Mission Success Rate Research Articles

Augmented Reality Interface for Adverse-Visibility Conditions Validated by First Responders in Rescue Training Scenarios

Updating the equipment of the first responder (FR) by providing them with new capabilities and useful information will inevitably lead to better mission success rates and, therefore, more lives saved. This paper describes the design and implementation of a modular interface for augmented reality displays integrated into standard FR equipment that will provide support during the adverse-visibility situations that the rescuers find during their missions. This interface includes assistance based on the machine learning module denoted as Robust Vision Module, which detects relevant objects in a rescue scenario, particularly victims, using the feed from a thermal camera. This feed can be displayed directly alongside the detected objects, helping FRs to avoid missing anything during their operations. Additionally, the information exposition in the interface is organized according to the biometrical parameters of FRs during the operations. The main novelty of the project is its orientation towards useful solutions for FRs focusing on something occasionally ignored during research projects: the point of view of the final user. The functionalities have been designed after multiple iterations between researchers and FRs, involving testing and evaluation through realistic situations in training scenarios. Thanks to this feedback, the overall satisfaction according to the evaluations of 18 FRs is 3.84 out of 5 for the Robust Vision Module and 3.99 out of 5 for the complete AR interface. These functionalities and the different display modes available for the FRs to adapt to each situation are detailed in this paper.

Vibration suppression of flexible hose for autonomous aerial refueling based on adaptive boundary compound controller

The suppression of vibrations in flexible hoses during autonomous aerial refueling (AAR) is crucial for enhancing mission success rates and safety, advancing AAR technology. In this paper, an adaptive boundary compound control (ABCC) strategy is proposed to address the vibration suppression problem of the flexible hose of a tanker subject to the bow wave effect (BWE) of the receiver, state output constraint, and partial actuator failure. Unlike previous studies, this research takes into account the impact of the receiver's BWE on these vibrations. The flexible hose is modeled as a three-dimensional Euler-Bernoulli beam (TDEBB), utilizing partial differential equations (PDEs) to provide a more accurate description of its dynamic characteristics. Furthermore, the proposed ABCC strategy accurately detects all disturbances, ensures that the system state output remains within specified limits, and maintains stability even in the face of actuator failures. Last but not least, numerical comparisons and simulations demonstrate that the ABCC method significantly suppresses hose vibrations and maintains end displacement within a narrow range.

Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications.

Resilient multi-objective mission planning for UAV formation: A unified framework integrating task pre- and re-assignment

Combat effectiveness of unmanned aerial vehicle (UAV) formations can be severely affected by the mission execution reliability. During the practical execution phase, there are inevitable risks where UAVs being destroyed or targets failed to be executed. To improve the mission reliability, a resilient mission planning framework integrates task pre- and re-assignment modules is developed in this paper. In the task pre-assignment phase, to guarantee the mission reliability, probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model. And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution. As in the task-reassignment phase, possible trigger events are first analyzed. A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario. And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention. Three cases of different scales demonstrate that the two modules cooperate well with each other. On the one hand, the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced. On the other hand, the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond. The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Ladybug: An Automated Cultivation Robot for Addressing the Manpower Shortage in the Agricultural Industry

The agricultural sector is projected to need more labor as a result of declining interest in careers within this domain. Despite the escalating demand for agricultural goods, previous endeavors to mitigate this challenge through the deployment of robotic prototypes have encountered hindrances such as issues pertaining to automation, adaptability to varying tasks, and the financial burdens associated with development. To address this exigency, we have developed an automated cultivation robot utilizing advancements in the Internet of Things (IoT), Image Processing, and artificial Intelligence (AI) for seeding in pots. The robot demonstrates the capacity to sow seeds in 257 pots per hour, accomplish a mission within 12.53 minutes, traverse at a velocity of 360 meters per hour, and seed pots at a rate of 13.37 seconds per pot. It possesses an operational duration of approximately two hours, completing nine cycles and seeding 486 pots on a single charge. Notably, the robot exhibits a mission success rate of 1.00 and a seeding accuracy 0.78. Moreover, it features an adaptable workspace and a lightweight frame weighing 20 kg, rendering it a cost-effective solution for mass production.

