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Quality and safety of the cold chain undergo strict international regulations that identify storage and shipping temperatures. In fact, the improper handling and transportation of temperature-sensitive products such as food and pharmaceuticals may have harmful effects on human health and a negative economic impact. A passive RFID tag modified with a copper-doped ionic liquid was used to detect the crossing of a temperature threshold (8 °C) during the shipping of medical products. The tag was insensitive to humidity variations and irreversibly changed its status once temperature exceeded the ionic liquid melting point, which can be tuned by changing the concentration of dopant.

The I-READ 4.0 project represents an integrated and autonomous Cyber Physical System, for automatic management of very large warehouses with high-stock rotation index. Thanks to an internet of UHF-RFID readers, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in indoor scenario. The proposed solution can be employed independently on the warehouse sizes, the good types and the spatial resolution required for item localization by virtue of its low-cost, reconfigurability and scalability. The I-READ 4.0 system allows also to increase the safety levels of the human operators within the warehouse.

Theoretical modeling of the aeroelastic static instability with the possibility of tracing post-critical branches (via arc length technique) in the framework of Joined Wings has never been presented before. A complete formulation of the numerical iterative method of solution of the aeroelastic equations is presented. The true critical condition is compared with the divergence speed evaluated by solving an eigenvalue problem about a steady state equilibrium, showing how this last approach may be unreliable and even nonconservative. This work also explores the theoretical implications of using mechanical loads to mimic the real loading conditions. A physical interpretation based on the aeroelastic effects, overconstrained nature of the system, and the bending/torsion coupling, is provided to interpret the conditions that lead to the snap-divergence.

The ability of autonomous systems to make complex decisions is becoming an increasingly commonplace requirement for many cooperative control operations, including the management of teams of robots such as unmanned aerial vehicles (UAV). Central to this research is the requirement to optimize the vehicle decisions, such as route planning and allocation of team resources, while operating in a dynamic and uncertain environment. Even with the advent of increasingly sophisticated vehicle sensors that can improve the information about the surroundings, uncertainty remains a ubiquitous feature of UAV applications and a key issue in UAV research.

While there is currently significant interest in developing Unmanned Aerial Systems (UASs) that can be supervised by a single operator, the majority of these systems focus on Intelligence, Surveillance, and Reconnaissance (ISR) domains. One domain that has received significantly less attention is the use of multiple UASs to insert or extract supplies or people. To this end, MAVIES (Multi-Autonomous Vehicle Insertion-Extraction System) was developed to allow a single operator the ability to supervise a primary cargo Unmanned Aerial Vehicle (UAV) along with multiple scouting UAVs. This paper will detail the development of the design requirements generated through a Hybrid Cognitive Task Analysis (hCTA) and the display that resulted from these efforts. A major innovation in the hCTA process in this effort was the alteration of the traditional decision ladder process to specifically identify decision-making tasks that must be augmented with automation.

A scalable human-in-the-loop decision support system has been built around an active learning algorithm operating on aircraft engine time series data. The system integrates hierarchical clustering and active learning algorithms backed by a big data analytics ecosystem with a browser-based user interface. This combination enables multiple expert users to efficiently train a model to classify aircraft engine behavior by prioritizing the segments of flight for human analysis. This system lowers the time required from human experts by eliminating unnecessary labelling effort and supporting the aircraft maintenance industry's service technicians.

Real-time scheduling and task assignment for multiple Unmanned Vehicles (UVs) in uncertain environments will require the computational ability of optimization algorithms combined with the judgment and adaptability of human supervisors. Identifying the characteristics that make a scheduling algorithm suitable for human-computer collaboration is essential for the development of an effective scheduling system. This high-level systems analysis paper begins the process of deriving requirements for collaborative scheduling algorithms by conducting a survey of 117 publications within the past five years in academia and industry on multiple UV scheduling algorithms. The goal of the survey is to determine the types and frequency of scheduling algorithms that are currently in use and to classify the characteristics and capabilities of these algorithms. Results show that academia has settled on meta-heuristic and auction-based algorithms as providing the best balance of performance and computational speed. In industry, however, the most widely used solution methods are “iterative” approaches that monotonically improve the schedule with further iterations. Industry-developed algorithms are more likely to be capable of scheduling heterogeneous UVs, but university researchers have developed more algorithms that can account for uncertainty and provide estimates of robustness. The different objectives of industry practitioners and academic researchers may be driving these disparities. Addressing this gap will be essential to the development and adoption of future humancomputer collaborative scheduling systems.

With the increased use of Unmanned Aerial Vehicles (UAVs), it is envisioned that UAV operators will become high level mission supervisors, responsible for information management and task planning. In the context of search missions, operators supervising a large number of UAVs can become overwhelmed with the sheer amount of information collected by the UAVs, making it difficult to optimize the information collection or direct their attention to the relevant data. Novel decisionsupport methods that account for realistic operator performance will therefore be required to aid the operators. This paper considers a decision support formulation for sequential search tasks, and discusses a non-preemptive scheduling formulation for a single operator performing a search mission in a time-constrained environment. The formulation is then generalized to include operator performance obtained from previous human-in-the-loop experiments, and presents one of the principal contributions of the paper. The sensitivity of the proposed model is analyzed in the presence of uncertainty to the operator model and search times, and a comparison is made between the expected performance difference between this scheduling system and a greedy scheduling strategy representative of operator planning. The paper concludes with the design of a human-in-the-loop experiment for a scheduling, replanning task for a simulated UAV mission.

