1,150 research outputs found
Routing UAVs to Co-Optimize Mission Effectiveness and Network Performance with Dynamic Programming
In support of the Air Force Research Laboratory\u27s (AFRL) vision of the layered sensing operations center, command and control intelligence surveillance and reconnaissance (C2ISR) more focus must be placed on architectures that support information systems, rather than just the information systems themselves. By extending the role of UAVs beyond simply intelligence, surveillance, and reconnaissance (ISR) operations and into a dual-role with networking operations we can better utilize our information assets. To achieve the goal of dual-role UAVs, a concrete approach to planning must be taken. This research defines a mathematical model and a non-trivial deterministic algorithmic approach to determining UAV placement to support ad-hoc network capability, while maintaining the valuable service of surveillance activities
Simultaneous Search and Monitoring by Unmanned Aerial Vehicles
Simultaneous Search and Monitoring (SSM) is studied in this paper for a single Unmanned Aerial Vehicle (UAV) searcher and multiple moving ground targets. Searching for unknown targets and monitoring known targets are two intrinsically related problems, but have mostly been addressed in isolation. We combine the two problems with a joint objective function in a Partially Observable Markov Decision Process (POMDP). An online policy planning approach is proposed to plan a reactive policy to solve the POMDP, using both MonteCarlo sampling and Simulated Annealing. The simulation result shows that the searcher will successfully find unknown targets without losing known ones. We demonstrate, with a theoretical proof and comparative simulations, that the proposed approach can deliver a better performance than conventional foresight optimization methods
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Footprint and height corrections for UAV-borne gamma-ray spectrometry studies
Advancements in the development of gamma-ray spectrometers (GRS) have led to small and lightweight spectrometers that can be used under unmanned aerial vehicles (UAVs). Airborne GRS measurements are used to determine radionuclide concentrations in the ground, among which the natural occurring radionuclides K-40, U-238, and Th-232. For successful applications of these GRS sensors, it is important that absolute values of concentrations can be measured. To extract these absolute radionuclide concentrations, airborne gamma-ray data has to be corrected for measurement height. However, the current analysis models are only valid for the height range of 50-250 m. The purpose of this study is to develop a procedure that correctly predicts the true radionuclide concentration in the ground when measuring in the UAV operating range of 0-40 m. An analytical model is developed to predict the radiation footprint as a function of height. This model is used as a tool to properly determine a source-detector geometry to be used in Monte-Carlo simulations of detector response at various elevations between 0 and 40 m. The analytical model predicts that the smallest achievable footprint at 10 m height lies between 22 and 91 m and between 40 and 140 m at 20 m height. By using Monte-Carlo simulations it is shown that the analytical model correctly predicts the reduction in full energy peak gamma-rays, but does not predict the Compton continuum of a spectrum as a function of height. Therefore, Monte-Carlo simulations should be used to predict the shape and intensity of gamma-ray spectra as a function of height. A finite set of Monte-Carlo simulations at intervals of 5 m were used for the analysis of GRS measurements at heights up to 35 m. The resulting radionuclide concentrations at every height agree with the radionuclide concentration measured on the ground
An investigation into hazard-centric analysis of complex autonomous systems
This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be
improved with information obtained with model-based dynamic simulation, using a Monte Carlo
approach, to update a Bayesian Belief model representing the expected relations between cause and
effects – and thereby produce an enhanced HAZOP. The work considers how the expertise of a
hazard and operability study team might be augmented with access to behavioural models,
simulations and belief inference models. This incorporates models of dynamically complex system
behaviour, considering where these might contribute to the expertise of a hazard and operability study
team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire
containing behavioural outputs from a representative systems model, responses were collected from a
group with relevant domain expertise. From this it is argued that the quality of analysis is dependent
upon the experience and expertise of the participants but this might be artificially augmented using
probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of
an improved exemplar system dynamics model are used to condition a behavioural inference model
and also to generate measures of emergence associated with the deviation parameter used in the study.
A Bayesian approach towards probability is adopted where particular events and combinations of
circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice.
Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and
operability study, conditioned by the likely occurrence of flaw events causing specific deviant
behaviour from evidence observed in the system dynamical behaviour, may combine intuitive
estimates based upon experience and expertise, with quantitative statistical information representing
plausible evidence of safety constraint violation. A further behavioural measure identifies potential
emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the
awareness of potential hazard cases
Analysis of synchronous localization systems for UAVs urban applications
[EN] Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Crà mer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.S
An Overview of Drone Energy Consumption Factors and Models
At present, there is a growing demand for drones with diverse capabilities
that can be used in both civilian and military applications, and this topic is
receiving increasing attention. When it comes to drone operations, the amount
of energy they consume is a determining factor in their ability to achieve
their full potential. According to this, it appears that it is necessary to
identify the factors affecting the energy consumption of the unmanned air
vehicle (UAV) during the mission process, as well as examine the general
factors that influence the consumption of energy. This chapter aims to provide
an overview of the current state of research in the area of UAV energy
consumption and provide general categorizations of factors affecting UAV's
energy consumption as well as an investigation of different energy models
Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning
Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulting performance. Learning techniques are capable of adapting the model and providing better policies asymptotically compared to cooperative planners, yet they often violate the safety conditions of the system due to their exploratory nature. Moreover they frequently require an impractically large number of interactions to perform well. This paper introduces the intelligent Cooperative Control Architecture (iCCA) as a framework for combining cooperative planners and reinforcement learning techniques. iCCA improves the policy of the cooperative planner, while reduces the risk and sample complexity of the learner. Empirical results in gridworld and task assignment for fuel-limited UAV domains with problem sizes up to 9 billion state-action pairs verify the advantage of iCCA over pure learning and planning strategies
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