Assisted Counterexample-Guided Inductive Optimization for Robot Path Planning

Mengze Li, Lucas Cordeiro

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper presents and evaluates a novel offline
mobile robot path planning algorithm based on the Assisted
Counterexample-Guided Inductive Optimization (ACEGIO)
technique. ACEGIO employs a technique to assist the
Counterexample-Guided Inductive Optimization (CEGIO) technique
by requesting counterexamples from Boolean Satisfiability
(SAT) and Satisfiability Modulo Theories (SMT) solvers to
improve its efficiency and effectiveness. In particular, we implemented
the Gradient Descent (GD) technique as an auxiliary
technique to CEGIO. GD-Assisted Counterexample-Guided Inductive
optimization (ACEGIO-GD) has been successfully applied
to obtain two-dimensional paths for autonomous mobile robots
using off-the-shelf SAT and SMT solvers. Experimental results
demonstrate that the ACEGIO-based path planning algorithm
has substantial improvements in efficiency and effectiveness compared
to the traditional CEGIO-based path planning algorithm,
which allows generating the optimal paths for autonomous mobile
robots with much less execution time. If compared to other
traditional path planning optimization techniques (e.g., GA and
PSO), the execution time of the proposed algorithm is relatively
high, whereas its performance is stable, reliable and robust.
Original languageEnglish
Publication statusPublished - 2021
EventXI Brazilian Symposium on Computing Systems Engineering -
Duration: 22 Nov 202125 Nov 2021

Conference

ConferenceXI Brazilian Symposium on Computing Systems Engineering
Period22/11/2125/11/21

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