There are many organizations that use satellites and drones to collect information, such as ground photographs or atmospheric pressure measurements. Often, these separate organizations have overlapping collection interests, yet they are controlled by separate planning systems with asynchronous scheduling cycles. This paper develops a method for coordinating various collection tasks between the planning systems to increase the overall utility of the collected data. The method focuses on allocation of collection requests to scheduling systems rather than complete centralized planning over the entire system so that the current planning infrastructure can be maintained. Previous work in this area is expanded upon by inclusion of an online learning method to capture information about the uncertainty pertaining to the scheduling and completion of collection tasks, which is subsequently used in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end through a few different theoretical scenarios. These results provide evidence that the newly developed methods can increase the total value of serviced requests compared to current operations, with some theoretical scenarios producing more than double the value using the new methods over the current styles of planning.

The United States (US) Army has over 66,000 soldiers engaged in contingency operations across the world. Current budgetary constraints and an uncertain global security environment require these operations to be executed as efficiently as possible. Base camps are the secured areas where soldiers live when deployed to contingency operations. Base camps impose a significant financial and tactical burden during contingency operations and sub-optimal soldier quality of life decisions have significantly contributed to costs. Quality of life (QOL) refers to the non-security and non-mission related services that directly sustain the mission effectiveness of soldiers. Current US Army base camp tactics, techniques, and procedures (TTPs) do not sufficiently specify QOL services, and more detailed doctrine should be developed to support combat units executing contingency operations. In this investigation we employ quantitative methods to select decisions that improve QOL and inform doctrine. We leverage a QOL function and resource consumption data developed by US Army Natick Soldier Research, Development and Engineering Center's (Natick Labs) to build a model that improves QOL under the constraints of four fundamental resources: fuel, water, waste water, and solid waste. We employ a mixed integer linear program modeling approach and execute sensitivity analysis to evaluate the strength of our results. Our final model is formulated as a chance constraint optimization to address the uncertainty associated with resource availability in contingency operations. Our results provide QOL decisions that reduce resource consumption while maintaining an equivalent QOL level when compared to current TTPs. The model provides quantitative rigor, informing decision makers of specific base camp design principles for the development of doctrine.

The ability to learn network structure characteristics and disease dynamic parameters improves the predictive power of epidemic models, the understanding of disease propagation processes and the development of efficient curing and vaccination policies. This paper presents a parameter estimation method that learns network characteristics and disease dynamics from our estimated infection curve. We apply the method to data collected during the 2009 H1N1 epidemic and show that the best-fit model, among a family of graphs, admits a scale-free network. This finding implies that random vaccination alone will not efficiently halt the spread of influenza, and instead vaccination and contact-reduction programs should exploit the special network structure.

Draper’s Earth Phenomena Observation System (EPOS) has developed mission management solutions for sensor webs over the past decade. We are extending the EPOS capability to include coordination of asynchronous sensor webs, with a particular focus on enhancing both the quantity and value of the data available to scientists in developing science models related to hurricanes. We describe our efforts supporting the Hurricane Severe Storm Sentinel (HS3) mission. We discuss our approach to improving on key collection metrics of interest to hurricane scientists, e.g., persistent surveillance of measurements in the hurricane box, through greater use of available sensor web assets and optimization of coordination planning decisions, e.g., take-off time of HS3 mission aircraft.

We are developing a simulation testbed, the Earth Phenomena Observing System (EPOS), which includes an automated mission manager. The automated mission manager will reduce requirements on the human operators of satellites, improve system resource utilization, and provide the capability to dynamically respond to temporal terrestrial phenomena. Examples of triggering events are localized transient phenomena that have a significant impact on human life such as volcanic eruptions, weather (hurricanes, tornadoes, etc.), biomass burning (e.g., forest fires). We developed an optimization-based approach to planning observations using the TES and HIRDLS instruments on Aura to achieve the greatest science value of the observation data, making use of near-real-time information about cloud cover.

