Instrumentation & Measurement Magazine 25-5 - 25

Table 1 - Comparison of the typical onboard computing platforms in UAS
Typical Platform
Pixhawk 4
Intel Aero Compute Board
NVIDIA Jetson Xavier
UAV and Robotics Platform
Processor
ARM
CPU
Embedded GPU
Heterogeneous SoC
available edge computing platforms in UAS and analyzes the applicable
computing task scenarios of each computing platform
to provide guidelines for addressing the computing bottleneck.
Under the constraints of SWaP, some computing platforms
have been widely used in flight control, payload data
processing, data acquisition and control, etc. These onboard
computing platforms in UAS can be the candidate edge
computing platforms for the embedded OCM instrument.
However, the data processing scenarios suitable for onboard
computing platforms with different architectures are different.
Therefore, in the development of the embedded OCM instrument,
a reasonable selection of the computing platform should
be considered first. Table 1 shows the comparison of different
onboard computing platforms that are commonly used, and
Fig. 2 shows the typical onboard computing platforms in UAS.
These onboard computing platforms can combine their
characteristics in power consumption and computing performance
with data analysis requirements to develop different
embedded OCM instruments for different scenarios. Different
types of platforms are analyzed, as described in the following
section.
ARM-based Onboard Computing Platform
This kind of platform has extremely low power consumption,
which makes it a good choice for UAS control. It can run a realtime
operating system, collect the sensor readings, and execute
control laws to control UAS [10]. Due to insufficient computing
performance, it is often used in scenarios where simple data
analysis algorithms are executed and power consumption is
severely limited.
CPU-based Onboard Computing Platform
The CPU-based platform has multi-core and good computing
performance but higher power consumption than the
Power Consumption
+
++
+++
++
Capability
Serial
+
+++
++
++
Parallel
-
-
++
+
ARM-based platform. For example, Intel has launched a CPUbased
Intel Aero Compute Board specifically for UAS [11].
Though its serial computing capability is enough for logical
control and general data processing tasks, the computing
performance is still insufficient, and it is hard to ensure computational
timeliness when dealing with compute-intensive
tasks such as deep learning models. Therefore, a CPU-based
platform is more suitable for IO-intensive data processing
tasks.
GPU-based Onboard Computing Platform
The GPU is a definite leader in parallel computing by virtue of
its architectural advantages. A large number of cores makes it
good at performing many simple operations in parallel at the
same time. But the platform also has high power consumption
while providing high computing performance. It is usually
used for compute-intensive tasks. For example, NVIDIA Jetson
Xavier has been widely used in image processing based on
deep convolution neural networks, where the GPU can show
the powerful parallel processing ability of a single instruction
with multiple data [12]. However, when processing the condition
monitoring data stream, due to the characteristics of small
data sample size and high sampling rate, the GPU consumes
a lot of time in data and model IO operations, making it difficult
to give full play to its advantages of parallel computing.
Heterogeneous SoC-based Onboard Computing
Platform
A heterogeneous System on Chip (SoC) fuses processors of different
architectures into one chip, which makes it possible to
achieve a balance between power consumption, serial computing,
and parallel computing. For instance, together with
Xilinx, TOPIC launched a heterogeneous SoC-based UAV and
robotics platform for UAS [13]. Its processor is an UltraScale+
Fig. 2. Typical onboard computing platforms in UAS.
August 2022
IEEE Instrumentation & Measurement Magazine
25

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