Branch-Priority Exploration for Mobile Robots in Restricted Industrial Corridors

This paper proposes the Branch-Priority Exploration (BPE) framework for autonomous coverage in confined industrial corridor environments. BPE integrates three components: (1) a symmetry-aware LiDAR branch detector; (2) a hierarchical BFS/DFS mode-switching policy; and (3) a barrier-based branch memory. Frontier-based methods often struggle in industrial corridors where branches split off from the main corridor. The symmetric layout of such environments, featuring T-shaped junctions and L-shaped turns, creates recurring geometric patterns that conventional frontier scoring fails to exploit. When the robot reaches a junction, nearby frontier candidates often receive similar scores, causing repeated target switching as the local map changes. Meanwhile, frontier cells inside a branch tend to score lower than those along the main corridor; so, the robot often bypasses the branch and continues forward, which leads to additional backtracking later. Even when the robot eventually returns, residual frontier cells near the entrance may attract the planner repeatedly, causing redundant re-entry into already-covered branches. To address these issues, a branch-priority exploration framework is developed. A symmetry-aware branch detection module uses LiDAR range measurements from multiple directions to identify T-shaped junctions and lateral openings, applying identical geometric criteria to lateral openings on either side of the robot. This allows branch entry to be triggered by explicit geometric evidence, rather than frontier score comparisons that tend to be unreliable near intersections. When a branch is detected, the robot transitions from BFS mode to DFS mode for systematic branch coverage. Entry and post-return locks prevent mode reversal before the robot commits to the new heading. Once a branch is completed, a permanent virtual barrier is placed at its entrance; so, the planner no longer routes the robot back into that branch. The framework is formalized as a constrained coverage problem on occupancy grids, and monotonic coverage progress and finite branch completion under barrier memory are established theoretically. A fully reproducible ROS implementation on a wheeled platform with differential drive is validated. Experiments span several corridor environments of increasing topological complexity. Compared to a nearest-frontier baseline, the proposed method substantially reduces both exploration time and goal cancellations while achieving complete coverage across all trials. The cancellation count matches the number of T-branches per environment, with near-zero variance across repeated runs.