Conference agenda: http://www.aiaa.org/agenda.cfm?lumeetingid=1998, This paper analyzes task assignment for heterogeneous air vehicles using a guaranteed conflict-free assignment algorithm, the Consensus Based Bundle Algorithm (CBBA). We extend this recently proposed algorithm to handle two realistic multi- UAV operational complications. Our rst extension accounts for obstacle regions in order to generate collision free paths for UAVs. Our second extension reduces task planner sensitivity to sensor measurement noise, and thereby minimizes churning behavior in flight paths. After integrating our enhanced CBBA module with a 3D visualization and interaction software tool, we simulate multiple aircraft servicing stationary and moving ground targets. Preliminary simulation results establish that consistent, conflict-free multi-UAV path assignments can be calculated on the order of a few seconds. The enhanced CBBA consequently demonstrates signfi cant potential for real-time performance in stressing environments.

http://www.aiaa.org/agenda.cfm?lumeetingid=1998, Information consensus in sensor networks has received much attention due to its numerous applications in distributed decision making. This paper discusses the problem of a distributed group of agents coming to agreement on a probability vector over a network, such as would be required in a decentralized estimation of state transition probabilities or agreement on a probabilistic search map. Unique from other recent consensus literature, however, the agents in this problem must reach agreement while accounting for the uncertainties in their respective probabilities, which are formulated according to generally non-Gaussian distributions. The first part of this paper considers the problem in which the agents seek agreement to the centralized Bayesian estimate of the probabilities, which is accomplished using consensus on hyperparameters. The second part shows that the new hyperparameter consensus methodology can ensure convergence to the centralized estimate even while measurements of a static process are occurring concurrently with the consensus algorithm. A machine repair example is used to illustrate the advantages of hyperparameter consensus over conventional consensus approaches., United States. Air Force Office of Scientific Research (Grant FA9550-08-1-0086)

The performance of many complex UAV decision-making problems can be extremely sensitive to small errors in the model parameters. One way of mitigating this sensitivity is by designing algorithms that more effectively learn the model throughout the course of a mission. This paper addresses this important problem by considering model uncertainty in a multi-agent Markov Decision Process (MDP) and using an active learning approach to quickly learn transition model parameters. We build on previous research that allowed UAVs to passively update model parameter estimates by incorporating new state transition observations. In this work, however, the UAVs choose to actively reduce the uncertainty in their model parameters by taking exploratory and informative actions. These actions result in a faster adaptation and, by explicitly accounting for UAV fuel dynamics, also mitigates the risk of the exploration. This paper compares the nominal, passive learning approach against two methods for incorporating active learning into the MDP framework: (1) All state transitions are rewarded equally, and (2) State transition rewards are weighted according to the expected resulting reduction in the variance of the model parameter. In both cases, agent behaviors emerge that enable faster convergence of the uncertain model parameters to their true values.

Markov decision processes (MDPs) are a natural framework for solving multiagent planning problems since they can model stochastic system dynamics and interdependencies between agents. In these approaches, accurate modeling of the system in question is important, since mismodeling may lead to severely degraded performance (i.e. loss of vehicles). Furthermore, in many problems of interest, it may be dicult or impossible to obtain an accurate model before the system begins operating; rather, the model must be estimated online. Therefore, an adaptation mechanism that can estimate the system model and adjust the system control policy online can improve performance over a static (o-line) approach. This paper presents an MDP formulation of a multi-agent persistent surveillance problem and shows, in simulation, the importance of accurate modeling of the system. An adaptation mechanism, consisting of a Bayesian model estimator and a continuouslyrunning MDP solver, is then discussed. Finally, we present hardware flight results from the MIT RAVEN testbed that clearly demonstrate the performance benefits of this adaptive approach in the persistent surveillance problem.

The goal of multiple model estimation is to estimate the state and covariance of a hybrid system. In this paper, we are concerned about stochastic hybrid estimation, where common applications include Air Traffic Control (ATC) and UAV multi-target tracking problems. The basic modeling assumption is that the system switches between individual dynamic models according to a probabilistic process modeled with a known Markov Chain. Unfortunately, uncertainty in the parameters of this Markov Chain can degrade estimator performance by generating a covariance mismatch from the true covariance. A main concern of this paper is the issue of covariance underestimation in which the estimator is overly confident about the state estimate, as it can ultimately lead to increased estimation errors and tracking inefficiencies. We therefore introduce a new filter formulation that tackles the specific problem of estimator overconfidence and find the largest covariance matrix admissible given a Bayesian prior on the uncertain Markov Chain parameters. At each step of the filter, the uncertain Markov Chain is propagated by using Monte Carlo sampling, and a small quadratic program is solved that maximizes the trace of the robust covariance matrix within an admissible set. Our simulations show improved estimator performance over techniques that do not account for this uncertainty in the context of two tracking problems.

This paper describes the implementation of a CPU which can operate successfully in hostile environments such as an orbiting space vehicle. The design goal is to reduce sensitivity to the effects of radiation which can cause malfunctions in electronic circuit or a complete failure of the system. Moreover, to test performance and collect data, special hardware structures are integrated on silicon to collect data on detected errors.

This paper presents a modified formulation of the classical task assignment problem that has recently been used to coordinate teams of UAVs. The main contribution is a version of the task assignment problem that can be used to tailor the control system to mitigate the eect of noise in the situational awareness on the solution. The net eect will be to limit the rate of change in the reassignment in a well-defined manner. The approach here is to perform reassignment at the rate that information is updated, which enables immediate reaction to any significant changes in the environment. We demonstrate that the modified formulation can be interpreted as a noise rejection algorithm that can be tuned to reduce the eect of variation in the uncertain parameters in the problem. Simulations are presented to demonstrate the eectiveness of this algorithm.