Semantic-Aware Obstacle Tracking and Avoidance for Ceiling-Mounted Healthcare Robots

Automation of healthcare workflows and devices demands safe and trustworthy robotic behavior, particularly in environments shared with patients and medical staff. For ceiling-mounted imaging robots, the key challenge lies in perceiving and monitoring the 3D workspace to plan safe, collision-free motions around people and equipment. Beyond simple obstacle avoidance, semantic understanding is essential to distinguish between object types — such as patients, walking aids, or medical tools — and to adapt motion behavior accordingly. We address this challenge with a semantic-aware obstacle tracking and avoidance pipeline that extends prior 2D semantic navigation concepts into full 3D space. The approach combines 2D semantic segmentation with depth projection to estimate object positions and dimensions in real time from RGB-D data. These detections are fused in a tracking module to build a continuous, semantic world model from which class-dependent safety margins are derived. The resulting information enables adaptive motion planning that increases distance from high-risk objects (e.g., persons) or reduces velocity when close interaction is required. Experiments on a real ceiling-mounted robot in laboratory scenarios demonstrate the system’s ability to enhance safety, predictability, and contextual awareness during automated healthcare procedures.