Mission Environment Research Articles

Optimal Triggering Policy of Protective Devices Considering Self-Exciting Mechanism of Shocks

Safety-critical systems are commonly required to complete specific missions in shock environments, and their failures may lead to severe economic losses and significant safety hazards. To enhance system reliability, protective devices are commonly equipped to resist external shocks. The existing literature focuses mainly on the maintenance policy of safety-critical systems, ignoring the system reliability analysis considering the effect of protective devices and the self-exciting mechanism of shocks. This paper considers multi-state systems equipped with a protective device in shock environments where valid shocks and invalid shocks occur stochastically. The system state degenerates due to valid shocks or the self-exciting behavior of invalid shocks. The self-exciting mechanism is triggered when the number of cumulative or consecutive invalid shocks suffered by the system exceeds a certain threshold, leading the system to a worse state. The protective device can be triggered to protect the system from the damage of external shocks when the state is worse than a predetermined threshold. The protective effect is characterized by reducing the probability of valid shocks. A finite Markov chain embedding approach is used to evaluate the system reliability index. In addition, an optimization model is constructed to determine the optimal triggering threshold of the protective device. The numerical results indicate that protective devices can significantly improve the reliability of the system and incorporating the self-exciting mechanism of shocks into reliability modeling contributes to accurate reliability evaluation.

Expediting recovery of autonomous underwater vehicles in dynamic mission environments: A system‐of‐systems challenge for underwater warfare

AbstractAs autonomous underwater vehicle (AUV) adoption increases, operators demand advanced behaviors from commercial off‐the‐shelf systems. However, new behaviors can often only be deployed operationally once assured. This paper overviews research into expediting recovery to an operator's vessel through a custom homing behavior, demonstrating technology advancement in conjunction with test and evaluation activities. Advanced docking infrastructure and frameworks are still under development, yet current AUV operations require rapid and reliable recovery when mission factors change. Homing is achieved with a directional acoustic transponder providing range and bearing data to the AUV from the operator's vessel. A converted measurement Kalman filter processes range and bearing data that generates dynamic waypoints for the AUV through MOOS‐IvP as a backseat driver; a universal approach and filtering that is unique from prior AUV research. Results from simulations and field trials were analysed through a modular and experimental Test & Evaluation framework that was adopted specifically to help verify and validate the new AUV behavior, including systematic variations in recovery boat manoeuvres. The process includes documented use for the first time in AUV research of combinatorial screening (high throughput testing) and an average standardized residual metric to focus development early, encourage constructive iteration and build operational and engineering trust. Consistent homing was demonstrated with localization within 0.3 m and homing within 1.8 m of a moving digital acoustic transponder.

Probabilistic Inference and Dynamic Programming: A Unified Approach to Multi-Agent Autonomous Coordination in Complex and Uncertain Environments

We present a unified approach to multi-agent autonomous coordination in complex and uncertain environments, using path planning as a problem context. We start by posing the problem on a probabilistic factor graph, showing how various path planning algorithms can be translated into specific message composition rules. This unified approach provides a very general framework that, in addition to including standard algorithms (such as sum-product, max-product, dynamic programming and mixed Reward/Entropy criteria-based algorithms), expands the design options for smoother or sharper distributions (resulting in a generalized sum/max-product algorithm, a smooth dynamic programming algorithm and a modified versions of the reward/entropy recursions). The main purpose of this contribution is to extend this framework to a multi-agent system, which by its nature defines a totally different context. Indeed, when there are interdependencies among the key elements of a hybrid team (such as goals, changing mission environment, assets and threats/obstacles/constraints), interactive optimization algorithms should provide the tools for producing intelligent courses of action that are congruent with and overcome bounded rationality and cognitive biases inherent in human decision-making. Our work, using path planning as a domain of application, seeks to make progress towards this aim by providing a scientifically rigorous algorithmic framework for proactive agent autonomy.

UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation

Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.