Enhancing Targeted Operations Through Drone Technology: Maximising Efficacy and Minimising Risk

The article meticulously examines the strategic use of drone technology in targeted operations, with a strong focus on operational efficiency and technological advancements within established legal frameworks, including cyberlaw regulations. It discusses how technology can enhance mission effectiveness, mitigate risks, and ensure compliance with ethical and legal standards, particularly in targeted killings. Starting with recent advancements in drone data processing, sensor technology, and flight capabilities, the analysis emphasises the importance of adhering to cyberlaw regulations governing data privacy, cybersecurity, and ethical use in warfare. These advancements have revolutionised targeted killings, enabling more precise targeting, enhanced surveillance capabilities, and improved situational awareness. Additionally, the study addresses logistical challenges, mission planning intricacies, integration with military systems, and other operational considerations associated with drone deployment. It explores innovative strategies for overcoming these challenges through computational methods and advanced technology, demonstrating how organisations can optimise efficiency and achieve strategic objectives with drone technology. The article emphasises the significance of best practices in data analysis, mission execution, and decision-making processes to enhance mission success rates. Ultimately, it underscores the vital role of technology in maximising operational efficiency while minimising human risk, highlighting the importance of technological strategies and operational effectiveness in warfare scenarios.

ТАКСОНОМІЧНИЙ МЕТОД ВИБОРУ СПОСОБУ ВЕКТОРИЗАЦІЇ ПОТОЧНОГО ЗОБРАЖЕННЯ ДЛЯ ОПТИКО-ЕЛЕКТРОННОГО МОДУЛЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТА

In the article, a taxonomic method for selecting the optimal vectorization approach for processing current images in the optical-electronic module of unmanned aerial vehicles (UAVs) was developed. The primary goal of this method is to optimize UAV navigation systems, which is particularly relevant due to the need to improve data processing efficiency in conditions of limited access to satellite navigation systems. This is critically important for ensuring high autonomy and accuracy of UAVs during the performance of various tasks. The need for such a method arose from the specific challenges faced by UAVs when operating in complex and unpredictable conditions. Analysis has shown that the use of vector images can significantly reduce the time required for information processing. This is possible due to the smaller volume of data that needs to be processed compared to traditional raster images. Reducing information processing time is critically important for the operational capability of UAVs to respond to dynamic changes in the operational environment, especially in critical situations. The taxonomic approach applied in the study allows for systematic classification and evaluation of different vectorization methods in terms of their processing speed, accuracy of results, and data retention efficiency. This enables the identification of the most suitable method for the specific operational conditions in which the UAV is used, thereby optimizing the overall system performance. Furthermore, the results of this research are significant not only in the scientific field but also in practical applications. By improving autonomous navigation systems for UAVs, the study directly contributes to enhancing their effectiveness in complex and unpredictable conditions. This is particularly relevant for missions that require a high level of precision and reliability, such as those performed by military UAVs and emergency response. Enhanced navigation capabilities allow these UAVs to perform complex maneuvers and make real-time decisions based on processed vectorized images, thereby reducing the risk of errors and increasing mission success rates. An improved vectorization method could lead to significant enhancements in UAV navigation systems, making them more resistant to failures and capable of operating in less structured environments without heavy reliance on satellite signals. This enhancement is vitally important for both military and civilian UAV applications, where operational flexibility and reliability are extremely important.

Concurrent Design Optimization of Tether-Net System and Actions for Reliable Space-Debris Capture

Tether-nets deployed from a chaser spacecraft are a promising solution to capturing space debris. The success of the one-shot capture process depends on the net’s structural dynamic properties, attributed to its physical design, and on the ability to perform an optimal launch and closure subject to sensing and actuation uncertainties. Hence, this paper presents a reliability-based optimization framework to simultaneously optimize the net design and its launch and closing actions to minimize the system mass (case 1) or closing time (case 2) while preserving a specified probability of capture success. Success is assessed in terms of a capture quality index and the number of locked node pairs. Gaussian noise is used to model the uncertainties in the dynamics, state estimation, and actuation of the tether-net, which is propagated via Monte Carlo sampling. To account for uncertainties and ensure computational efficiency, given the cost of simulating the tether-net dynamics, Bayesian optimization is used to solve this problem. Optimization results show that the mission success rate in the presence of uncertainties has increased from 75% to over 98%, while the capture completion time has almost halved.

