Collision-aware Task Assignment for Multi-Robot Systems

We propose a novel formulation of the collision-aware task assignment (CATA) problem and a decentralized auction-based algorithm to solve the problem with optimality bound. Using a collision cone, we predict potential collisions and introduce a binary decision variable into the local reward function for task bidding. We further improve CATA by implementing a receding collision horizon to address the stopping robot scenario, i.e. when robots are confined to their task location and become static obstacles to other moving robots. The auction-based algorithm encourages the robots to bid for tasks with collision mitigation considerations. We validate the improved task assignment solution with both simulation and experimental results, which show significant reduction of overlapping paths as well as deadlocks

İNSANSIZ HAVA SİSTEMLERİ ROTA PLANLAMASI DİNAMİK ÇÖZÜM METOTLARI VE LİTERATÜR ARAŞTIRMASI

War tools and materials, which are an indicator of war strategy and development, vary with the technology. Modern Warfares in the information age, where near space and the space being used as a tool, differ from both the agricultural era wars, in which arrows and bows had been used as a tool, and industrial era battles, in which machine guns and tanks were employed. These characteristics influence this area of interest and area of influence in the area of responsibilities, and affect the ability and the necessity of modern armies as well. Being able to perform reconnaissance and surveillance missions in the most effective and efficient way is reliant on not only having the knowledge to create and invent them as well as having them in the inventor but also the ability of planning these systems with a modern scientific approach. Vehicle Routing Problem (VRP), encountered during the assignment of Unmanned Aerial Systems\Vehicles (UAS\V) which is being used at the strategic level, can lead to elevated costs in the defense sector as well as other sectors. For this reason, the efficient solution of route planning is a very important issue to provide major cost savings and to observe the targets timely.This study investigates the literature in “dynamic” route planning “solution” methods and defines the approaches for future “dynamic solution” studies of strategic UAVs which are being recently used in Turkey. Using this approach will increase the efficiency of usage of the UAVs and decrease the operating and project costs of them as well.Savaş stratejisinin bir göstergesi olan muharebe araç ve gereçleri, teknoloji ile birlikte değişiklik göstermektedir. Yakın uzay ve uzayın da bir araç olarak kullanıldığı bilgi çağının savaşları; ok ve yayın kullanıldığı tarım dönemi ile top ve tüfeklerin kullanıldığı sanayi dönemi savaşlarından farklılık göstermektedir. Bu farklılıklar, harekât bölgesinin etki ve ilgi alanını genişlettiği gibi modern orduların kabiliyetlerini ve gereksinimlerini de etkilemektedir. Keşif ve gözetleme görevinin en etkin ve verimli şekilde yapabilmesi, envanterindeki sistemlerin modern olması ile birlikte bu sistemleri bilimsel yaklaşımlarla planlayabilme yeteneğine bağlıdır. Stratejik seviyede kullanılan İnsansız Hava Araçlarının (İHA) görevlendirmeleri esnasında karşılaşılan Araç Rotalama Problemleri (ARP), diğer sektörlerde olduğu gibi savunma sektöründe de oldukça yüksek maliyetlere sebep olabilmektedir. Bu nedenle İHA rota planlamalarının verimli bir şekilde çözümü, büyük tasarruflar sağlayacak olması ve hedeflerin zamanında gözetlenebilmeleri açısından önemlidir. Bu çalışmanın amacı, İnsansız Hava Sistemlerinin (İHS) dinamik rota planlamasına yönelik yapılan çalışmalarının incelenmesi ve ileride yapılabilecek çözüm yaklaşımlarına yön verilmesidir. Bu sayede, Türkiye’de henüz kullanılmaya başlayan stratejik İHS’lerin görev etkinliklerinin artırılması, proje ve kullanım maliyetlerinin düşürülmesi hedeflenmektedir

Deployment of Real Time UAV Aerial Surveillance with Coverage Model

This thesis assesses the feasibility of applying coverage model to the problem of using unmanned aerial vehicles (UAVs) for aerial surveillances. The purpose of aerial surveillance is using sensors to cover the task area to obtain the information regarding to this area, such as environmental study. Comparing to static sensors, business purposes UAVs have higher mobility. Since static sensors have limited sensing range, it is not possible to use them to cover the task area. UAVs with sensors onboard can be used for surveillance and save the data to obtain more detailed information of the task area. The data retrieved by the sensors can be used for developing autonomous control algorithms for navigation of the UAV. However, there are some inevitable factors that shortens the flight time of the UAV. For example, considering the maximum payload of the UAV, the battery of UAV can usually last at most 30 minutes since it cannot be very large. It is very important to improve the efficiency of UAV surveillance by pre-designing the flight path for one UAVs or deployment positions for multiple UAVs