Deep Learning Enhanced UAV Imagery for Critical Infrastructure Protection

Unmanned aerial vehicles (UAVs) have seen a significant increase in their commercial application because of various technological break-throughs. As UAVs are typically used in open environments for purposes such as military applications, surveillance, and delivery of commodities, they rely primarily on the visual signals recorded by the flying UAV. UAVs can be used for critical infrastructure protection where their surveillance capabilities are used for monitoring these sites. While carrying out such missions in open environments, visual degradation is an unavoidable concern. It has a negative impact on the performance and security of the system. We propose a deep-learning-based framework, called Aerialgan, to solve the visual degradation caused by haze in the atmosphere. We trained the proposed model using an adversarial training algorithm which is commonly known as the generative adversarial ntwork (GAN) and aims to enhance the hazy images collected by a UAV and generate a clean, haze-free image of the same scene. In addition, we present the Aerial Non-Homogeneous Hazy (ANHH) dataset, which contains over 66,000 pairs of hazy and ground truth aerial photos with realistic, non-homogeneous haze of various densities. We used performance metrics such as peak signal-to-noise ratio and structural similarity index to evaluate our model on ANHH and compare it with contemporary state-of-the-art techniques in image dehazing. The proposed technique can be very useful in improving the reliability of surveillance where UAVs are used for critical infrastructure protection applications.

An architecture for mission coordination of heterogeneous robots

Context:Robots can potentially collaborate to execute a variety of tasks in the service robots domain. However, developing applications of service robots can be complex due to the high level of uncertainty and required level of autonomy. Objective:We aim at contributing an architecture for the development of applications, capable of coordinating multi-robot missions, and that promotes modifiability and seamless integration of independently developed components. Method:In this work, we introduce MissionControl: an ensemble-based architecture to coordinate missions of heterogeneous robots to autonomously form coalitions. MissionControl comprises a component model and a runtime environment. The component model specifies how the system can be extended for different robot’s behaviors and environments. The runtime environment provides the processes required for coordinating the execution of missions at runtime. Results:We evaluated MissionControl in a simulated environment in the healthcare domain. We randomly generated 81 scenarios with uncertainty in the robots’ initial configurations. Then, each scenario was executed 8 times (i.e. 648 runs), where we evaluated the feasibility and efficiency of MissionControl for autonomously forming coalitions against a baseline approach that uses a random robot allocation. Statistical hypotheses testing yielded that MissionControl was able to achieve higher success rates while reducing the required time to conclude a mission, when compared to a baseline approach. We also perform an evaluation of the key quality attributes of the architecture, i.e. modifiability and integrability. Conclusions:MissionControl demonstrated itself able to coordinate multi-robot missions by autonomously assigning missions. Despite the error-prone robotic mission environment and demanding computational resources, MissionControl led to a significant increase in the success rate, while also decreasing the time required to conclude robotic missions when compared to a baseline approach.

Emergent Search of UAV Swarm Guided by the Target Probability Map

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms.

Voters, Neighbors, and City Performance in Environmental Sustainability Transitions

While most city governments in the United States acknowledge the importance of clean and healthy natural environment, their commitment to environmental sustainability varies widely. Scholarship on why some cities pursue the environmental agenda more than others is still evolving. This research adds to the model of municipal sustainability adoption by showing negative effects of residents’ political conservatism and positive effects of environmentally conscious neighboring cities on local sustainability action. The analysis of data for government finances and employment over the Great Recession also shows that commitment to sustainability was not associated with any distinct patterns in spending, debt or employment. Taken together, these findings point at a non-neutral role of social factors in sustainability transitions and suggest that cities pursuing the environmental mission can withstand tough economic times without substantial performance tradeoffs.

Lead titanate nanowires/polyamide-imide piezoelectric nanocomposites for high-temperature energy harvesting

With an increasing demand for self-powered microelectronics to be used in scientific tasks and exploration missions in extreme environments, the implementation of piezoelectric energy harvesting at high temperature is an important research focus for renewable energy sources. Piezoelectric composites made from piezoelectric ceramic fillers and polymer matrices combine high electromechanical coupling with flexibility, resulting in a versatile candidate for energy harvesting. In this research, a nanocomposite formed by lead titanate nanowires (PbTiO3 NWs) and a polyamide-imide (PAI) matrix is developed and shown to have an excellent piezoelectric response at high temperatures. By in situ measurements of the piezoelectric g31 and d31 coefficients of the nanocomposite films with different PbTiO3 NW weight fractions in the temperature range 25–250 °C, the dependence of temperature and filler concentration on the piezoelectric properties are fully characterized. In addition, the effect of NW alignment on piezoelectric properties through direct ink writing is also studied. Finally, a bending-mode energy harvester made from this nanocomposite is demonstrated to generate a maximum power density of 33 μW·m−2 at resonance and a power density of 20 μW·m−2 under low-frequency random excitation at 200 °C, while keeping a long operating life of at least 100 h. In conclusion, this work provides a new solution for high-temperature energy harvesting from low-frequency random vibrations using PbTiO3/PAI nanocomposites.

