Motion-encoded particle swarm optimization for moving target search using UAVs

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24\% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.Comment: Applied Soft Computing, 202

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

An Information Value Approach to Route Planning for UAV Search and Track Missions

This dissertation has three contributions in the area of path planning for Unmanned Aerial Vehicle (UAV) Search And Track (SAT) missions. These contributions are: (a) the study of a novel metric, G, used to quantify the value of the target information gained during a search and track mission, (b) an optimal planning horizon that minimizes time-error of a planning horizon when interrupted by Poisson random events, and (c) a modified Particle Swarm Optimization (PSO) algorithm for search missions that uses the prior target distribution in the generation of paths rather than just in the evaluation of them. UAV route planning is an important topic with many applications. Of these, military applications are the best known. This dissertation focuses on route planning for SAT missions that jointly optimize the conflicting objectives of detecting new targets and monitoring previously detected targets. The information theoretic approach proposed here is different from and is superior to existing approaches. One of the main differences is that G quantifies the value of the target information rather than the information itself. Several examples are provided to highlight G’s desirable properties. Another important component of path planning is the selection of a planning horizon, which specifies the amount of time to include in a plan. Unfortunately, little research is available to aid in the selection of a planning horizon. The proposed planning horizon is derived in the context of plan updates triggered by Poisson random events. To our knowledge, it is the only theoretically derived horizon available making it an important contribution. While the proposed horizon is optimal in minimizing planning time errors, simulation results show that it is also near optimal in minimizing the average time needed to capture an evasive target. The final contribution is the modified PSO. Our modification is based on the idea that PSO should be provided with the target distribution for path generation. This allows the algorithm to create candidate path plans in target rich regions. The modified PSO is studied using a search mission and is used in the study of G

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

New Development on Sense and Avoid Strategies for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) can carry out more complex civilian and military applications with less cost and more flexibility in comparison of manned aircraft. Mid-air collision thus becomes profoundly important considering the safe operation of air transportation systems, when UAVs are increasingly used more with various applications and share the same airspace with manned air vehicles. To ensure safe flights, UAVs have to configure Sense and Avoid (S&A) systems performing necessary maneuvers to avoid collisions. After analyzing the manner of S&A system, avoidance strategies based on a subset of possible collision scenarios are proposed in this thesis. 1) To avoid a face-to-face intruder, a feasible trajectory is generated by differential geometric guidance, where the constraints of UAV dynamics are considered. 2) The Biogeography Based Optimization (BBO) approach is exploited to generate an optimal trajectory to avoid multiple intruders’ threats in the landing phase. 3) By formulating the collision avoidance problem within a Markov Decision Process (MDP) framework, a desired trajectory is produced to avoid multiple intruders in the 2D plane. 4) MDP optimization method is extended to address the problem of optimal 3D conflict resolution involving multiple aircraft. 5) Considering that the safety of UAVs is directly related to the dynamic constraints, the differential flatness technique is developed to smoothen the optimal trajectory. 6) Energy based controller is designed such that the UAV is capable of following the generated trajectory

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Asset allocation in frequency and in 3 spatial dimensions for electronic warfare application

Indiana University-Purdue University Indianapolis (IUPUI)This paper describes two research areas applied to Particle Swarm Optimization (PSO) in an electronic warfare asset scenario. First, a three spatial dimension solution utilizing topographical data is implemented and tested against a two dimensional solution. A three dimensional (3D) optimization increases solution space for optimization of asset location. Topography from NASA's Digital Elevation Model is also added to the solution to provide a realistic scenario. The optimization is tested for run time, average distances between receivers, average distance between receivers and paired transmitters, and transmission power. Due to load times of maps and increased iterations, the average run times were increased from 123ms to 178ms, which remains below the 1 second target for convergence speeds. The spread distance between receivers was able to increase from 86km to 89km. The distance between receiver and its paired transmitters as well as the total received power did not change signi cannily. In the second research contribution, a user input is created and placed into an unconstrained 2D active swarm. This \human in the swarm" scenario allows a user to change keep-away boundaries during optimization. The blended human and swarm solution successfully implemented human input into a running optimization with a time delay. The results of this research show that a electronic warfare solutions with real 3D topography can be simulated with minimal computational costs over two dimensional solutions and that electronic warfare solutions can successfully optimize using human input data