Multisensor Out Of Sequence Data Fusion for Estimating the State of Discrete Control Systems

The fusion center of a complex control system estimates its state with the information provided by different sensors. Physically distributed sensors, communication networks, pre-processing algorithms, multitasking, etc, introduce non-systematic delays in the arrival of information to the fusion center, making the information available out-of-sequence (OOS). For real-time control systems, the state has to be efficiently estimated with all the information received so far. So, several solutions of the OOS problem for dynamic multiple-input multiple-output (MIMO) discrete control systems traditionally solved by the Kalman filter (KF) have been proposed recently. This paper presents two new streamlined algorithms for the linear and non-linear case. IFAsyn, the linear algorithm, is equivalent to other optimal solutions but more general, efficient and easy to implement. EIFAsyn, the nonlinear one, is a new solution of the OOS problem in the extended KF (EKF) framework

Hierarchical mission planning with a GA-optimizer for unmanned high altitude pseudo-satellites

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions

Minimum time search in real-world scenarios using multiple UAVs with onboard orientable cameras

This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.This work was supported by Airbus under SAVIER AER30459 projec

Unified fusion system based on bayesian networks for autonomous mobile robots

A multisensor fusion system that is usedfor estimating the location of a robot and the state of the objects around is presented. The whole fusion system has been implemented as a Dynamic Bayesian Networks (DBN) with the purpose of having a homogenous and formalized way of capturing the dependencies that exist between the robot location, the state of the environment, and all the sensorial data. At this stage of the research it consists of two independent DBNs, one for estimating the robot location and another for building an occupancy probabilistic map of the environment, which are the basis of a unified fusion system. The dependencies of the variables and information in the two DBN will be captured by a unique DBN constructed by adding arcs (and nodes if necessary) between the two DBN. The DBN implemented so far can be used in robots with different sets of sensors

ART-GCS: an adaptive real-time multi-agent ground control station

Ground Control Stations (GCS) are essential tools to monitor and command real-world complex missions involving Unmanned Vehicles (UVs). As the number and types of UVs in the mission grows, implementing a robust and adaptable GCS, capable of simplifying and reducing operator' interactions and mental workloads, becomes an engineering challenge. To address it, this paper presents a new Adaptive-Real-Time (ART)-GCS that 1) allows to monitor and control a runtime changing number of heterogeneous UVs, 2) adapt its GUI to the mission requirements and operators workload to minimize their fatigue and stress, and 3) provide support to experiments with actual and simulated UVs. To show its benefits in real-world missions, this paper presents a field experiment where, for safety reasons, a simulated unmanned aerial vehicle has to find an oil-spill that must be enclosed by a containment boom dragged by two real unmanned surface vehicles

Minimum time search in uncertain dynamic domains with complex sensorial platforms

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models

Ant colony optimization for multi-UAV minimum time search in uncertain domains

This paper presents a new approach based on ant colony optimization (ACO) to determine the trajectories of a fleet of unmanned air vehicles (UAVs) looking for a lost target in the minimum possible time. ACO is especially suitable for the complexity and probabilistic nature of the minimum time search (MTS) problem, where a balance between the computational requirements and the quality of solutions is needed. The presented approach includes a new MTS heuristic that exploits the probability and spatial properties of the problem, allowing our ant based algorithm to quickly obtain high-quality high-level straight-segmented UAV trajectories. The potential of the algorithm is tested for different ACO parameterizations, over several search scenarios with different characteristics such as number of UAVs, or target dynamics and location distributions. The statistical comparison against other techniques previously used for MTS (ad hoc heuristics, cross entropy optimization, bayesian optimization algorithm and genetic algorithms) shows that the new approach outperforms the others.This work was supported by Airbus under the SAVIER AER-30459 project

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

Harmful Algal and Cyanobacterial Blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world's water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces DEVS-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with Model Based Systems Engineering (MBSE) and Cyber-Physical Systems (CPS) infrastructure for dynamic environments

Enhancing EJsS with extension plugins

Easy JavaScript Simulations (EJsS) is an open-source tool that allows teachers with limited programming experience to straightforwardly bundle an interactive computer science or engineer simulation in an HTML+ JavaScript webpage. Its prominent place in Physics (where it has won several prizes) should not hinder its application in other fields (such as building the front-end of remote laboratories or learning analytics) after having adapted part of the functionality of EJsS to them. To facilitate the future inclusion of new functionalities in EJsS, this paper presents a new version of this tool that allows the enhancement of EJsS, letting it incorporate new tools and change its graphical user interface, by means of extension plugins (special software libraries). To illustrate the benefits of this distributable self-contained non-intrusive strategy, the paper (a) discusses the new methodological possibilities that the Plugins bring to EJsS developers and users, and (b) presents three plugins: one to support the plugin management and the others to easily set up a streamlined remote laboratory. Moreover, the paper also presents the main characteristics of that remote lab to allow readers take advantage of EJsS and the three plugins to set up new online experiments for their students quickly