Programmable degrading engine powered photoactivated organic colloidal motors

Owing to environmental and health concerns, engineered nanobots are confronting tremendous challenges in the practical transition to industrial and civil applications. Even if the nanomaterials are made of biodegradable components, they still need to suffer a long-time decomposing process to completely destroy the nanostructures. Here, we report a straightforward and scalable preparation of light-driven organic colloidal motors assembled by macromolecular payloads and photoactivated azo-containing polymer engines through a continuous nanoprecipitation process. In response to UV irradiation, the light-fueled polymer engine enables converting light to chemical energy by photodegrading the polymer-aggregated phase into short-chained biodegradable residues and environmental-friendly nitrogen gas. The release behavior of the nitrogen independent of the surrounding chemical medium can be conveniently programmed for on-demand powering the motors (up to ∼120 μm/s) to carry out transport missions in various complicated environments (e.g. high viscous and high ionic solution, tumor matrix). The organic polymer engine can be completely deconstructed after the mission accomplishment for relieving potential environmental risks of the engineered nanomachines.

Drone Positioning System Using UWB Sensing and Out-of-Band Control

Research on drones becomes vigorous not only for single drone but also drone fleet, which operates multiple drones in a formation. To widen the applicability of drone fleet, high positioning accuracy and low latency are required. Most drone products or research use the Global Positioning System (GPS) for positioning, but the accuracy is insufficient for performing various drone missions in dense or indoor environments. In this paper, we present a novel Ultra-wideband (UWB) positioning system that uses an out-of-band network for control channel. Each UWB module serves as a remote sensor for detecting and positioning multiple drones. Unlike traditional anchor-tag based UWB ranging, all nodes in the proposed system have no distinction between the roles of anchor and tag by utilizing enhanced multidimensional scaling (MDS). We practically implemented our proposed system and evaluated the system with numerical experiments. Through the numerical analysis, we have found that the proposed system provides enough accuracy and low latency to be applied to the drone fleet.

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

Large Unmanned Aerial Vehicle (UAV) clusters, containing hundreds of UAVs, have widely been used in the modern world. Therein, mission planning is the core of large UAV cluster collaborative systems. In this paper, we propose a mission planning method by introducing the Simple Attention Model (SAM) into Dynamic Information Reinforcement Learning (DIRL), named DIRL-SAM. To reduce the computational complexity of the original attention model, we derive the SAM with a lightweight interactive model to rapidly extract high-dimensional features of the cluster information. In DIRL, dynamic training conditions are considered to simulate different mission environments. Meanwhile, the data expansion in DIRL guarantees the convergence of the model in these dynamic environments, which improves the robustness of the algorithm. Finally, the simulation experiment results show that the proposed method can adaptively provide feasible mission planning schemes with second-level solution speed and that it exhibits excellent generalization performance in large-scale cluster planning problems.

A Portable Six-Wheeled Mobile Robot With Reconfigurable Body and Self-Adaptable Obstacle-Climbing Mechanisms

Abstract Mobile robots can replace rescuers in rescue and detection missions in complex and unstructured environments and draw the interest of many researchers. This paper presents a novel six-wheeled mobile robot with a reconfigurable body and self-adaptable obstacle-climbing mechanisms, which can reconfigure itself to three locomotion states to realize the advantages of terrain adaptability, obstacle-crossing ability, and portability. Design criteria and mechanical design of the proposed mobile robot are first presented, based on which the geometry of the robot is modeled and the geometric constraint, static conditions, and motion stability condition for obstacle crossing of the robot are derived and formulated. Numerical simulations are then conducted to verify the geometric passing capability, static passing capability, and motion stability and to find feasible structure parameters of the robot in obstacle crossing. Further, a physical prototype of the proposed mobile robot is developed and integrated with mechatronic systems and remote control. Using the prototype, field experiments are carried out to verify the feasibility of the proposed design and theoretical derivations. The results show that the proposed mobile robot satisfies all the criteria set and is feasible for applications in disastrous rescuing scenarios.

