  The potential impact of autonomous robots on everyday life is evident in
emerging applications such as precision agriculture, search and rescue, and
infrastructure inspection. However, such applications necessitate operation in
unknown and unstructured environments with a broad and sophisticated set of
objectives, all under strict computation and power limitations. We therefore
argue that the computational kernels enabling robotic autonomy must be
scheduled and optimized to guarantee timely and correct behavior, while
allowing for reconfiguration of scheduling parameters at run time. In this
paper, we consider a necessary first step towards this goal of computational
awareness in autonomous robots: an empirical study of a base set of
computational kernels from the resource management perspective. Specifically,
we conduct a data-driven study of the timing, power, and memory performance of
kernels for localization and mapping, path planning, task allocation, depth
estimation, and optical flow, across three embedded computing platforms. We
profile and analyze these kernels to provide insight into scheduling and
dynamic resource management for computation-aware autonomous robots. Notably,
our results show that there is a correlation of kernel performance with a
robot's operational environment, justifying the notion of computation-aware
robots and why our work is a crucial step towards this goal.
