Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing

Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers the problem of an agent equipped with multiple sensors, each with different sensing accuracy and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. Previous work has focused on the less general Adaptive Informative Path Planning (AIPP) problem, which considers only the effect of the agent's movement on received observations. The AIPPMS problem adds additional complexity by requiring that the agent reasons jointly about the effects of sensing and movement while balancing resource constraints with information objectives. We formulate the AIPPMS problem as a belief Markov decision process with Gaussian process beliefs and solve it using a sequential Bayesian optimization approach with online planning. Our approach consistently outperforms previous AIPPMS solutions by more than doubling the average reward received in almost every experiment while also reducing the root-mean-square error in the environment belief by 50%. We completely open-source our implementation to aid in further development and comparison

Diagnostics and Prognostocs of Electro-Mechanical Actuators

This presentation describes the research performed at NASA Ames Research Center on diagnostics and prognostics of electro-mechanical actuators

Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures

Planning to Explore: Using a Coordinated Multisource Infrastructure to Overcome Present and Future Space Flight Planning Challenges

Few human endeavors present as much of a planning and scheduling challenge as space flight, particularly manned space flight. Just on the operational side of it, efforts of thousands of people across hundreds of organizations need to be coordinated. Numerous tasks of varying complexity and nature, from scientific to construction, need to be accomplished within limited mission time frames. Resources need to be carefully managed and contingencies worked out, often on a very short notice. From the beginning of the NASA space program, planning has been done by large teams of domain experts working months, sometimes years, to put together a single mission. This approach, while proven very reliable up to now, is becoming increasingly harder to sustain. Elevated levels of NASA space activities, from deployment of the new Crew Exploration Vehicle (CEV) and completion of the International Space Station (ISS), to the planned lunar missions and permanent lunar bases, will put an even greater strain on this largely manual process. While several attempts to automate it have been made in the past, none have fully succeeded. In this paper we describe the current NASA planning methods, outline their advantages and disadvantages, discuss the planning challenges of upcoming missions and propose a distributed planning/scheduling framework (CMMD) aimed at unifying and optimizing the planning effort. CMMD will not attempt to make the process completely automated, but rather serve in a decision support capacity for human managers and planners. It will help manage information gathering, creation of partial and consolidated schedules, inter-team negotiations, contingencies investigation, and rapid re-planning when the situation demands it. The fist area of CMMD application will be planning for Extravehicular Activities (EVA) and associated logistics. Other potential applications, not only in the space flight domain, and future research efforts will be discussed as well

Dynamic Routing of Aircraft in the Presence of Adverse Weather Using a POMDP Framework

Each year weather-related airline delays result in hundreds of millions of dollars in additional fuel burn, maintenance, and lost revenue, not to mention passenger inconvenience. The current approaches for aircraft route planning in the presence of adverse weather still mainly rely on deterministic methods. In contrast, this work aims to deal with the problem using a Partially Observable Markov Decision Processes (POMDPs) framework, which allows for reasoning over uncertainty (including uncertainty in weather evolution over time) and results in solutions that are more robust to disruptions. The POMDP-based decision support system is demonstrated on several scenarios involving convective weather cells and is benchmarked against a deterministic planning system with functionality similar to those currently in use or under development

Spall Fault Quantification Method for Flight Control Electromechanical Actuator

Flight control electro-mechanical actuators (EMAs) are among the primary onboard systems that significantly influence the reliability and safety of unmanned aerial vehicles. Recent reliability studies have shown that the ball-screw element of a flight control EMA is subject to oscillating operating conditions that may initiate rapid degradation, such as fatigue spall defects. Accordingly, detecting and quantifying such faults are crucial for developing efficient fault prognostic and remaining useful life estimation capabilities. In this study, a vibration-based fault quantification method is developed to quantify the fatigue faults of a ball-screw mechanism of an EMA. The method is based on identifying the ball passing instants through a localized surface defect on the vibrational jerk rather than the vibrational acceleration measurement. The jerk is numerically determined from conventional accelerometers using a Savitzky–Golay differentiator. This method was successfully tested for ball bearings and it is adjusted in this paper for ball-screw faults. The experimental validation is investigated on a set of fault-seeded samples on NASA’s Ames Research Center Flyable Electro-Mechanical Actuator test stand

Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform

Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rovers electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults

Real-Time Monitoring and Prediction of Airspace Safety

The U.S. National Airspace System (NAS) has reached an extremely high level of safety in recent years. However, it will only become more difficult to maintain the current level of safety with the forecasted increase in operations, and so the FAA has been making revolutionary changes to the NAS to both expand capacity and ensure safety. Our work complements these efforts by developing a novel model-based framework for real-time monitoring and prediction of the safety of the NAS. Our framework is divided into two parts: (offline) safety analysis and modeling part, and a real-time (online) monitoring and prediction of safety. The goal of the safety analysis task is to identify hazards to flight (distilled from several national databases) and to codify these hazards within our framework such that we can monitor and predict them. From these we define safety metrics that can be monitored and predicted using dynamic models of airspace operations, aircraft, and weather, along with a rigorous, mathematical treatment of uncertainty. We demonstrate our overall approach and highlight the advantages of this approach over the current state-of-the-art through simulated scenarios