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Costs of Limiting Route Optimization to Published Waypoints in the Traffic Aware Planner

The Traffic Aware Planner (TAP) is an airborne advisory tool that generates optimized, traffic-avoiding routes to support the aircraft crew in making strategic reroute requests to Air Traffic Control (ATC). TAP is derived from a research-prototype self-separation tool, the Autonomous Operations Planner (AOP), in which optimized route modifications that avoid conflicts with traffic and weather, using waypoints at explicit latitudes and longitudes (a technique supported by self-separation concepts), are generated by maneuver patterns applied to the existing route. For use in current-day operations in which trajectory changes must be requested from ATC via voice communication, TAP produces optimized routes described by advisories that use only published waypoints prior to a reconnection waypoint on the existing route. We describe how the relevant algorithms of AOP have been modified to implement this requirement. The modifications include techniques for finding appropriate published waypoints in a maneuver pattern and a method for combining the genetic algorithm of AOP with an exhaustive search of certain types of advisory. We demonstrate methods to investigate the increased computation required by these techniques and to estimate other costs (measured in terms such as time to destination and fuel burned) that may be incurred when only published waypoints are used.

TASAR

An Experimental Study of the Effect of Transparency on Pilot Trust in the Emergency Landing Planner

This experimental study examined the effects of transparency (operationalized as increasing levels of explanation) on pilot trust of an automated emergency landing planner. A low-fidelity study was conducted where commercial pilots (N12) interacted with simulated recommendations from NASA's Emergency Landing Planner (ELP). These recommendations varied in their associated levels of transparency. Results indicated that trust in the ELP was influenced by the level of transparency within the human-machine interface of the ELP.

transparency

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

NASA’s Traffic Aware Planner (TAP) software is a research-prototype decision support tool that provides pilots with time- and fuel-saving route recommendations that optimize their current trajectory. The software runs on a first-of-a-kind system architecture onboard three aircraft in revenue service conducting operational evaluations with a major domestic airline. Therefore, significant NASA-internal testing is required prior to releasing the software to the partner airline. This paper describes a flight test plan that exercises the functionality of the TAP software in a representative operational environment, describes the system architecture developed and implemented for the NASA Langley HU-25A Guardian aircraft to support the test objectives, presents outcomes of the flight test campaign, and discusses use cases that demonstrate the value of flight testing for this activity.Research into flight path optimization of transport aircraft conducted by the National Aeronautics and SpaceAdministration (NASA) has produced an operational concept known as Traffic Aware Strategic Aircrew Requests(TASAR) [1, 2]. This near-term concept [3] provides the aircrew with a flight deck decision support tool known asthe Traffic Aware Planner (TAP). The TAP software leverages a growing number of information sources on the flightdeck to make time- and fuel-saving route optimization recommendations to the aircrew while en route. The aircrewcan then use the suggestions provided by the tool to make route change requests with a greater likelihood of acceptanceby air traffic control (ATC). Since TASAR is a concept intended for the current operational environment, it isintentionally designed to have no safety-critical impact or require any changes to current Federal AviationAdministration (FAA) rules and procedures [4, 5].The research prototype TAP system [6–8], explained further in Section III.C, continually incorporates up-to-dateaircraft state data from onboard avionics, as well as the latest position of surrounding traffic, the most recent windforecast, and the most recent convective weather forecast, in order to calculate candidate trajectory modifications thatimprove upon the current active route. These trajectories account for user-selectable objective functions [3] of reducedfuel burn, reduced flight time, or an airline-derived combination of factors known as trip cost. Previous analyses andsimulations have estimated substantial savings for airlines employing this technique within the U.S. National AirspaceSystem (NAS) [9–11]. Operational evaluations with Alaska Airlines seek to validate these projected benefits usingmeasured data while simultaneously providing benefits to the airline [12, 13].The TAP software has undergone a number of human-in-the-loop simulations [14] and flight test activities[15–17] in order to validate the operational concept, evaluate human factors considerations (e.g., workload, usability,distraction, etc.), and to assess the ability of the software to function in a representative operational environment (e.g.,connected to live avionics data, using in-flight internet connectivity, etc.). However, these simulations and flight testcampaigns did not account for the hardware architecture implemented on the three aircraft for Alaska Airlines’operational evaluations of the TAP software. Therefore, a need was identified to thoroughly test the functionality ofthe software in a similar hardware architecture to that of the partner airline’s aircraft. Information regarding testapparatus and environments used to evaluate TAP prior to testing on the HU-25A can be found in reference [18].A campaign of flight trials on a NASA aircraft, the HU-25A Guardian, was conducted to ensure that the researchprototype TAP system functions well in a configuration similar to the Alaska Airlines aircraft prior to deployment.This airborne, networked environment enables an assessment of the operational factors unique to the flight environment. Additionally, this activity evaluated the effectiveness and benefit of new TAP functionality andoperation in a relevant flight environment while allowing the rapid prototyping of new concepts and features.This paper is organized as follows: Section II discusses the details of the flight test plan, flight profiles, and theduties of personnel involved with conducting flight operations. Section III describes the test platform, avionicsequipage, and system architecture. Section IV presents a discussion of results, and Section V contains concludingremarks.