Optimal ship navigation and algorithms for stochactic obstacle scenes

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.This thesis is comprised of two different but related sections. In the first section, we consider the optimal ship navigation problem wherein the goal is to find the shortest path between two given coordinates in the presence of obstacles subject to safety distance and turn-radius constraints. These obstacles can be debris, rock formations, small islands, ice blocks, other ships, or even an entire coastline. We present a graph-theoretic solution on an appropriately-weighted directed graph representation of the navigation area obtained via 8-adjacency integer lattice discretization and utilization of the A∗ algorithm. We explicitly account for the following three conditions as part of the turn-radius constraints: (1) the ship’s left and right turn radii are different, (2) ship’s speed reduces while turning, and (3) the ship needs to navigate a certain minimum number of lattice edges along a straight line before making any turns. The last constraint ensures that the navigation area can be discretized at any desired resolution. We illustrate our methodology on an ice navigation example involving a 100,000 DWT merchant ship and present a proof- of-concept by simulating the ship’s path in a full-mission ship handling simulator at Istanbul Technical University. In the second section, we consider the stochastic obstacle scene problem wherein an agent needs to traverse a spatial arrangement of possible-obstacles, and the status of the obstacles may be disambiguated en route at a cost. The goal is to find an algorithm that decides what and where to disambiguate en route so that the expected length of the traversal is minimized. We present a polynomial-time method for a graph-theoretical version of the problem when the associated graph is restricted to parallel avenues with fixed policies within the avenues. We show how previously proposed algorithms for the continuous space version can be adapted to a discrete setting. We propose a gener- alized framework encompassing these algorithms that uses penalty functions to guide the navigation in realtime. Within this framework, we introduce a new algorithm that provides near-optimal results within very short execution times. Our algorithms are illustrated via computational experiments involving synthetic data as well as an actual naval minefield data set. Keywords: Graph theory, shortest path, ship navigation, probabilistic path planning, stochastic dynamic programming, Markov decision process, Canadian traveler’s problemContents Declaration of Authorship ii Abstract iv ¨ Oz v Acknowledgments vii List of Figures x List of Tables xi 1 Optimal Ship Navigation with Safety Distance and Realistic Turn Con- straints 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 The Optimal Ship Navigation Problem . . . . . . . . . . . . . . . . . . . . 4 1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Safety Distance Constraints . . . . . . . . . . . . . . . . . . . . . . 5 1.4.2 Lattice Discretization . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.3 Ship-Turn Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4.4 The A∗ Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.5 Smoothing the Optimal Path . . . . . . . . . . . . . . . . . . . . . 13 1.5 Ice Navigation Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.6 Simulator Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.7 Summary, Conclusions, and Future Research . . . . . . . . . . . . . . . . 18 2 Algorithms for Stochastic Obstacle Scenes 21 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 The Stochastic Obstacle Scene Problem: Continuous vs. Discrete Settings 23 2.2.1 Deciding Where to Disambiguate: Single Disk Case . . . . . . . . 23 2.2.2 Deciding Where to Disambiguate: Two Disks Case . . . . . . . . . 25 2.2.3 Discretization of the Continuous Setting: An Example . . . . . . . 27 2.3 Definition of the Stochastic Obstacle Scene Problem . . . . . . . . . . . . 27 2.3.1 Continuous SOSP . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.3.2 Discrete SOSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.3.3 Discretized SOSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4 A Polynomial Algorithm for Discrete SOSP on Parallel Graphs . . . . . . 29 2.5 Discrete Adaptation of the Simulated Risk Disambiguation Algorithm . . 30 2.5.1 Adaptation to Discrete SOSP . . . . . . . . . . . . . . . . . . . . . 30 2.5.2 Adaptation to Discretized SOSP . . . . . . . . . . . . . . . . . . . 32 2.6 Discrete Adaptation of the Reset Disambiguation Algorithm . . . . . . . . 33 2.7 Generalizing SRA and RDA: Penalty-Based Algorithms and DTA . . . . . 34 2.7.1 Illustration of the Algorithms . . . . . . . . . . . . . . . . . . . . . 36 2.8 Computational Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.8.1 Environment A (The COBRA Data) Experiments . . . . . . . . . 40 2.8.2 Environment B Experiments . . . . . . . . . . . . . . . . . . . . . 41 2.8.3 Environment C Experiments . . . . . . . . . . . . . . . . . . . . . 43 2.9 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 A Impact of Cost Change in Parallel Graphs 47 Bibliograph

Models and algorithms for trauma network design.

