Agent-based Sensor-Mission Assignment for Tasks Sharing Assets

(c) IFAAMASPeer reviewedPostprin

United States Air Force Applications of Unmanned Aerial Systems (UAS): A Delphi Study to Examine Current and Future UAS Autonomous Mission Capabilities

As UAS technology continues to grow and enable increased autonomous capabilities, acquisition and operational decision makers must determine paths to pursue for existing and emerging mission areas. The DoD has published a number of 25-year unmanned systems integration roadmaps (USIR) to describe future capabilities and challenges. However, these roadmaps have lacked distinguishable stakeholder perspectives. Following the USIRs concept, this research focused on UAS autonomy through the lens of UAS subject matter experts (SMEs). We used the Delphi method with SMEs from USAF communities performing day-to-day operations, acquisitions, and research in UAS domains to forecast mission capabilities over the next 20 years; specifically, within the context of increased UAS autonomous capabilities. Through two rounds of questions, the study provided insight to the capabilities SMEs viewed as most important and likely to be incorporated as well as how different stakeholders view the many challenges and opportunities autonomy present for future missions

Ontological Problem-Solving Framework for Assigning Sensor Systems and Algorithms to High-Level Missions

The lack of knowledge models to represent sensor systems, algorithms, and missions makes opportunistically discovering a synthesis of systems and algorithms that can satisfy high-level mission specifications impractical. A novel ontological problem-solving framework has been designed that leverages knowledge models describing sensors, algorithms, and high-level missions to facilitate automated inference of assigning systems to subtasks that may satisfy a given mission specification. To demonstrate the efficacy of the ontological problem-solving architecture, a family of persistence surveillance sensor systems and algorithms has been instantiated in a prototype environment to demonstrate the assignment of systems to subtasks of high-level missions

User benefits and funding strategies

The justification, economic and technological benefits of NASA Space Programs (aside from pure scientific objectives), in improving the quality of life in the United States is discussed and outlined. Specifically, a three-step, systematic method is described for selecting relevant and highly beneficial payloads and instruments for the Interim Upper Stage (IUS) that will be used with the space shuttle until the space tug becomes available. Viable Government and private industry cost-sharing strategies which would maximize the number of IUS payloads, and the benefits obtainable under a limited NASA budget were also determined. Charts are shown which list the payload instruments, and their relevance in contributing to such areas as earth resources management, agriculture, weather forecasting, and many others

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously

Intelligence Surveillance and Reconnaissance Asset Assignment for Optimal Mission Effectiveness

This research develops mathematical programming techniques to solve an intelligence, surveillance, and reconnaissance sensor assignment problem for USSTRATCOM. The problem as specified is hypothesized to be difficult (i.e. np-hard). With the smallest test cases, the true optimal solution is found using simple optimization techniques, but, due to intractability, the optimal solutions for larger test cases are not found using these same techniques. Instead, heuristic techniques are applied to several test cases in order to determine the best, robust methodologies to find true or near optimal solutions. Specifically, simulated annealing (SA) is tested for convergence properties across several different parameter settings. This research also utilizes local search techniques with simple exchange neighborhoods of various sizes. Mission prioritization is also examined via a weighted sum scalarization technique