  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.
