  Navigation in the real-world is hard and filled with complex scenarios. The
Benchmark Autonomous Robot Navigation (BARN) Challenge is a competition that
focuses on highly constrained spaces. Teams compete using a standard platform
in a simulation and a real-world stage, with scenarios ranging from easy to
challenging. This technical report presents the system and methods employed by
the Inventec Team during the BARN Challenge 2023
(https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge23.html). At
its core, our method uses the baseline learning-based controller LfLH. We
developed extensions using a finite state machine to trigger recovery
behaviors, and introduced two alternatives for forward safety collision checks,
based on footprint inflation and model-predictive control. Moreover, we also
present a backtrack safety check based on costmap region-of-interest. Compared
to the original baseline, we managed a significant increase in the navigation
score, from 0.2334 to 0.2445 (4.76%). Overall, our team ranked second place
both in simulation and in the real-world stage. Our code is publicly available
at:
(https://github.com/inventec-ai-center/inventec-team-barn-challenge-2023.git)
