Research Project

Artificial Intelligence and Machine Learning-Based Autonomous Mission Planning for Intelligence, Surveillance, and Reconnaissance (ISR) Missions

Abstract

Implementation of this project advances fully autonomous Unmanned Aerial Vehicle operations by providing a scalable mission development environment that generates downloadable mission and Concept of Operations packages. The envisioned system includes a development environment, a mission simulation environment, a simulation testbed, and an onboard embedded system. This development environment with hardware simulation [software emulators on highly integrated System on Chip packages] enables real-time mission rehearsals. In addition, Dynamic Artificial Intelligence software optimizes mission profiles by integrating contingency resolutions into specific missions. This project is a collaborative work between Xtensor Systems and CSUN.

Motivation/Research Problem

Our research problems are three-fold during the Phase I work: (1) Develop an implementable multi-layer framework for fully autonomous missions. Mission commanders and operators will use the top layer to oversee mission strategy, planning, and operations. Designers and programmers will use the lower layer to develop autonomous executable code; (2) Conduct research on trustable and effective mission generation algorithms under the framework we establish, explore schemes that approximate dynamic programming, fuzzy sets, etc., and develop simple and effective algorithms; and (3) Build a software development environment that enables rapid mission development and delivery, and tests out our proposed framework.

Research Team

Lead Researchers 

  • Xiaojun (Ashley) Geng

Collaborators

  • Sembiam Rengarajan, College of Engineering and Computer Science

Student Team

  • Sarah Cazessus
  • Noah.Preheim

Funding

  • Funding Organization:
  • Funding Program:
Alignment, Engagement and Contributions to the priorities of NASA’s Mission Directorates

This project is aligned with the Navy plans — having all UAVs equipped with the necessary sensors and flight control systems embedded in software to execute autonomous missions from takeoff to landing while completing missions, including collecting and disseminating ISR data.

Research Objectives

The technical objectives are to: (1) build an implementable hierarchical software architecture for fully autonomous systems that synthesizes mission planning with sensing, monitoring, fault management, and decision making; (2) design mission generation algorithms based on the proposed software architecture using fuzzy sets, dynamic programming, and AI-related optimizations; and (3) develop a software & hardware toolset that generates onboard UAV autonomous mission plans, following the proposed software architecture..

Research Methods
  1. Literature Review on Missing Planning algorithms / software packages
  2. Designing system block diagram
  3. Developing software architecture
  4. Implementing the proposed concept on software and hardware
Research Deliverables and Products
  1. Reports
  2. System descriptions/documentation
  3. Demo system with hardware/software
Research Timeline

Start Date: Jan. 24, 2022 
End Date: August 31, 2022

Research Team

Lead Researchers 

  • Xiaojun (Ashley) Geng

Collaborators

  • Sembiam Rengarajan, College of Engineering and Computer Science

Student Team

  • Sarah Cazessus
  • Noah Preheim

Funding

  • Funding Organization:
  • Funding Program: