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)

翻译：现实世界中的导航充满挑战，且充斥着复杂场景。基准自主机器人导航（BARN）挑战赛是一项聚焦于高度受限空间的竞赛。参赛队伍需使用标准平台，在仿真环境和真实场景中完成从简单到困难的各类任务。本技术报告详细阐述了英业达团队在2023年BARN挑战赛（https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge23.html）中所采用的系统与方法。我们的核心方法基于学习型控制器LfLH基线方案，并通过有限状态机触发恢复行为进行了扩展，同时引入了两种基于足迹膨胀与模型预测控制的前向安全碰撞检测替代方案。此外，我们还提出了一种基于代价地图感兴趣区域的后向安全检测方法。相较于原始基线，我们的导航评分从0.2334显著提升至0.2445（增幅4.76%）。最终，团队在仿真和真实场景阶段均获得第二名。相关代码已公开于：https://github.com/inventec-ai-center/inventec-team-barn-challenge-2023.git