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Motion planning with task scheduling in heterogeneous computing systems
Fields, Noah B.
Fields, Noah B.
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2023
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Abstract
Motion planning is an important problem in many contexts of
robotics. Heterogeneous computing systems in robots are able to run
tasks on different processing units in varying orders, but with
different impacts on the robot's state and performance. Currently
existing sampling-based motion planning frameworks explore a state
space through typically random sampling to create a path to a goal
region, but only consider physical obstacles in their way such as
walls, and do not consider the constraints of computational
requirements on the path or the impacts of choosing different
schedules for computation. We introduce a novel system which uses
Petri nets as a modeling system on the computational requirements, and
uses constraint solvers to find computation schedules for the motion
planning tasks. This allows us to select motions based not only on
their physical validity, but also computation-related parameters. We
subdivide a space of constraints on the system into regions, enabling
schedule reuse in order to improve the algorithm's efficiency. We also
discuss the use of Petri nets to model another aspect of computation
in a heterogeneous environment, memory contention. Our system enables
us to consider physical dynamics such as heat and power in a way that
prior systems are not capable. We demonstrate that our system can
handle a variety of constraints of different severities, and can avoid
computational obstacles more effectively than na\"ive planning systems
which do not consider computational constraints and instead disallow
regions as though they are physical obstacles.
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