Integrated FSM and Visual Servoing for Autonomous Object Search in Indoor Mobile Robot

Abstract

Autonomous mobile robots require reliable coordination among navigation, perception, tracking, and precision approach modules to complete indoor object-search missions. Existing systems often remain fragmented, treating navigation, detection, tracking, and docking as separate tasks rather than as an end-to-end pipeline. This study has two objectives: to validate a fault-tolerant coordination architecture for autonomous search-and-approach behavior and to compare two search strategies under controlled indoor target-position scenarios. The primary contribution is a methodological integration framework based on a Finite State Machine (FSM) that coordinates ROS 2 Navigation2 global navigation, YOLO11n object detection, centroid tracking, and Image-Based Visual Servoing (IBVS), while managing transitions among navigation, visual servoing, recovery, and mission-completion states. A quantitative Gazebo simulation experiment used 40 controlled trials to compare Random Exploration and Waypoint-Based Search. The integrated system achieved a 100% mission success rate without command conflicts, indicating effective FSM-based coordination between global navigation and local visual control. Waypoint-Based Search was more efficient when the target was aligned with predefined nodes, achieving a mean detection time of 50.55 s compared with 164.80 s for Random Exploration. Conversely, Random Exploration performed better when the target was away from predefined paths, reducing mean detection time to 87.00 s compared with 183.64 s. Fault-tolerant behavior was demonstrated in simulation through successful mission completion despite repeated LiDAR-triggered obstacle-recovery events during visual approach. These findings show that search efficiency depends on alignment between exploration design and spatial structure, not universal strategy superiority.