Urban Air Mobility (UAM) applications, such as air taxis, will rely heavily on perception
for situational awareness and safe operation. With recent advances in AI/ML, state-of-the-art
perception systems can provide the high-fidelity information necessary for UAM systems.
However, due to size, weight, power, and cost (SWaP-C) constraints, the available computing
resources of the on-board computing platform in such UAM systems are limited. Therefore,
real-time processing of sophisticated perception algorithms, along with guidance, navigation,
and control (GNC) functions in a UAM system, is challenging and requires the careful allocation
of computing resources.
Furthermore, the optimal allocation of computing resources may change over time depending
on the speed of the vehicle, environmental complexities, and other factors. For instance, a
fast-moving air vehicle at low altitude would need a low-latency perception system, as a long
delay in perception can negatively affect safety. Conversely, a slowly landing air vehicle in a
complex urban environment would prefer a highly accurate perception system, even if it takes a
little longer. However, most perception and control systems are not designed to support such
dynamic reconfigurations necessary to maximize performance and safety.
We advocate for developing “anytime” perception and control capabilities that can dynamically
reconfigure the capabilities of perception and GNC algorithms at runtime to enable safe
and intelligent UAM applications. The anytime approach will efficiently allocate the limited
computing resources in ways that maximize mission success and ensure safety. The anytime
capability is also valuable in the context of distributed sensing, enabling the efficient sharing of
perception information across multiple sensor modalities between the nodes.
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Free, publicly-accessible full text available January 4, 2025
