Modular Robot Control and Continuous Constraint Satisfaction
Markus Fromherz, Tad Hogg, Warren Jackson, and Yi Shang
Abstract
Continuous constraint satisfaction is at the core of many real-world
applications. One example is in the control of modular,
hyper-redundant robots, which are robots with many more degrees of
freedom than required for typical tasks. Casting the control problem
as a constraint problem is a promising approach for robustly handling
a variety of non-standard constraints found in such robots. However,
before we can scale to the many degrees of freedom and nonlinearities
of this system and deploy constraint solvers for embedded, real-time
control, we need to better understand the complexity issues arising in
these problems. In this paper, we first present a parametric model
for robotic control. We then study the complexity of related but
simpler problems by analyzing two classes of artificial constraint
satisfaction problems inspired by (discrete) 3-SAT problems, which
have a strong relation between structure and search cost. With this,
we also propose a generic benchmarking model for continuous constraint
satisfaction problems.
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