in postdisaster environments. The study commences by emphasizing the critical role of UAVs in disaster management, especially in the context of assessing structural damage. Acknowledging the challenges posed by complex urban landscapes and the need for swift response, the research focuses on the development and evaluation of path planning strategies that leverage advanced modeling techniques. The proposed model integrates spatial data, building blueprints, and environmental information to generate a realistic representation of the disaster-affected area, facilitating informed decision- making for path planning. A range of path planning approaches is examined within the model-based framework. These include grid-based methods, which discretize the area into cells and deploy UAVs to cover designated regions, and graph-based approaches such as Rapidly-exploring Random Trees (RRT), which generate paths through probabilistic exploration. The paper also delves into machine learning-enhanced strategies, leveraging reinforcement learning algorithms to adapt UAV paths based on real-time observations and evolving disaster dynamics. Furthermore, the study underscores the scalability of the model-based approach, demonstrating its applicability to scenarios involving a varying number of UAVs. Through comprehensive experimentation, the paper provides insights into the optimal deployment of UAV teams, shedding light on the balance between increased coverage and potential communication overhead.

A 12-month retrospective descriptive analysis of a single helicopter emergency medical service operator in four South African provinces.

Helicopter Emergency Medical Services (HEMS) is integrated into modern emergency medical services because of its suggested mortality benefit in certain patient populations, it is an expensive resource and appropriate use/feasibility in low- to middle income countries (LMIC) is highly debated. To maximise benefit, correct patient selection in HEMS is paramount. To achieve this, current practices first need to be described. The study aims to describe a population of patients utilising HEMS in South Africa, in terms of flight data, patient demographics, provisional diagnosis, as well as clinical characteristics and interventions. A retrospective flight- and patient-chart review were conducted, extracting clinical and mission data of a single aeromedical operator in South Africa, over a 12-month period (July 2017 - June 2018) in Gauteng, Free State, Mpumalanga and North-West provinces. A total of 916 cases were included (203 primary cases, 713 interfacility transport (IFT) cases). Most patients transported were male (n=548, 59.8%) and suffered blunt trauma (n=379, 41.4%). Medical pathology (n=247, 27%) and neonatal transfers (n=184, 20.1%) follows. Flights occurred mainly in daylight hours (n=729, 79.6%) with median mission times of 1-hour 53 minutes (primary missions), and 3 hours 10 minutes (IFT missions). Median on-scene times were 26 minutes (primary missions) and 55 minutes (IFT missions). Almost half were transported with an endotracheal tube (n=428, 46.7%), with a large number receiving no respiratory support (n=414, 45.2%). No patients received fibrinolysis, defibrillation, cardioversion or cardiac pacing. Intravenous fluid therapy (n=867, 94.7%) was almost universal, with common administration of sedation (n=430, 46.9%) and analgesia (n=329, 35.9%). Apart from the lack of universal call-out criteria and response to the high burden of trauma, HEMS seem to fulfil an important critical care transport role. It seems that cardiac pathologies are under-represented in this study and might have an important implication for crew training requirements.

Reliability optimization model in man‐machine systems considering human factors in uncertain situations

AbstractOver the last four decades, researchers have recognized that traditional automation has many negative consequences stemming from human out‐of‐the‐loop problems. However, there is a paucity of research to optimize function allocation problems under uncertainty by considering both human and machine influential factors. This work attempts to address this standing paucity of research by designing an optimization model with the objective function of maximizing system reliability besides balancing human workload over a mission time and keeping him/her in the control loop. To maximize system reliability, the prediction of human and machine fault rates is inspired by the human‐in‐the‐loop fault tree analysis (FTA). Furthermore, the failure probabilities of human‐related events in the proposed fault tree are estimated via fuzzy logic inference systems. A typical supervisory control task is selected to demonstrate the application and feasibility of the proposed method. In conclusion, this mathematical model as a decision support method provides guidance for automation designers to improve an automated system via systematic prediction of failures in the early stage of the design phase.

Modular Mechatronics Infrastructure for robotic planetary exploration assets in a field operation scenario

In 2021 the Modular Mechatronics Infrastructure (MMI) was introduced as a solution to reduce weight, costs, and development time in robotic planetary missions. With standardized interfaces and multi-functional elements, this modular approach is planned to be used more often in sustainable exploration activities on the Moon and Mars. The German multi-robot research project “Autonomous Robotic Networks to Help Modern Societies (ARCHES)” has explored this concept with the use of various collaborative robotic assets which have their capabilities extended by the MMI. Different scientific payloads, engineering infrastructure modules, and specific purpose tools can be integrated to and manipulated by a robotic arm and a standardized electromechanical docking-interface. Throughout the MMI’s design and implementation phase the performed preliminary tests confirmed that the different systems of the robotic cooperative team such as the Docking Interface System (DIS), the Power Management System (PMS), and the Data Communication System (DCS) functioned successfully. During the summer of 2022 a Demonstration Mission on Mount Etna (Sicily, Italy) was carried out as part of the ARCHES Project. This field scenario allowed the validation of the robotics systems in an analogue harsh environment and the confirmation of enhanced operations with the application of this modular method. Among the numerous activities performed in this volcanic terrain there are the efficient assembling of the Low Frequency Array (LOFAR) network, the energy-saving and reduced complexity of a detached Laser Induced Breakdown Spectroscopy (LIBS) module, and the uninterrupted powered operation between modules when switching between different power sources. The field data collected during this analogue campaign provided important outcomes for the modular robotics application. Modular and autonomous robots certainly benefit from their versatility, re-usability, less complex systems, reduced requirements for space qualification, and lower risks for the mission. These characteristics will ensure that long duration and complex robotic planetary endeavours are not as challenging as they used to be in the past.

