Dynamic Detection of Topological Information from Grid-Based Generalized Voronoi Diagrams

In the context of robotics, the grid-based Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent their surrounding area. Current approaches for incrementally constructing GVDs mainly focus on providing metric skeletons of underlying grids, while the connectivity among GVD vertices and edges remains implicit, which makes high-level spatial reasoning tasks impractical. In this paper, we present an algorithm named Dynamic Topology Detector (DTD) for extracting a GVD with topological information from a grid map. Beyond the construction and reconstruction of a GVD on grids, DTD further extracts connectivity among the GVD edges and vertices. DTD also provides efficient repair mechanism to treat with local changes, making it work well in dynamic environments. Simulation tests in representative scenarios demonstrate that (1) compared with the static algorithms, DTD generally makes an order of magnitude improvement regarding computation times when working in dynamic environments; (2) with negligible extra computation, DTD detects topologies not computed by existing incremental algorithms. We also demonstrate the usefulness of the resulting topological information for high-level path planning tasks

Incremental Construction of Generalized Voronoi Diagrams on Pointerless Quadtrees

In robotics, Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent the spatial topologies of their surrounding area. In this paper we consider the problem of constructing GVDs on discrete environments. Several algorithms that solve this problem exist in the literature, notably the Brushfire algorithm and its improved versions which possess local repair mechanism. However, when the area to be processed is very large or is of high resolution, the size of the metric matrices used by these algorithms to compute GVDs can be prohibitive. To address this issue, we propose an improvement on the current algorithms, using pointerless quadtrees in place of metric matrices to compute and maintain GVDs. Beyond the construction and reconstruction of a GVD, our algorithm further provides a method to approximate roadmaps in multiple granularities from the quadtree based GVD. Simulation tests in representative scenarios demonstrate that, compared with the current algorithms, our algorithm generally makes an order of magnitude improvement regarding memory cost when the area is larger than 210×210. We also demonstrate the usefulness of the approximated roadmaps for coarse-to-fine pathfinding tasks

Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees

In the context of robot navigation, game AI, and so on, real-time search is extensively used to undertake motion planning. Though it satisfies the requirement of quick response to users’ commands and environmental changes, learning real-time search (LRTS) suffers from the heuristic depressions where agents behave irrationally. There have introduced several effective solutions, such as state abstractions. This paper combines LRTS and encoded quad-tree abstraction which represent the search space in multiresolutions. When exploring the environments, agents are enabled to locally repair the quad-tree models and incrementally refine the spatial cognition. By virtue of the idea of state aggregation and heuristic generalization, our EQ LRTS (encoded quad-tree based LRTS) possesses the ability of quickly escaping from heuristic depressions with less state revisitations. Experiments and analysis show that (a) our encoding principle for quad-trees is a much more memory-efficient method than other data structures expressing quad-trees, (b) EQ LRTS differs a lot in several characteristics from classical PR LRTS which represent the space and refine the paths hierarchically, and (c) EQ LRTS substantially reduces the planning amount and curtails heuristic updates compared with LRTS on uniform cells

Essays on spillover effects of economic and geopolitical uncertainty : a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Auckland (Albany), New Zealand

Listed in 2020 Dean's List of Exceptional ThesesWe are living in an age of uncertainty. While uncertainty can originate from multiple sources, the most prominent ones include economic policies and geopolitical conditions. Over the past two decades, geopolitical and economic policy uncertainties have risen dramatically around the globe, raising concerns among policymakers and financial market participants about the cross-country and cross-market transmission effects of these uncertainties. Consequently, a growing body of literature has emerged around the measurement of uncertainty, the cross-country transmission of uncertainty, and the spillover effects of a given uncertainty for financial markets. By offering several advantages over other measures of uncertainty, news-based uncertainty indicators have become increasingly popular since the seminal work by Baker, Bloom, and Davis (2016). As the transmission of geopolitical uncertainty across countries and that of economic policy uncertainty to financial markets carry important implications for risk-management and policy-making decisions, it is crucial to understand and explain the behavior of these transmission mechanisms. By relying on news-based indicators of geopolitical and economic policy uncertainty, this thesis contributes to the literature by exploring the potential determinants of uncertainty transmission to stock markets as well as across countries. The first essay estimates and explains the cross-country transmission of geopolitical uncertainty (GPU). Using the news-based GPU indices for a sample of emerging economies along with the United States, the spillover models are employed to measure the pairwise and system-wide transmission of GPU. A substantial amount of GPU transmission is found across the sample countries, with some countries and geographical clusters are being more prominent than others. A cross-sectional analysis, motivated by a gravity model framework, is further utilized to explain the pairwise transmission of GPU, which reveals that bilateral linkages and country-specific factors play an essential role in driving the transmission of GPU. The overall findings continue to hold even after considering the short- and long-term time horizons. The findings of this essay may help predict the trajectory of GPU from one country to another, which is an essential input for the assessment of cross-border investment appraisals as well as international stability initiatives. A bulk of the literature has examined the impact of US uncertainty on international stock markets without paying much attention to the correlation between the US and the other stock markets. Motivated by this void in the extant literature, the second essay examines the role of US uncertainty in driving the US stock market’s spillovers to global stock markets, after controlling for the stock market correlation. To this end, I consider a wide range of stock markets around the world, as well as three news-based uncertainties from the US, namely economic policy uncertainty (EPU), equity market uncertainty, and equity market volatility. I find that the US uncertainties significantly cause the spillovers from the US to global stock markets. This causality from US uncertainties depends upon certain country-characteristics. Specifically, the US uncertainties explain better the spillovers between US and target countries, when those countries have a higher degree of financial openness, trade linkage with the US, and vulnerable fiscal position. Improved levels of stock market development in the target countries, however, mitigate their stock markets’ vulnerability to the US uncertainty shocks. The essay offers potential insights and implications for investors and policymakers. Inspired by the concerns that small open economies may well be more vulnerable to foreign uncertainty than to local uncertainty, the third essay focuses on New Zealand, which is a small open economy. This essay introduces a weekly EPU index for New Zealand and, and examines the return and volatility spillovers from NZ EPU and US EPU on the aggregate (NZSE) and sectoral indices of New Zealand stock market. Overall, the findings suggest that NZ equity sectors and NZSE receive much stronger and more pronounced spillover effects from US EPU compared to the local counterpart. While the return spillovers from both EPUs are somewhat similar yet limited to just a few sectors, the volatility spillovers from US EPU on NZ sectors outstrip those from the NZ EPU. For volatility spillovers, the domestically oriented sectors are relatively more vulnerable to NZ EPU, while those having export/import concentration with the US are mainly susceptible to US EPU. The findings of this essay may be useful to investors seeking sectoral diversification opportunities across New Zealand and the US

