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11 results on '"Araújo, Rodrigo F."'

3. Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)

Author

Araújo, Rodrigo F., Ribeiro, Alexandre, Bessa, Iury V., Cordeiro, Lucas C., and Filho, João E. C.

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

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)

Published
2017

4. Counterexample Guided Inductive Optimization

Author

Araujo, Rodrigo F., Albuquerque, Higo F., de Bessa, Iury V., Cordeiro, Lucas C., and Filho, Joao Edgar C.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science
Abstract

This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima.

Published
2017

7. OptCE: A Counterexample-Guided Inductive Optimization Solver

Author

Albuquerque, Higo F., Araújo, Rodrigo F., Bessa, Iury V., Cordeiro, Lucas C., de Lima Filho, Eddie B., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cavalheiro, Simone, editor, and Fiadeiro, José, editor

Published
2017
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11. Gain‐scheduled control design for discrete‐time nonlinear systems using difference‐algebraic representations.

Author

Reis, Gabriela L., Araújo, Rodrigo F., Torres, Leonardo A. B., and Palhares, Reinaldo M.

Subjects
*NONLINEAR systems, *DISCRETE-time systems, *STATE feedback (Feedback control systems), *LINEAR matrix inequalities, *LYAPUNOV functions, *PSYCHOLOGICAL feedback
Abstract

Summary: This paper addresses the local stabilization problem of nonlinear systems described by Difference‐Algebraic Representations (DAR). A novel set of sufficient Linear Matrix Inequalities (LMI) conditions are developed to design gain‐scheduled state feedback controllers. The proposed approach uses parameter‐dependent Lyapunov functions and new auxiliary decision variables aiming to obtain less conservative results. Two optimization problems are proposed to either obtain the largest estimated Domain‐of‐Attraction (DOA) or minimize the ℓ2‐gain from the energy‐bounded disturbance input to the performance output. Furthermore, this investigation considers control input saturation and system states constrained in a polyhedral region. Numerical examples are provided to illustrate the effectiveness of the proposed methodology, showing favorable comparisons with recently published similar approaches. [ABSTRACT FROM AUTHOR]

Published
2021
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