Tailored continuous risk management in nanosatellite space project VZLUSAT-1 using FMECA

The purpose of the research is to demonstrate an application of the existing methodology for risk assessment Failure Mode and Effect Criticality Analysis (FMECA) in nanosatellite projects together with a risk register proposal elaborated based on a literature review and expert interviews. The paper also elaborates on the integration of FMECA and knowledge capture and transfer technique. Literature evidence and the use case of the VZLUSAT-1 CubeSat show that it is challenging to implement proper project management and especially risk management practices into nanosatellite (or CubeSat) projects. The risks identified are mostly connected with the technical part, design, and operation of the satellite, and personal, financial, stakeholder, or managerial risks are omitted. The mission success rate can be significantly improved when risks are adequately considered at stages preceding the launch and operation of the satellite.

A multi-agent reinforcement learning algorithm with the action preference selection strategy for massive target cooperative search mission planning

Target search is widely applied in military reconnaissance, geological exploration and personnel search and rescue. Most target search algorithms perform well in single target search but are inefficient or even ineffective in multi-target search. To solve the multi-target search problem in an uncertain environment, this paper constructs a multi-agent massive target cooperative search mission planning model and proposes an improved reinforcement learning algorithm using the action preference selection strategy. Based on the Reinforce algorithm, this algorithm solves the problem of invalid searches within the stochastic strategy by changing the preferred action selection method. The proposed method improves the efficiency of multiple agents in the search for targets without collision using a cooperative mechanism and reward rules based on the odor effect. Simulation experiments are conducted in three aspects to verify the effectiveness and robustness of the improved algorithm and compare it with other reinforcement learning algorithms in the field of multi-agent learning. The results demonstrate that the improved algorithm has obvious advantages in terms of mission success rate, target search rate and average search time, and the movement trajectory of multiple agents is more concise.

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%.

Variable Thermal Conductivity Metamaterials Applied to Passive Thermal Control of Satellites

Abstract Active materials like the proposed variable thermal conductivity metamaterial enable new thermal designs and low-cost, low-power, passive thermal control. Thermal control of satellites conventionally requires active thermal control systems that are expensive, large, inefficient, energy-intensive, and unavailable for CubeSats. The high-temperature operation case is the thermal system’s primary design consideration for CubeSats. The thermal path is designed to reject as much heat as possible to ensure the system does not overheat. In other cases, such as during a power anomaly, the oversized thermal path results in rapid cooling, culminating in mission failure due to thermal limits on the electronics or batteries. Improving the thermal control of CubeSats can enable new thermally challenging missions, increase satellite longevity, and increase mission success rate by controlling the dynamic thermal environment. The materials available for thermal management are inherently limited, but new engineered materials provide unique opportunities to change how satellites adapt to thermal loads. This paper investigates using an adaptive metamaterial designed to passively change its thermal conductivity as a function of temperature to meet the needs of the satellite. The thermal performance of a CubeSat is evaluated with a variable thermal conductivity metamaterial located in the critical thermal path from the satellite to the radiator. The system’s performance using two metamaterial configurations is compared to a baseline copper thermal path. Multiple satellite thermal operation cases are investigated to determine the operation ranges, and the metamaterial’s performance in various conditions is quantified.

Multi-Tier 3D Trajectory Planning for Cellular-Connected UAVs in Complex Urban Environments

Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address communication and navigation limitations by leveraging base stations for air–ground communication. However, in complex urban scenarios with stringent communication requirements, achieving asymmetrical control becomes crucial to strike a balance between communication reliability and flight safety. Moreover, existing cellular-connected UAV trajectory planning algorithms often struggle to handle real scenes with sudden and intricate obstacles. To address the aforementioned challenges, this paper presents the multi-tier trajectory planning method (MTTP), which takes into account air–ground communication service assurance and collision avoidance in intricate urban environments. The proposed approach establishes a flight risk model that accounts for both the outage probability of UAV-ground base station (GBS) communication and the complexity of flight environments, and transforms the inherently complex three-dimensional (3D) trajectory optimization problem into a risk distance minimization model. To optimize the flight trajectory, a hierarchical progressive solution approach is proposed, which combines the strengths of the heuristic search algorithm (HSA) and deep reinforcement learning (DRL) algorithm. This innovative fusion of techniques empowers MTTP to efficiently navigate complex scenarios with sudden obstacles and communication challenges. Simulations show that the proposed MTTP method achieves a more superior performance of trajectory planning than the conventional communication-based solution, which yields a substantial reduction in flight distance of at least 8.49% and an impressive 10% increase in the mission success rate. Furthermore, a real-world scenario is chosen from the Yuhang District, Hangzhou (a southern Chinese city), to validate the practical applicability of the MTTP method in highly complex operating scenarios.

