A diagram showing each research area of the DECISIVE project: field readiness, trust, interface, situation awareness, communications, obstacle avoidance, navigation, autonomy, and mapping. The images in the diagram show an operator holding a controller flying a drone into an indoor facility, with example bar charts of performance data.

This page features products that are the result of the UA-1 DECISIVE: Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations project by the University of Massachusetts Lowell. This project is sponsored by the Department of the Army, U.S. Army Combat Capabilities Development Command Soldier Center (W911QY-18- 2-0006).

DECISIVE Test Methods Handbook

This handbook outlines all test methods developed under the DECISIVE project for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communications, field readiness, interface, obstacle avoidance, navigation, mapping, autonomy, trust, and situation awareness. Specifications for 20 test methods are included:

Test Methods

  • Non-Line-of-Sight (NLOS) Communications
  • Non-Line-of-Sight (NLOS) Video Latency
  • Interference Reaction
  • Runtime Endurance
  • Takeoff and Land/Perch
  • Room Clearing
  • Indoor Noise Level
  • Logistics Characterization
  • Operator Control Unit (OCU) Characterization
  • Obstacle Avoidance and Collision Resilience
  • Position and Traversal Accuracy
  • Navigation Through Apertures
  • Navigation Through Confined Spaces
  • Indoor Mapping Resolution
  • Indoor Mapping Accuracy
  • Non-Contextual Autonomy Ranking
  • Contextual Autonomy Ranking
  • Characterizing Factors of Trust
  • Interface-Afforded Attention Allocation
  • Situation Awareness (SA) Survey Comparison

The DECISIVE Test Methods Handbook (v1.1) is available on arXiv:

DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1." arXiv preprint arXiv:2211.01801, November 2022.

DECISIVE Benchmarking Data Report

This report includes the results of performance benchmarking of a set of 8 small unmanned aerial systems (sUAS) using the test methods specified in the DECISIVE test methods handbook (see above). The following sUAS platforms were evaluated: Cleo Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2 GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden Eagle, and Vantage Robotics Vesper. The data contained in the report is the result of conducting over 230 tests in-house at the NERVE Center and off-site at Fort Devens and Muscatatuck Urban Training Center, and analyses of best in class systems per test method category.

The DECISIVE Benchmarking Data Report (results from Phase I) is available on arXiv:

Adam Norton, Reza Ahmadzadeh, Kshitij Jerath, Paul Robinette, Jay Weitzen, Thanuka Wickramarathne, Holly Yanco, Minseop Choi, Ryan Donald, Brendan Donoghue, Christian Dumas, Peter Gavriel, Alden Giedraitis, Brendan Hertel, Jack Houle, Nathan Letteri, Edwin Meriaux, Zahra Rezaei Khavas, Rakshith Singh, Gregg Willcox, and Naye Yoni. "DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I." arXiv preprint arXiv:2301.07853, January 2023.

Advancing and Evaluating UAS for Defense

The capstone event for the DECISIVE project, "Advancing and Evaluating UAS for Defense," was held on October 12, 2022. The event feature research briefs on the UA-1 and UA-2 projects funded by the UML/DEVCOM HEROES program. All presentations given at the event are available for download via a publicly accessible Google Drive folder. Below is a list of the presentations given:

  • HEROES Program Overview, Dr. Christopher Drew
  • UA-1 Project Review: DECISIVE: Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations, Mr. Adam Norton
  • UA-2 Project Review: Drone-Based Wireless Sensor Network for Multimodal Underground Situational Awareness, Dr. Xuejun Lu
  • UMass Lowell UAS Project Transition, Mr. Adam Norton
  • Test Methodologies to Evaluate UAS Communication and the Effects of Packet Loss in NLOS Indoor Environments, Dr. Jay Weitzen
  • Embedding IR Sensors to Increase UAS Situational Awareness, Dr. Xuejun Lu
  • Artificial Intelligence for Image Recognition During UAS Operations, Dr. Yuanchang Xie
  • Methods for Combining and Representing Autonomy Ranking Scores of UAS, Dr. Reza Ahmadzadeh
  • Characterizing and Evaluating Field Readiness of UAS for Subterranean and Indoor Environments, Mr. Adam Norton
  • Evaluation of Indoor Navigation and Obstacle Avoidance and Resilience Capabilities of UAS, Dr. Kshitij Jerath
  • Field Testing of UAS Navigation Through Confined Spaces and Apertures, Mr. Adam Norton
  • Test Methods to Evaluate Indoor and Subterranean Mapping Capabilities of UAS, Mr. Adam Norton
  • Trustworthy and Safe Human-UAS Interaction for Subterranean Mapping, Dr. Paul Robinette
  • Assessing Operator Situational Awareness of UAS for Decision-Support in Subterranean Environments, Dr. Thanuka Wickramarathne