In the current age of rapid climate change, analysis of coincident or near-coincident Earth Science data taken from different observing platforms will provide critical knowledge both to scientists and to policy makers. At Draper Laboratory, we are pursuing efforts aimed at overcoming challenges to this type of research. In this paper we present a developed web portal framework that would allow investigators to easily and quickly visualize available Earth Science and related socio-economic data as a function of time and space. Thus, scientists and policy makers wishing to fuse coordinated data into a single study could make use of all available data within a domain of interest without knowing a priori which observations were taken. We also overview in this work research aimed at fusing large numbers of heterogeneous signals into data driven models capable of describing effects of climate change on natural and anthropogenic systems. The first model we developed predicts the short-term spread of wildfires up to two days in advance and can be used to aid in the allocation of disaster recovery resources.

The recent development of more advanced sensor and communication systems has improved researchers’ abilities to monitor the Earth’s climate. However, current Earth observation operations are hindered by the existence of “stovepipe” systems in which inefficiencies are inherent. In this paper, we will apply a recently developed planning algorithm and run test cases to demonstrate the value of coordinating the observations/measurements of Earth-based targets across multiple collection systems. Specifically, we will compare coordinated and uncoordinated cases in a notional representation that uses a previously proposed version of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission.

This paper considers the nonlethal targeting assignment problem in the counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We propose two models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counterinsurgents, and 2) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies a strategy for assigning k US agents to produce the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in an experiment the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.

We investigate methods for creating theater-level plans that are robust to uncertainty, and apply our planning techniques to a notional problem of assigning tasks to Remotely Piloted Aircraft (RPA) squadrons. We present a framework that connects desired outcomes to tasks, enabling planners to value task assignments based on ability to accomplish desired objectives. We introduce an integer programming formulation that implements this framework. We apply Chance Constrained Programming (Charnes and Cooper 1959) and robust methods introduced by Bertsimas and Sim (2004) to this framework and analyze their performance in creating robust plans for theater-level task assignments. We demonstrate how robust planning creates plans that remain feasible in execution longer than nonrobust plans and achieve better overall value by avoiding replanning costs. We analyze strengths and weaknesses of each model.

The incorporation of unmanned aerial vehicles (UAVs) into an increasing variety of applications has resulted in a need for operations planning methods that include the complex constraints of specific applications. The research presented in this paper concentrates on the planning of operations for UAVs for the purpose of monitoring Earth’s phenomena through data collection. The problem presented in this research focuses on developing an operations plan for multiple, heterogeneous UAVs to collect data from spatially distributed locations. The planning of UAV operations for this purpose requires complex constraints to accurately represent the operation. These constraints include: planning in three dimensions to take full advantage of the UAVs’ abilities, ensuring data is collected during the time period in which the phenomena occurs, and fully utilizing each UAV’s performance capabilities.

In this paper, we examine how to improve the frequency and accuracy with which highquality Earth observations are made by coordinating across multiple collection systems, including air and space assets, in an asynchronous environment. In particular, we consider how these improvements could impact Earth observing sensors in two use areas; climate studies and intelligence collection operations. To do this, we make simplifying but reasonable assumptions and use a complex yet intuitive value function to solve a series of simple optimization problems that allocate requests to single-mission planners, or “sub-planners.” We consider requests with time windows and priority levels, some of which require simultaneous observations by different sensors. The primary contributions of this paper include our approach to the asynchronous and distributed nature of the problem and the development of a value function to facilitate the coordination of the observations with multiple surveillance assets.

In the current age of rapid climate change, analysis of coincident Earth Science data taken from different observing platforms will provide critical knowledge both to scientists and to policy makers. We are pursuing efforts aimed at overcoming challenges in several different phases of this process. 1) We have developed an architecture for optimized, coordinated dynamic tasking of asynchronously planned and distributed sensor systems, on both satellites and aircraft. Implementation of this architecture allows for an increased likelihood of coincident observation between heterogeneous instruments. 2) We have developed the framework for a web portal to help scientists and decision makers more easily discover which Earth systems are observed as a function of time and space. The web portal would allow users to visually determine coincident data availability within a specified space-time query, thus enabling an investigator to efficiently identify and analyze all available observations without knowing a priori which assets were monitoring his/her domain of interest. 3) We are using data mining techniques to develop models that use coincident Earth observations combined with a range of expected climate change parameters to determine statistically likely event scenarios for extreme disturbance events (e.g., fires, landslides, spread of infectious disease) in the nearand long-term future. I. PLANNING FOR COINCIDENT EARTH OBSERVATIONS Draper Laboratory’s Earth Phenomena Observation System (EPOS) was primarily developed in a number of NASA ESTO AIST projects (under AIST-99, AIST-02, and AIST-05). The fundamental EPOS concept of operation is that of optimized dynamic replanning and execution. Sensor data and model forecasts are inputs to a closed-loop decision-making system. In collaboration with users, EPOS monitors the input, and when appropriate, replans and executes a new plan that optimizes the tasking of available sensing assets to gather data. The functional architecture for EPOS is illustrated in Figure 1. Included in this architecture are three primary components. Situation Awareness: Situation Awareness provides estimates of current world and system states. Situation Assessment: Situation Assessment takes Situation Awareness output and uses Information Exploitation technologies, e.g., pattern recognition and data mining, to support monitoring, diagnosis and prediction of world and system states.

With recent advances in unmanned aerial vehicle (UAV) technology, UAVs are finding uses beyond their traditional “patrol drone” role. UAVs are increasingly valued as unmanned sensor platforms capable of performing imaging in a variety of domains. However, flight planning for UAVs is still an emerging discipline, and in practice is often performed by hand. We describe an automated planner and graphical user interface designed to assist an operator in mission planning for a single UAV imaging ground targets. Over time, the planner has evolved from a purely automated flight planner into a humansystem collaborative planning environment. The resulting software package is flexible and powerful, allowing many different degrees of human-system collaboration. The planner has been used to support NASA’s Western States Fire Missions and may also prove to be useful in many other domains.

1 A Single Orbital Revolution Planner for NASA’s EO-1 Spacecraft Mark Abramson , David Carter, Alex Kahn, Stephan Kolitz, Jason Riek, and Peter Scheidler Draper Laboratory, 555 Technology Square, Cambridge, MA, 02139 A new operational concept is being developed for NASA’s EO-1 spacecraft that will allow the public to request scenes to be imaged. When public imaging requests are accepted, the number of scene requests per revolution is expected to grow significantly. This paper describes a planner developed at Draper Laboratory that can be used by EO-1 operations staff to determine which image requests to accept on any given orbital revolution. Nomenclature € x = spacecraft state € t(x) = spacecraft state, associated UTC time Δt(x) = spacecraft state, imaging time remaining € φ (x) = spacecraft state, roll angle € φ = spacecraft roll rate € ts = settling time after a roll maneuver

Many large data collection enterprises, (e.g., the collection of Earth observation data) are organized as single-mission systems of separately managed collection systems, i.e., stovepipes. This organizational approach inhibits communication and coordination among collection systems. The approach is so pervasive that essentially it is a pattern of behavior that new collection systems will be organized as a stovepipe. This pattern limits the collection potential of the overall enterprise – and so we view this pattern as a negative way of doing things or an anti-pattern. This paper discusses our solution to this anti-pattern: the coordination manager, which provides a framework for optimization-based rolling horizon dynamic planning and scheduling, an approach that addresses the issue of coordination of stovepipe mission planning systems. It is the first step toward an objective multiple mission system designed from the beginning to participate with other single-mission systems.

: With recent advances in research and technology, autonomous surface vessel capabilities have steadily increased. These autonomous surface vessel technologies enable missions and tasks to be performed without the direction of human operators, and have changed the way scientists and engineers approach problems. Because these robotic devices can work without manned guidance, they can execute missions that are too difficult, dangerous, expensive, or tedious for human operators to attempt. The United States government is currently expanding the use of autonomous surface vessel technologies through the United States Navy's Spartan Scout unmanned surface vessel (USV) and NASA,s Ocean-Atmosphere Sensor Integration System (OASIS) USV. These USVs are well-suited to complete monotonous, dangerous, and time-consuming missions. The USVs provide better performance, lower cost, and reduced risk to human life than manned systems. In this thesis, we explore how to plan multiple USV observation schedules for two significant notional observation scenarios, collecting water temperatures ahead of the path of a hurricane, and collecting fluorometer readings to observe and track a harmful algal bloom. A control system must be in place that coordinates a fleet of USVs to targets in an efficient manner. We develop three algorithms to solve the unmanned surface vehicle observation-planning problem. A greedy construction heuristic runs fastest, but produces suboptimal plans; a 3-phase algorithm which combines a greedy construction heuristic with an improvement phase and an insertion phase, requires more execution time, but generates significantly better plans; an optimal mixed integer programming algorithm produces optimal plans, but can only solve small problem instances.