Behavioural strategy for indoor mobile robot navigation in dynamic environments

PhD ThesisDevelopment of behavioural strategies for indoor mobile navigation has become a challenging and practical issue in a cluttered indoor environment, such as a hospital or factory, where there are many static and moving objects, including humans and other robots, all of which trying to complete their own specific tasks; some objects may be moving in a similar direction to the robot, whereas others may be moving in the opposite direction. The key requirement for any mobile robot is to avoid colliding with any object which may prevent it from reaching its goal, or as a consequence bring harm to any individual within its workspace. This challenge is further complicated by unobserved objects suddenly appearing in the robots path, particularly when the robot crosses a corridor or an open doorway. Therefore the mobile robot must be able to anticipate such scenarios and manoeuvre quickly to avoid collisions. In this project, a hybrid control architecture has been designed to navigate within dynamic environments. The control system includes three levels namely: deliberative, intermediate and reactive, which work together to achieve short, fast and safe navigation. The deliberative level creates a short and safe path from the current position of the mobile robot to its goal using the wavefront algorithm, estimates the current location of the mobile robot, and extracts the region from which unobserved objects may appear. The intermediate level links the deliberative level and the reactive level, that includes several behaviours for implementing the global path in such a way to avoid any collision. In avoiding dynamic obstacles, the controller has to identify and extract obstacles from the sensor data, estimate their speeds, and then regular its speed and direction to minimize the collision risk and maximize the speed to the goal. The velocity obstacle approach (VO) is considered an easy and simple method for avoiding dynamic obstacles, whilst the collision cone principle is used to detect the collision situation between two circular-shaped objects. However the VO approach has two challenges when applied in indoor environments. The first challenge is extraction of collision cones of non-circular objects from sensor data, in which applying fitting circle methods generally produces large and inaccurate collision cones especially for line-shaped obstacle such as walls. The second challenge is that the mobile robot cannot sometimes move to its goal because all its velocities to the goal are located within collision cones. In this project, a method has been demonstrated to extract the colliii sion cones of circular and non-circular objects using a laser sensor, where the obstacle size and the collision time are considered to weigh the robot velocities. In addition the principle of the virtual obstacle was proposed to minimize the collision risk with unobserved moving obstacles. The simulation and experiments using the proposed control system on a Pioneer mobile robot showed that the mobile robot can successfully avoid static and dynamic obstacles. Furthermore the mobile robot was able to reach its target within an indoor environment without causing any collision or missing the target

Navigating Through Virtual Worlds: From Single Characters to Large Crowds

With the rise and success of digital games over the past few decades, path planning algorithms have become an important aspect in modern game development for all types of genres. Indirectly-controlled playable characters as well as non-player characters have to find their way through the game's environment to reach their goal destinations. Modern gaming hardware and new algorithms enable the simulation of large crowds with thousands of individual characters. Still, the task of generating feasible and believable paths in a time- and storage-efficient way is a big challenge in this emerging and exciting research field. In this chapter, the authors describe classical algorithms and data structures, as well as recent approaches that enable the simulation of new and immersive features related to path planning and crowd simulation in modern games. The authors discuss the pros and cons of such algorithms, give an overview of current research questions and show why graph-based methods will soon be replaced by novel approaches that work on a surface-based representation of the environment

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Collision avoidance and dynamic modeling for wheeled mobile robots and industrial manipulators

Collision Avoidance and Dynamic Modeling are key topics for researchers dealing with mobile and industrial robotics. A wide variety of algorithms, approaches and methodologies have been exploited, designed or adapted to tackle the problems of finding safe trajectories for mobile robots and industrial manipulators, and of calculating reliable dynamics models able to capture expected and possible also unexpected behaviors of robots. The knowledge of these two aspects and their potential is important to ensure the efficient and correct functioning of Industry 4.0 plants such as automated warehouses, autonomous surveillance systems and assembly lines. Collision avoidance is a crucial aspect to improve automation and safety, and to solve the problem of planning collision-free trajectories in systems composed of multiple autonomous agents such as unmanned mobile robots and manipulators with several degrees of freedom. A rigorous and accurate model explaining the dynamics of robots, is necessary to tackle tasks such as simulation, torque estimation, reduction of mechanical vibrations and design of control law