New multi-UAV formation keeping method based on improved artificial potential field

Formation keeping is important for multiple Unmanned Aerial Vehicles (multi-UAV) to fully play their roles in cooperative combats and improve their mission success rate. However, in practical applications, it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time. This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field (APF) method. Firstly, we build a formation flight model based on the “Leader-Follower” structure and design a robust H∞ controller with three channels X, Y and Z to eliminate dynamic uncertainties, so as to realize high-precision formation keeping. Secondly, to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia, this paper comes up with the improved APF method with deformation factor considered. The judgment criterion is proposed and applied to ensure flight safety. In the end, the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime, it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles. Therefore, the method proposed here boasts good engineering application prospect.

Unmanned Aerial Vehicles for Search and Rescue: A Survey

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.

Connectivity-Aware Particle Swarm Optimisation for Swarm Shepherding

Swarm cohesion is a prime contributor to the success of swarm guidance. However, a limited sensing range of the agents could lead to fragmentation of the swarm, which cascades to mission failure. Shepherding is a swarm guidance approach that relies on a finite number of agents to control a proportionally larger number of swarm members. We propose a connectivity-aware shepherding mechanism for improving the cohesion of the swarm in the presence of sensing constraints. A graph-based model of the flock is integrated with a particle swarm optimisation algorithm and a data clustering algorithm to identify the most appropriate herding point for influencing the swarm to move and to guide them towards the goal. The path to this herding point is optimised by particle swarm optimisation. The proposed methodology is evaluated with multiple swarm configurations. A comparison between the proposed methodology and classic shepherding demonstrates the superior performance of the former, which is demonstrated by a reduction of task completion time to half and a doubling in mission success rate.

Aerial-assisted task offloading approach over multilayer satellite edge computing

Satellite communication technology can solve the signal covering issues in special areas, such as deserts and oceans, which is difficult to handle with in traditional terrestrial wireless networks. Mobile edge computing (EMC) is crucial to help satellite communication to improve latency and energy efficiency performance. In this paper, we model a satellite constellation edge computing architecture with three layers and propose a task offloading deployment approach to balance the computation task. Simulation results show that the proposed algorithm can greatly reduce the execution delay of missions, improve the utilization of satellite resources, and increase the mission success rate compared with the existing strategies.

Loss-Framed Adaptive Microcontingency Management for Preventing Prolonged Sedentariness: Development and Feasibility Study.

A growing body of evidence shows that financial incentives can effectively reinforce individuals' positive behavior change and improve compliance with health intervention programs. A critical factor in the design of incentive-based interventions is to set a proper incentive magnitude. However, it is highly challenging to determine such magnitudes as the effects of incentive magnitude depend on personal attitudes and contexts. This study aimed to illustrate loss-framed adaptive microcontingency management (L-AMCM) and the lessons learned from a feasibility study. L-AMCM discourages an individual's adverse health behaviors by deducting particular expenses from a regularly assigned budget, where expenses are adaptively estimated based on the individual's previous responses to varying expenses and contexts. We developed a mobile health intervention app for preventing prolonged sedentary lifestyles. This app delivered a behavioral mission (ie, suggesting taking an active break for a while) with an incentive bid when 50 minutes of uninterrupted sedentary behavior happened. Participants were assigned to either the fixed (ie, deducting the monotonous expense for each mission failure) or adaptive (ie, deducting varying expenses estimated by the L-AMCM for each mission failure) incentive group. The intervention lasted 3 weeks. We recruited 41 participants (n=15, 37% women; fixed incentive group: n=20, 49% of participants; adaptive incentive group: n=21, 51% of participants) whose mean age was 24.0 (SD 3.8; range 19-34) years. Mission success rates did not show statistically significant differences by group (P=.54; fixed incentive group mean 0.66, SD 0.24; adaptive incentive group mean 0.61, SD 0.22). The follow-up analysis of the adaptive incentive group revealed that the influence of incentive magnitudes on mission success was not statistically significant (P=.18; odds ratio 0.98, 95% CI 0.95-1.01). On the basis of the qualitative interviews, such results were possibly because the participants had sufficient intrinsic motivation and less sensitivity to incentive magnitudes. Although our L-AMCM did not significantly affect users' mission success rate, this study configures a pioneering work toward adaptively estimating incentives by considering user behaviors and contexts through leveraging mobile sensing and machine learning. We hope that this study inspires researchers to develop incentive-based interventions.

Path Planning of Unmanned Aerial Vehicle in Complex Environments Based on State-Detection Twin Delayed Deep Deterministic Policy Gradient

This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better.