Mission and operational requirements for effective
and robust search and track lead to defining a number
of cognitive capabilities in sensing, communication,
and intelligent control and navigation during conceptual
design. Autonomy can be enabling or enhancing for
one or more of these capabilities. The design components of autonomy for search and track consist of
algorithms and associated software models for planning, execution, search, detection, and tracking; sensing, processing, and memory hardware; aerial platforms;
and infrastructure for development and testing (
including ROS, simulation environments such as MORSE,
and indoor or outdoor field testing). In addition, autonomy performance testing for search and track scans a
wide range of goal-directed behaviors, from purely reactive (maintaining an object in view) to tactical
(knowing when to switch from search to track mode
when an object is found) to deliberative (generating
effective plans for search). Finally, testing a system for
autonomy involves the three metrics of effectiveness
(does autonomy enhance or enable behaviors that accomplish mission and operational goals?), robustness
(does autonomy adapt successfully to changes in its
mission or operational environment?), and safety. These
considerations of autonomy have never been a part of
traditional conceptual design, as discussed earlier.
We presented a simple example of conceptual design for a search and track mission based on fast development and testing of autonomous cognitive
capabilities in planning and execution. These requirements led to selection of platform and sensor
hardware and software. We illustrated the role of
simulation and testing in isolating a particular behavior
of the executive to alternate between search and track,
measuring the effectiveness of this component.
This article argued for autonomy as part of concep-
tual design to ensure optimal design and acceptance.
We reviewed principles of conceptual design for
aeronautical vehicles, as well as the capabilities and
performance metrics of autonomous systems. We
reviewed architectures for autonomy and argued that
autonomy is inherently layered, distributed, and human
centered. We briefly discussed how considerations of
autonomy change the conceptual design process,
starting from determining whether autonomy is en-
abling or enhancing for a mission. Finally, we walked
through a simple but illustrative example of autono-
mous search and track for sUAVs.
As noted by Russell and Norvig (2016), “Intelli-
gence is concerned mainly with rational action; ide-
ally, an intelligent agent takes the best possible action
in a situation.” Here we have quantified rational action
as correctness and responsiveness of an autonomous
system. Considering autonomy at the conceptual de-
sign phase should be an integral part of designing
future autonomous systems. Specifically, a more rig-
orous design based on autonomy will reduce design
cycles and better ensure successful deployment.
The authors contributed equally to this article. This
work was conducted through NASA’s Convergent
Aeronautics Solution project, through the Design
Environment for Novel Vertical Lift Vehicles (DE-
LIVER) subproject. The DELIVER team also consisted
of Xavier Bouyssounouse and Rusty Hunt. The au-
thors acknowledge contributions made by interns
Suneel Belkhele, Sohan Vichare, and Adiyan Kaul and
thank the anonymous reviewers for helpful sugges-
tions in revising a previous draft.
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