Underwood, Matthew C.

V & V of A Spacecrafts Autonomous Planner Through Extended Automation

We have introduced and used significant automation during the verification and validation (V & V) of a spacecraft's autonomous planner. This paper describes the problem we faced, the solution we employed, and the applicability of our approach in a general V & V setting.

planning

Applications Explorer Missions (AEM): Mission planners handbook

The Applications Explorer Missions (AEM) Program is a planned series of space applications missions whose purpose is to perform various tasks that require a low cost, quick reaction, small spacecraft in a dedicated orbit. The Heat Capacity Mapping Mission (HCMM) is the first mission of this series. The spacecraft described in this document was conceived to support a variety of applications instruments and the HCMM instrument in particular. The maximum use of commonality has been achieved. That is, all of the subsystems employed are taken directly or modified from other programs such as IUE, IMP, RAE, and Nimbus. The result is a small versatile spacecraft. The purpose of this document, the AEM Mission Planners Handbook (AEM/MPH) is to describe the spacecraft and its capabilities in general and the HCMM in particular. This document will also serve as a guide for potential users as to the capabilities of the AEM spacecraft and its achievable orbits. It should enable each potential user to determine the suitability of the AEM concept to his mission.

Smith, S. R.

A strategy planner for NASA robotics applications

Automatic strategy or task planning is an important element of robotics systems. A strategy planner under development at Goddard Space Flight Center automatically produces robot plans for assembly, disassembly, or repair of NASA spacecraft from computer aided design descriptions of the individual parts of the spacecraft.

Brodd, S. S.

Toward the fully capable AI space mission planner

The necessity of expending numerous ground-side manhours to write extremely concise command codes for deep-space probes in-flight, which carry limited on-board processing capabilities, is encouraging the development of AI modules to perform the same task. An experimental automated sequence planner, Deviser, has proven successful enough, when used on a mainframe computer, to continue with the next generation of the concept, PLAN-IT, an expert scheduler for spacecraft observation targets. The new system will provide the ground-based user with interactive graphic displays for choosing targets and generates an expected time-line. An updated Deviser, using the new modules, will be tested after the Voyager Uranus encounter, and PLAN-IT will be employed to formulate Spacelab activity schedules.

Grenander, S.

Robotic planner expert system (RPLANES)

The Artificial Intelligence Section of the Mission Planning and Analysis of the Johnson Space Center has developed a prototype of an expert system for robotic planning. A robot is given a high level goal to perform an action (i.e., swap, adjust, or stow) on a component unit of an object such as a satellite and the Robotic Planner Expert System (RPLANES) generates the necessary goals for arm actions. RPLANES is designed using the Inference Corp. Automated Reasoning Tool (ART) development tool. It resides on a SYMBOLICS 3670. RPLANES and its evolution are described.

Grice, Ervin Oneal

Coordinating the activities of a planner and an execution agent

A research program was defined that will explore the link between planning and execution systems. A simple scenario was defined in which a very capable off-line planning system interacts with the user and a smaller, less capable, on-line real-time system executing plans and reacting to faults. However, the on-line execution system may have a more flexible representation of the plans it is executing. This imbalance in the capabilities of the two agents involved should clarify some of the research objectives and give an experimental framework for the work. The task is to investigate the knowledge representations and communication protocols needed to link a user stating some requirements for a task to be carried out through a planning system to the (remote) execution agent that can carry out the user's wishes. The notion that a single representation can encapsulate the expression of the user's requirements, the capabilities for action, the communication to the execution agent, the successful or faulty response from the execution agent and the means of keeping the user informed, is examined. Methods of creating plan patches to update the plans separately held by each of the parties involved to keep them in step as they each react to changing circumstances in real-time is investigated. This involves the specification of plan patch attachment points that can be understood by the recipient. Transaction based methods are also investigated for coordinating the activities of the planner with those of the execution agent and user. The trial application area for the research is in the command and control of an advanced Earth Observation Space Platform.