Trauma continues to be the leading cause of death and disability in the US for people aged 44 and under, making it a major public health problem. The geographical maldistribution of Trauma Centers (TCs), and the resulting higher access time to the nearest TC, has been shown to impact trauma patient safety and increase disability or mortality. State governments often design a trauma network to provide prompt and definitive care to their citizens. However, this process is mainly manual and experience-based and often leads to a suboptimal network in terms of patient safety and resource utilization. This dissertation fills important voids in this domain and adds much-needed realism to develop insights that trauma decision-makers can use to design their trauma network. In this dissertation, we develop multiple optimization-based trauma network design approaches focusing minimizing mistriages and, in some cases, ensuring equity in care among regions. To mimic trauma care in practice, several realistic features are considered in our approach, which include the consideration of: (i) both severely and non-severely injured trauma patients and associated mistriages, (ii) intermediate trauma centers (ITCs) along with major trauma centers (MTCs), (iii) three dominant criteria for destination determination, and (iv) mistriages in on-scene clinical assessment of injuries. Our first contribution (Chapter 2) proposes the Trauma Center Location Problem (TCLP) that determines the optimal number and location of major trauma centers (MTCs) to improve patient safety. The bi-objective optimization model for TCLP explicitly considers both types of patients (severe and non-severe) and associated mistriages (specifically, system-related under- and over-triages) as a surrogate for patient safety. These mistriages are estimated using our proposed notional tasking algorithm that attempts to mimic the EMS on-scene decision of destination hospital and transportation mode. We develop a heuristic based on Particle Swarm Optimization framework to efficiently solve realistic problem sizes. We illustrate our approach using 2012 data from the state of OH and show that an optimized network for the state could achieve 31.5% improvement in patient safety compared to the 2012 network with the addition of just one MTC; redistribution of the 21 MTCs in the 2012 network led to a 30.4% improvement. Our second contribution (Chapter 3) introduces a Nested Trauma Network Design Problem (NTNDP), which is a nested multi-level, multi-customer, multi-transportation, multi-criteria, capacitated model. The NTNDP model has a bi-objective of maximizing the weighted sum of equity and effectiveness in patient safety. The proposed model includes intermediate trauma centers (TCs) that have been established in many US states to serve as feeder centers to major TCs. The model also incorporates three criteria used by EMS for destination determination; i.e., patient/family choice, closest facility, and protocol. Our proposed ‘3-phase’ approach efficiently solves the resulting MIP model by first solving a relaxed version of the model, then a Constraint Satisfaction Problem, and a modified version of the original optimization problem (if needed). A comprehensive experimental study is conducted to determine the sensitivity of the solutions to various system parameters. A case study is presented using 2019 data from the state of OH that shows more than 30% improvement in the patient safety objective. In our third contribution (Chapter 4), we introduce Trauma Network Design Problem considering Assessment-related Mistriages (TNDP-AM), where we explicitly consider mistriages in on-scene assessment of patient injuries by the EMS. The TNDP-AM model determines the number and location of major trauma centers to maximize patient safety. We model assessment-related mistriages using the Bernoulli random variable and propose a Simheuristic approach that integrates Monte Carlo Simulation with a genetic algorithm (GA) to solve the problem efficiently. Our findings indicate that the trauma network is susceptible to assessment-related mistriages; specifically, higher mistriages in assessing severe patients may lead to a 799% decrease in patient safety and potential clustering of MTCs near high trauma incidence rates. There are several implications of our findings to practice. State trauma decision-makers can use our approaches to not only better manage limited financial resources, but also understand the impact of changes in operational parameters on network performance. The design of training programs for EMS providers to build standardization in decision-making is another advantage