Verification of Costless Merge Pairing Heaps

Most algorithms’ performance is limited by the data structures they use. Internal algorithms then decide the performance of the data structure. This cycle continues until fundamental results, verified by analysis and experiment, prevent further improvement. In this paper I examine one specific example of this. The focus of this work is primarily on a new variant of the pairing heap. I will review the new implementation, compare its theoretical performance, and discuss my original contribution: the first preliminary data on its experimental performance. It is instructive to provide some background information, followed by a formal definition of heaps in 1.1. I also provide a brief overview of existing literature on the design of these data structures in 1.2 and discuss the methods for evaluating these types of structures in 1.3. Full details about the implementation of a pairing heap can be found in 2.2. Ongoing research has produced a variety of different types of heaps, which will be briefly discussed

Cycler Orbits and the Solar System Pony Express

In this work, we explore the concept of a secondary “data mule” consisting of a small satellite used to ferry data from a Mars mission to Earth for downlink. The concept exploits the fact that two nearby optical communicators can achieve extremely high data rates, and that a class of trajectories called “cyclers” can carry a satellite between Mars and Earth regularly. By exploiting cycler orbits, the courier needs minimal onboard propulsion. However, cycler orbits have long periodicity, as it can take years for the satellite, Mars, and Earth to repeat their relative geometry. Therefore, we propose the use of a network of such cycler “couriers” on phase-shifted trajectories to achieve a regular cadence of downlink trips. We design a series of search and optimization steps that can output a set of trajectories that at first approximation have low onboard propulsion requirements and can be used for any regular logistics network to and from Mars, then derive the link budget for proximity optical communications to show that this network can ferry large amounts of data

A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews

Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot's abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview--the robot's internal representation of its belief about both its own state, and other robots' states. A key problem for operators is that robots' worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots' scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots' scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot's worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots' schedules and (ii) detect and conduct a root cause analysis of the robots' desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- and easier-to-use than the users' current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots' schedules and detect and pinpoint the cause of the desynchronized worldviews.Comment: To appear in IEEE Conference on Visual Analytics Science and Technology (VAST) 202

Editorial: Robotic In-Situ Servicing, Assembly and Manufacturing

This research topic is dedicated to articles focused on robotic manufacturing, assembly, and servicing utilizing in-situ resources, especially for space robotic applications. The purpose was to gather resource material for researchers from a variety of disciplines to identify common themes, formulate problems, and share promising technologies for autonomous large-scale construction, servicing, and assembly robots. The articles under this special topic provide a snapshot of several key technologies under development to support on-orbit robotic servicing, assembly, and manufacturing

Nebulae: A Proposed Concept of Operation for Deep Space Computing Clouds

In this paper, we describe an ongoing multi-institution study in using emplaced computational resources such as high-volume storage and fast processing to enable instruments to gather and store much more data than would normally be possible, even if it cannot be downlinked to Earth in any reasonable time. The primary focus of the study is designing science pipelines for on-site summarization, archival for future downlink, and multisensor fusion. A secondary focus is on providing support for increasingly autonomous systems, including mapping, planning, and multi-platform collaboration. Key to both of these concepts is treating the spacecraft not as an autonomous agent but as an interactive batch processor, which allows us to avoid “quantum leaps” in machine intelligence required to realize the concepts. Our goal is to discuss preliminary results and technical directions for the community, and identify promising new opportunities for multi-sensor fusion with the help of planetary researchers

Observing Mars from Areostationary Orbit: Benefits and Applications

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Mosaic: A Satellite Constellation to Enable Groundbreaking Mars Climate System Science and Prepare for Human Exploration

The Martian climate system has been revealed to rival the complexity of Earth\u27s. Over the last 20 yr, a fragmented and incomplete picture has emerged of its structure and variability; we remain largely ignorant of many of the physical processes driving matter and energy flow between and within Mars\u27 diverse climate domains. Mars Orbiters for Surface, Atmosphere, and Ionosphere Connections (MOSAIC) is a constellation of ten platforms focused on understanding these climate connections, with orbits and instruments tailored to observe the Martian climate system from three complementary perspectives. First, low-circular near-polar Sun-synchronous orbits (a large mothership and three smallsats spaced in local time) enable vertical profiling of wind, aerosols, water, and temperature, as well as mapping of surface and subsurface ice. Second, elliptical orbits sampling all of Mars\u27 plasma regions enable multipoint measurements necessary to understand mass/energy transport and ion-driven escape, also enabling, with the polar orbiters, dense radio occultation coverage. Last, longitudinally spaced areostationary orbits enable synoptic views of the lower atmosphere necessary to understand global and mesoscale dynamics, global views of the hydrogen and oxygen exospheres, and upstream measurements of space weather conditions. MOSAIC will characterize climate system variability diurnally and seasonally, on meso-, regional, and global scales, targeting the shallow subsurface all the way out to the solar wind, making many first-of-their-kind measurements. Importantly, these measurements will also prepare for human exploration and habitation of Mars by providing water resource prospecting, operational forecasting of dust and radiation hazards, and ionospheric communication/positioning disruptions

Empirical Analysis of Costless Merge Pairing Heaps

Pairing heaps are a family of data structures that have found wide use in networking and other applications. They are popular because of their ease of implementation, but a complete theoretical analysis is still an open question. Introduced in 2009, the Costless Merge Pairing Heap boasted a potential for increased performance over the original Pairing Heap. To validate the claim of increased performance, the new Costless Merge variant was tested against the original Pairing Heap (both two-pass and multipass variants). Tests included heap-sorting of large data sets and the Hold Model, which is used to simulate a fixed-size queue of discrete events

Pursuit and Evasion with Uncertain Bearing Measurements

We study pursuit-evasion games in which a pursuer tries to capture an evader by moving on to the evader’s current location. We investigate how sensing capability of the pursuer affects the game outcome. In particular, we consider a pursuer which can sense only the bearing to an evader. Furthermore, there is noise in the measurements in such a way that an adversary may adjust each bearing measured by an angle up to ? away from the true value. In this work, we study two classical pursuit evasion games under bearing uncertainty. In the first game, played on the open plane, the pursuer tries to maintain the distance to an evader with equal speed. If the pursuer has full knowledge of the evaders location the pursuer can maintain the separation between the players by moving toward the evader. However, when an adversarial sensing model is introduced, we show that for any pursuer strategy, the evader can increase the distance to the pursuer indefinitely. The rate at which the distance increases is linear in time. ?. In the second game, both players are inside a bounded circular area. This version is known as the Lion-and-Man game, and has been well studied when no sensing limitations are imposed. In particular, the pursuer (Lion) is known to have an O(rlogr) strategy to capture the evader, where r is the radius of the circle. In contrast, when sensing uncertainty is introduced, we show that for any ? > 0, there exist environments in which the man can evade capture indefinitely