Convergence Rate For The Ordered Upwind Method

The ordered upwind method (OUM) is used to approximate the viscosity solution of the static Hamilton---Jacobi---Bellman with direction-dependent weights on unstructured meshes. The method has been previously shown to provide a solution that converges to the exact solution, but no convergence rate has been theoretically proven. In this paper, it is shown that the solutions produced by the OUM in the boundary value formulation converge at a rate of at least the square root of the largest edge length in the mesh in terms of maximum error. An example with similar order of numerical convergence is provided.postprin

Path planning for autonomous vehicles

In this study, we first address the problem of visibility-based target tracking for a team of mobile observers trying to track a team of mobile targets. Initially, we introduce the notion of pursuit fields for a single observer to track a single target around a corner based on the previous work. Pursuit fields are used to generate navigation strategies for a single observer. In order to account for the scenario with the presence of more than one observer or target, we propose a hierarchical approach. At first a ranking and aggregation technique is used for allocating each observer to a target. Subsequently, each observer computes its navigation strategy based on the results of the single observer-single target problem, thereby, decomposing a large multi-agent problem into numerous 2-agent problems. Based on the aforementioned analysis, we present a scalable algorithm that can accommodate an arbitrary number of observers and targets. The performance of this algorithm is evaluated based on simulation and implementation. To implement the strategy in reality, we further propose a setup of omni-directional camera, which can be used to get the visual information of the surroundings. With the help of this setup, we apply a position estimation technique for the pursuer to locate the evader. Experimental results show that the error has considerable effect when the measuring distance is very large. Due to this reason, the aforementioned tracking strategy is modified to keep the evader in an effective range for estimation. Finally, based on the error in position estimation, we present PID controllers for the pursuer to track the evader along a straight line. The responses of the proposed controllers are given by simulations. Considering the situation that pursuer does not have an on board vision sensor, we propose a novel tracking strategy based on the information on social network. We first introduce the notion of common agents, who take pictures around and share them on social network website. In order to take advantage of these images, a network evolution algorithm and an image scanning algorithm are presented. Based on the information from these images, evader can be located accordingly. Implementation results are presented to validate the feasibility. In the rest of the thesis, we address the scheduling and motion planning problem for an autonomous grain cart serving multiple combines. In the first part, we present the mathematical models of both combine harvester and grain cart. Based on the models, we propose a scheduling scheme which allows grain cart to unload all the combines without interruption in the harvesting activity. The proposed scheme is generalized to an arbitrary number of combines. In the second part, we present path planning analysis for the grain cart to switch between two combines. A numerical approach and a primitive-based approach are considered to obtain the time-optimal solution. The former approach needs a value function corresponding to the goal position to be computed beforehand. Based on the value function, a time-optimal path can be obtained accordingly. In the latter approach, path consists of singular primitives and regular primitives which ensure local time optimality. Finally, simulation results are presented to validate the feasibility of the proposed techniques

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Optimal Direction-Dependent Path Planning for Autonomous Vehicles

The focus of this thesis is optimal path planning. The path planning problem is posed as an optimal control problem, for which the viscosity solution to the static Hamilton-Jacobi-Bellman (HJB) equation is used to determine the optimal path. The Ordered Upwind Method (OUM) has been previously used to numerically approximate the viscosity solution of the static HJB equation for direction-dependent weights. The contributions of this thesis include an analytical bound on the convergence rate of the OUM for the boundary value problem to the viscosity solution of the HJB equation. The convergence result provided in this thesis is to our knowledge the tightest existing bound on the convergence order of OUM solutions to the viscosity solution of the static HJB equation. Only convergence without any guarantee of rate has been previously shown. Navigation functions are often used to provide controls to robots. These functions can suffer from local minima that are not also global minima, which correspond to the inability to find a path at those minima. Provided the weight function is positive, the viscosity solution to the static HJB equation cannot have local minima. Though this has been discussed in literature, a proof has not yet appeared. The solution of the HJB equation is shown in this work to have no local minima that is not also global. A path can be found using this method. Though finding the shortest path is often considered in optimal path planning, safe and energy efficient paths are required for rover path planning. Reducing instability risk based on tip-over axes and maximizing solar exposure are important to consider in achieving these goals. In addition to obstacle avoidance, soil risk and path length on terrain are considered. In particular, the tip-over instability risk is a direction-dependent criteria, for which accurate approximate solutions to the static HJB equation cannot be found using the simpler Fast Marching Method. An extension of the OUM to include a bi-directional search for the source-point path planning problem is also presented. The solution is found on a smaller region of the environment, containing the optimal path. Savings in computational time are observed. A comparison is made in the path planning problem in both timing and performance between a genetic algorithm rover path planner and OUM. A comparison in timing and number of updates required is made between OUM and several other algorithms that approximate the same static HJB equation. Finally, the OUM algorithm solving the boundary value problem is shown to converge numerically with the rate of the proven theoretical bound

Aeronautical Engineering: A continuing bibliography with indexes, supplement 153, October 1982

This bibliography lists 535 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in September 1982

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

HM 24: Blue versus Purple

The U.S. Naval War College, the Soviet Union, and the New Enemy in the Pacific, 1946.https://digital-commons.usnwc.edu/usnwc-historical-monographs/1023/thumbnail.jp

Simultaneous Search and Monitoring by Unmanned Aerial Vehicles

Although robot search and monitoring are two problems which are normally addressed separately, this work conceives the idea that search and monitoring are both required in realistic applications. A problem of simultaneous search and monitoring (SSM) is studied, which innovatively combines two problems in a synergistic perspective. The single pursuer SSM of randomly moving or evasive targets are studied first, and are extended to the cases with multiple pursuers. The precise mathematical frameworks for this work are POMDP, POSG and Dec-POMDP. They are all intractable and non-scalable. Different approaches are taken in each scenario, to reduce computation cost and achieve online and distributed planning, without significantly undermining the performance. For the single pursuer SSM of randomly moving targets, a novel policy reconstruction method is combined with a heuristic branching rule, to generate a heuristic reactive policy. For the single pursuer SSM of evasive targets, an assumption is made and justified, which simplifies the search evasion game to a dynamic guaranteed search problem. For the multiple-pursuer SSM of randomly moving targets, the partial open-loop feedback control method is originally applied to achieve the cooperation implicitly. For the multiple-pursuer SSM of evasive targets, the assumption made in the single pursuer case also simplifies the cooperative search evasion game to a cooperative dynamic guaranteed search problem. In moderate scenarios, the proposed methods show better performance than baseline methods, and can have practical computation efficiency. The extreme scenarios when SSM does not work are also studied

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin