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Donoghue, Brendan
UA-1 PH2 DECISIVE Testing Handbook: Test Methods and Benchmarking Performance Results for sUAS in Dense Urban Environments
Norton, Adam, Donoghue, Brendan, Gavriel, Peter
This report outlines all test methods and reviews all results derived from performance benchmarking of small unmanned aerial systems (sUAS) in dense urban environments conducted during Phase 2 of the Development and Execution of Comprehensive and Integrated Systematic Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell (HEROES Project UA-1). Using 9 of the developed test methods, over 100 tests were conducted to benchmark the performance of 8 sUAS platforms: Cleo Robotics Dronut X1P (P = prototype), FLIR Black Hornet 3 PRS, Flyability Elios 2 GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden Eagle, and Vantage Robotics Vesper.
DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1
Norton, Adam, Ahmadzadeh, Reza, Jerath, Kshitij, Robinette, Paul, Weitzen, Jay, Wickramarathne, Thanuka, Yanco, Holly, Choi, Minseop, Donald, Ryan, Donoghue, Brendan, Dumas, Christian, Gavriel, Peter, Giedraitis, Alden, Hertel, Brendan, Houle, Jack, Letteri, Nathan, Meriaux, Edwin, Khavas, Zahra Rezaei, Singh, Rakshith, Willcox, Gregg, Yoni, Naye
This handbook outlines all test methods developed under the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell 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. For sUAS deployment in subterranean and constrained indoor environments, this puts forth two assumptions about applicable sUAS to be evaluated using these test methods: (1) able to operate without access to GPS signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in) wide (i.e., can physically fit through a typical doorway, although successful navigation through is not guaranteed). All test methods are specified using a common format: Purpose, Summary of Test Method, Apparatus and Artifacts, Equipment, Metrics, Procedure, and Example Data. All test methods are designed to be run in real-world environments (e.g., MOUT sites) or using fabricated apparatuses (e.g., test bays built from wood, or contained inside of one or more shipping containers).
DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I
Norton, Adam, Ahmadzadeh, Reza, Jerath, Kshitij, Robinette, Paul, Weitzen, Jay, Wickramarathne, Thanuka, Yanco, Holly, Choi, Minseop, Donald, Ryan, Donoghue, Brendan, Dumas, Christian, Gavriel, Peter, Giedraitis, Alden, Hertel, Brendan, Houle, Jack, Letteri, Nathan, Meriaux, Edwin, Khavas, Zahra Rezaei, Singh, Rakshith, Willcox, Gregg, Yoni, Naye
This report reviews all results derived from performance benchmarking conducted during Phase I of the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell, using the test methods specified in the DECISIVE Test Methods Handbook v1.1 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. Using those 20 test methods, over 230 tests were conducted across 8 sUAS platforms: 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. Best in class criteria is specified for each applicable test method and the sUAS that match this criteria are named for each test method, including a high-level executive summary of their performance.
Hamiltonian descent for composite objectives
O', Donoghue, Brendan, Maddison, Chris J.
In optimization the duality gap between the primal and the dual problems is a measure of the suboptimality of any primal-dual point. In classical mechanics the equations of motion of a system can be derived from the Hamiltonian function, which is a quantity that describes the total energy of the system. In this paper we consider a convex optimization problem consisting of the sum of two convex functions, sometimes referred to as a composite objective, and we identify the duality gap to be the energy' of the system. In the Hamiltonian formalism the energy is conserved, so we add a contractive term to the standard equations of motion so that this energy decreases linearly (ie, geometrically) with time. This yields a continuous-time ordinary differential equation (ODE) in the primal and dual variables which converges to zero duality gap, ie, optimality.