Tate, Austin

An endorsement-based approach to student modeling for planner-controlled intelligent tutoring systems

An approach is described to student modeling for intelligent tutoring systems based on an explicit representation of the tutor's beliefs about the student and the arguments for and against those beliefs (called endorsements). A lexicographic comparison of arguments, sorted according to evidence reliability, provides a principled means of determining those beliefs that are considered true, false, or uncertain. Each of these beliefs is ultimately justified by underlying assessment data. The endorsement-based approach to student modeling is particularly appropriate for tutors controlled by instructional planners. These tutors place greater demands on a student model than opportunistic tutors. Numerical calculi approaches are less well-suited because it is difficult to correctly assign numbers for evidence reliability and rule plausibility. It may also be difficult to interpret final results and provide suitable combining functions. When numeric measures of uncertainty are used, arbitrary numeric thresholds are often required for planning decisions. Such an approach is inappropriate when robust context-sensitive planning decisions must be made. A TMS-based implementation of the endorsement-based approach to student modeling is presented, this approach is compared to alternatives, and a project history is provided describing the evolution of this approach.

Murray, William R.

Operations mission planner beyond the baseline

The scheduling of Space Station Freedom must satisfy four major requirements. It must ensure efficient housekeeping operations, maximize the collection of science, respond to changes in tasking and available resources, and accommodate the above changes in a manner that minimizes disruption of the ongoing operations of the station. While meeting these requirements the scheduler must cope with the complexity, scope, and flexibility of SSF operations. This requires the scheduler to deal with an astronomical number of possible schedules. The Operations Mission Planner (OMP) is centered around minimally disruptive replanning and the use of heuristics limit search in scheduling. OMP has already shown several artificial intelligence based scheduling techniques such as Interleaved Iterative Refinement and Bottleneck Identification using Process Chronologies.

Biefeld, Eric

The Dynamic Planner: The Sequencer, Scheduler, and Runway Allocator for Air Traffic Control Automation

The Dynamic Planner (DP) has been designed, implemented, and integrated into the Center-TRACON Automation System (CTAS) to assist Traffic Management Coordinators (TMCs), in real time, with the task of planning and scheduling arrival traffic approximately 35 to 200 nautical miles from the destination airport. The TMC may input to the DP a series of current and future scheduling constraints that reflect the operation and environmental conditions of the airspace. Under these constraints, the DP uses flight plans, track updates, and Estimated Time of Arrival (ETA) predictions to calculate optimal runway assignments and arrival schedules that help ensure an orderly, efficient, and conflict-free flow of traffic into the terminal area. These runway assignments and schedules can be shown directly to controllers or they can be used by other CTAS tools to generate advisories to the controllers. Additionally, the TMC and controllers may override the decisions made by the DP for tactical considerations. The DP will adapt to computations to accommodate these manual inputs.

Wong, Gregory L.

Model Checking the Remote Agent Planner

This work tackles the problem of using Model Checking for the purpose of verifying the HSTS (Scheduling Testbed System) planning system. HSTS is the planner and scheduler of the remote agent autonomous control system deployed in Deep Space One (DS1). Model Checking allows for the verification of domain models as well as planning entries. We have chosen the real-time model checker UPPAAL for this work. We start by motivating our work in the introduction. Then we give a brief description of HSTS and UPPAAL. After that, we give a sketch for the mapping of HSTS models into UPPAAL and we present samples of plan model properties one may want to verify.

Khatib, Lina

Leap Before You Look: Information Gathering In the PUCCINI Planner

Most of the work in planning with incomplete information takes a "look before you leap" perspective: Actions must be guaranteed to have their intended effects before they can be executed. We argue that this approach is impossible to follow in many real-world domains. The agent may not have enough information to ensure that an action will have a given effect in advance of executing it. This paper describes PUCCINI, a partial order planner used to control the Internet Softbot (Etzioni & Weld 1994). PUCCINI takes a different approach to coping with incomplete information: "Leap before you look!" PUCCINI doesn't require actions to be known to have the desired effects before execution. However, it still maintains soundness, by requiring the effects to be verified eventually. We discuss how this is achieved using a simple generalization of causal links.

Golden, Keith