14 research outputs found
Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)
We describe and evaluate a novel optimization-based off-line path planning
algorithm for mobile robots based on the Counterexample-Guided Inductive
Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples
generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories
(SMT) solvers, in order to guide the optimization process and to ensure global
optimization. This paper marks the first application of CEGIO for planning
mobile robot path. In particular, CEGIO has been successfully applied to obtain
optimal two-dimensional paths for autonomous mobile robots using off-the-shelf
SAT and SMT solvers.Comment: 7 pages, 14rd Latin American Robotics Symposium (LARS'2017
Verification of Magnitude and Phase Responses in Fixed-Point Digital Filters
In the digital signal processing (DSP) area, one of the most important tasks
is digital filter design. Currently, this procedure is performed with the aid
of computational tools, which generally assume filter coefficients represented
with floating-point arithmetic. Nonetheless, during the implementation phase,
which is often done in digital signal processors or field programmable gate
arrays, the representation of the obtained coefficients can be carried out
through integer or fixed-point arithmetic, which often results in unexpected
behavior or even unstable filters. The present work addresses this issue and
proposes a verification methodology based on the digital-system verifier
(DSVerifier), with the goal of checking fixed-point digital filters w.r.t.
implementation aspects. In particular, DSVerifier checks whether the number of
bits used in coefficient representation will result in a filter with the same
features specified during the design phase. Experimental results show that
errors regarding frequency response and overflow are likely to be identified
with the proposed methodology, which thus improves overall system's
reliability
Verification of fixed-point digital controllers using direct and delta forms realizations
OptCE: A Counterexample-Guided Inductive Optimization Solver
This paper presents optimization through counterexamples
(OptCE), which is a verification tool developed for optimizing target
functions. In particular, OptCE employs bounded model checking techniques
based on boolean satisfiability and satisfiability modulo theories,
which are able to obtain global minima of convex and non-convex functions.
OptCE is implemented in C/C++, performs all optimization steps
automatically, and iteratively analyzes counterexamples, in order to inductively
achieve global optimization based on a verification oracle. Experimental
results show that OptCE can effectively find optimal solutions
for all evaluated benchmarks, while traditional techniques are usually
trapped by local minima