SOCA: A Semantic Object-Class-Aware Adaptive Obstacle Avoidance Framework for Autonomous Mobile Robots

Conventional local planners treat obstacles as uniform geometric entities, regardless of whether an object is deformable, fragile, or a person. This paper presents SOCA (Semantic Object-Class-Aware), a semantic costmap layer for an unmodified Dynamic Window Approach (DWA) planner. Given externally supplied object identity or Semantic Safety Class (SSC) and planar geometry, SOCA converts five navigation-safety attributes into a class-dependent exclusion core and graded cost band. SOCA was compared with DWA and two class-blind baselines in 750 Gazebo runs and 300 physical trials, including 120 logged trials. With one obstacle, SOCA maintained clearances of 0.35, 0.40, and 0.91 m from a plastic bag, glassware, and a mannequin, respectively, compared with 0.23–0.28 m for DWA. With three objects, SOCA maintained greater person-class clearance than DWA in every label permutation (p = 0.002), with 0.3–0.8 s longer traversal times. On the robot, SOCA maintained greater clearance than DWA for every object class in all 30 paired blocks and was faster than the uniform baseline. Forced labels shifted clearance toward the assigned class, and the class ordering was preserved for the tested speed coefficients and enlarged preferred clearances. These results apply to stationary objects with supplied labels; moving entities remain outside the present scope.