Investigation of long-term storage of space materials for future constellation missions: study of Braycote® 601 EF lubricant

The long-term storage (LTS) of space materials is becoming more and more critical for the next missions. For instance, for MTG (Meteosat Third Generation) mission, 5-year assemble, integration and testing (AIT) time on ground, up to 17.5-year storage for recurring models plus at least 8.5 years in orbit operation are planned. Hence, a good understanding of materials’ aging properties is desirable to ensure that materials’ specifications are still suitable after LTS, and, eventually, to anticipate the potential non-conformities. For this purpose, the European Space Research and Technology Centre (ESTEC) is investigating these problematics thanks to its wide capabilities from aging with environmental, climatic and vacuum chambers up to physico-chemical characterizations by numerous instruments such as microscopes, spectroscopes, thermal, mechanical or electrical analyzers. An overview is presented herein through the study of a fluorinated based grease lubricant named Braycote® 601EF widely used in mechanisms in space sector such as the US space shuttles actuator or the basic end effectors of The European Robotic Arm.

ITUAVs: Intermittently Tethered UAVs for Future Wireless Networks

We propose the intermittently tethered unmanned aerial vehicle (iTUAV) as a tradeoff between the power availability of a tethered UAV (TUAV) and the flexibility of an untethered UAV. An iTUAV can provide cellular connectivity while being temporarily tethered to the most adequate ground anchor. Also, it can flexibly detach from one anchor, travel, then attach to another one to maintain/improve the coverage quality for mobile users. Hence, we discuss here the existing UAV-based cellular networking technologies, followed by a detailed description of the iTUAV system, its components, and mode of operation. Subsequently, we present a comparative study of the existing and proposed systems highlighting the differences in key features such as mobility and energy. To emphasize the potential of iTUAV systems, we conduct a case study, evaluate the iTUAV performance, and compare it to benchmarks. Obtained results show that with only 10 anchors in the area, the iTUAV system can serve up to 90% of the users covered by the untethered UAV swapping system. Moreover, results from a small case study prove that the iTUAV allows to balance performance/cost and can be implemented realistically. For instance, when user locations are clustered, with only 2 active iTUAVs and 4 anchors, achieved performance is superior to that of the system with 3 TUAVs, while when considering a single UAV on a 100 minutes event, a system with only 6 anchors outperforms the untethered UAV as it combines location flexibility with increased mission time.

Design of an improved adaptive slide controller in UUV dynamic base recovery

This paper addressed the problems in UUV dynamic base recovery, such as limited mission time, high control accuracy requirements and many uncertainties. The entire recycling process has been designed in sections to address these issues. A new sliding mode surface with predictive ideas based on the original fast terminal sliding mode control is designed for each phase, and a neural network (RBF) is introduced to compensate for the deviation between the actuator output and the controller command in practice. A new sliding-mode surface based on the compensated sliding-mode surface can be used to compensate for the failure of the original actuator due to faults and other factors, and a corresponding adaptive controller is derived. Simulation and experimental verification show that the designed controller not only has good convergence quality, but also can quickly adjust the position to the recovery mother ship after the original actuator failure, meeting the requirements of dynamic base recovery, and the feasibility in practice has been effectively proved.

Optimization techniques for Multi-Robot Task Allocation problems: Review on the state-of-the-art

In the last years, Multi-Robot Systems (MRS) have experienced considerable recognition due to various possible real-world applications. Multi-Robot Task Allocation (MRTA) is among the most interesting MRS problems. This problem concerns the situation when a set of given tasks must be performed by a team of mobile robots with the intention of optimizing an objective function (e.g., minimizing the mission time). This paper aims to present MRTA applications and categorizes methods into market-based, behavior-based, and optimization-based approaches. The paper focus on the latter and review several works in order to point out their advantages and limitations and to identify possible future research opportunities. Furthermore, a statistical analysis is provided to identify the most used methods and the evolution of the topic over the years.

Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulation

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling Evolutionary Algorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance—consisting of achieving the optimum period of time to carry out a preventive maintenance activity—are taken into account before being considered by the authors of the present paper. This work couples Multi-objective Evolutionary Algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the Multi-Objective Evolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed Multi-objective Evolutionary Algorithms is handled; moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of Multi-objective Evolutionary Algorithms and Discrete Event Simulation. Finally, the methodology is expanded to more complex systems which are successfully solved.