Complete coverage problem of multiple robots with different velocities

Complete coverage, which is integral to many robotic applications, aims to cover an area as quickly as possible. In such tasks, employing multiple robots can reduce the overall coverage time by appropriate task allocation. Several multi-robot coverage approaches divide the environment into balanced subareas and minimize the maximum subarea of all robots. However, balanced coverage in many situations, such as in the cases of robots with different velocities and heterogeneous multi-robot systems, may have inefficient results. This study addresses the unbalanced complete coverage problem of multiple robots with different velocities for a known environment. First, we propose a novel credit model to transform the unbalanced coverage problem into a set of single-objective optimization problems, which can find a combinational optimal solution by optimizing each separate objective function of the single-objective optimization problem to alleviate the computational complexity. Then, we propose a credit-based algorithm composed of a cyclic region growth algorithm and a region fine-tuning algorithm. The cyclic region growth algorithm finds an initial solution to the single-objective optimization problems set by a regional growth strategy with multiple restricts, whereas the region fine-tuning algorithm reallocates the tasks of the partitions with too many tasks to the partitions with too few tasks by constructing a search tree, thereby converging the initial solution to the optimal solution. Simulation results indicate that compared with conventional multi-robot complete coverage problem algorithms, the credit-based algorithm can obtain the optimal solution with the increased number of robots and enlarged size of the mission environment.

Mechanism Design of a Transformable Crawling Robot and Feasibility Analysis for the Unstructured Environment

The better application of crawl robots depends on their ability to adapt to unstructured environments with significant variations in their structural shape and size. This paper presents the design and analysis of a novel robot with different locomotion configurations to move through varying environments. The leg of the robot, inspired by insects, was designed as a multi-link structure, including the Hoekens linkage and multiple parallel four-link mechanisms. The end trajectory was a symmetrical closed curve composed of an approximate straight line and a shell curve with a downward opening. The special trajectory allowed the robot to share drives and components to achieve structural deformation and locomotion. The structural characteristics of the crawl robot on the inner and outer arcs were obtained based on the working space. The constraint relationship between the structure size, the radius of the arc, and the coefficient of static friction with which the robot could crawl on the arc were established. The feasible support posture and support position of the robot under different arc radii were obtained. The simulation tested the locomotion of the robot on the plane, arc, and restricted space. The robot can be used for detection, search, and rescue missions in unstructured environments.

Pyroshock Response Prediction of Spacecraft Structure in Wide Frequency Domain Based on Acceleration FRF

The initiating explosive shock environment of an aerospace mission has the characteristics of instantaneity, high amplitude and a wide frequency domain. An improved method based on the acceleration frequency response function (FRF) and virtual mode synthesis method (VMSS) is proposed to predict the pyroshock response of a spacecraft structure in a wide frequency domain. Firstly, the statistical energy analysis (SEA) model of the spacecraft structure was established, and the FRF and modal density of the model were obtained. Then, the paper explains how, due to the small number of modes in the low-frequency band, the calculation results of the SEA method in the low-frequency band were not accurate enough. The FRF of the SEA model in the low-frequency band was modified by an FRF test of the structure. Finally, the shock response spectrum (SRS) was obtained based on the VMSS and the modified FRF. A shock experiment on the spacecraft structure was conducted by using the shock experiment system, which is based on a light-gas gun. The accurate shock force function and acceleration response results were obtained. The numerical results based on the improved method are in line with those in the experiment. This verifies that the novel method can better grasp the response characteristics of the structure in the broadband domain. The novel method effectively improves the response prediction accuracy of the SEA model in the relatively low-frequency band. While ensuring the computational efficiency, more accurate shock response results in a wide frequency domain were obtained. The novel method presented in this paper provides support of numerical analysis for pyroshock response prediction of spacecraft structure in a wide frequency domain.

