This work presents an innovative solution for robotic odometry, path planning and exploration in wild unknown environments, focusing on digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The evaluation carried out on a robotic platform with a lightweight 3D LiDAR sensor model, assesses the consistency and efficiency in exploring completely unknown subterranean-like areas. The algorithm allows for dynamic changes to the desired target and behaviour. At the same time, the paper details the design of AREX, highlighting its robust localisation, mapping and efficient exploration target selection capabilities, with a focus on continuity in exploration direction for increased efficiency and reduced odometry errors. The real-time, high-precision environmental perception module is identified as critical for accurate obstacle avoidance and exploration boundary identification.

翻译：本工作提出了一种创新解决方案，用于未知野外环境中的机器人里程计、路径规划和自主探索，重点聚焦数字建模。该方法采用最小成本公式与伪随机生成目标相结合，集成多路径规划与评估机制，强调基于可行兴趣边界的未知地图全覆盖。在搭载轻量级3D LiDAR传感器的机器人平台上进行的评估，验证了算法在完全未知类地下区域探索中的一致性与效率。该算法支持对期望目标与行为进行动态调整。同时，本文详细阐述了AREX系统的设计，突出其鲁棒的定位建图能力以及高效的探索目标选择机制，特别强调探索方向连续性对提升效率、减少里程计误差的作用。实时高精度环境感知模块被确认为实现精准避障和探索边界识别的关键。