Karst Cave as Terrestrial Simulation Platform to Test and Design Human Base in Lunar Lava Tube

Developing efficient approaches to building a suitable environment for humans on the moon play a key role in future long-term sustainable lunar exploration activities, which has motivated many countries to propose diverse plans to build a lunar base. The lava tubes discovered by the Kaguya mission offer huge potential sites to host such bases. Through computation and analysis, we show that lunar lava tubes offer stable structures, suitable temperatures, low radiation doses, and low meteorite impact rates. We summarize previous research results and put forward the conditions to find and use a suitable lunar lava tube for human habitation on the moon. The establishment of extraterrestrial bases still faces many technical bottlenecks; many countries have begun to use the earth’s environment for extraterrestrial exploration and simulation missions. In this regard, we proposed the idea of using the Earth’s karst caves to simulate extraterrestrial lava tubes, selected caves in Chongqing as the simulation site, and demonstrated the feasibility from both structural and environmental aspects. Finally, we proposed a karst cave simulation platform with three main research directions: cave sealing technology, efficient daylight system, and internal circulation research of artificial ecosystems containing natural soil and rock. We hope to promote the development of related research on extraterrestrial bases through simulation experiments.

Water Quality Map Extraction from Field Measurements Targetting Robotic Simulations

The assessment of the quality of water can be shortly defined as the analysis of its physical, chemical and biological characteristics in order to determine the degradation of freshwater resources. In this context, one of the latest technological methods for real-time data acquisition comes from the use of unmanned vehicles (aerial, surface and underwater). Therefore, the development of control strategies to perform environmental missions is crucial to manage water resources in an efficient and effective way. Prior to the actual implementation, some in-silico experiments are needed to test the proposals, which is one of the purposes of this work. This proposal, based on real experiments in a lake, presents a novel method for the construction of a water quality map based on polygons. The result is compared with a classical data generation method showing positive outcomes. The generated limnological map has a twofold purpose: to test set-based predictive controllers in simulation scenarios with an aquatic robot and to determine if there is a source of contamination in the analyzed region of the lake.

The contribution of the rural environmental registry to family farmers in the state of Amazonas, Brazil

Environmental issues continue to be on a growing agenda around the world. A large part of the global concerns is focused on the Amazon, even though it leaves aside almost the entire human contingent that inhabits that space. Except for the Indians. They disregard that many of the harmful environmental impacts are practiced by family farmers because fire is their greatest technology and, in many cases, the only one. Therefore, the management of this territory must create mechanisms and instruments capable of promoting the region's sustainable development, bringing together environmental and social interests, which only can be done through laws. The Rural Environmental Registry (CAR, acronym in Portuguese) aims to implement sustainable public policies. Thus, this literature review study aims to show CAR's contribution to family farmers operating in the State of Amazonas from a legal and environmental point of view. The results show that a) it is necessary to know the main legal aspects of the CAR to understand its environmental importance, b) knowing each of the steps in the preparation of the registry allows this instrument to fulfill its environmental mission as a result of legal discipline, c) the simplification of the regime granted to family farmers and similar populations allows the legal requirements to be fulfilled by this essential human contingent in the Amazon and d) the rigorous execution of the CAR allowed for several conflicts of overlapping rural areas to be resolved, which benefited family farmers and populations equivalent, pioneers in these areas. The conclusion states that implementing the Brazilian forest code is essential for family farming agribusiness, requiring technical support. The CAR emerges as a fundamental instrument.

Value Iteration Networks with Double Estimator for Planetary Rover Path Planning.

Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN suffers from several limitations, including sensitivity to initialization and poor performance in large-scale domains. We introduce the double value iteration network (dVIN), which decouples action selection and value estimation in the VI module, using the weighted double estimator method to approximate the maximum expected value, instead of maximizing over the estimated action value. We have devised a simple, yet effective, two-stage training strategy for VI-based models to address the problem of high computational cost and poor performance in large-size domains. We evaluate the dVIN on planning problems in grid-world domains and realistic datasets, generated from terrain images of a moon landscape. We show that our dVIN empirically outperforms the baseline methods and generalize better to large-scale environments.