Abstract
In the era of autonomy, the integration of intelligent systems capable of navigating and perceiving their surroundings has become ubiquitous. Many sensors have been developed for environmental perceiving, with LIDAR emerging as a preeminent technology for precise obstacle detection. However, LIDAR possesses inherent limitations, impeding its ability to detect specific obstacles. This research addresses the identified limitations by recognizing obstacles that traditionally challenge LIDAR’s detection capabilities. Objects such as glass, carpet, wires, and ramps have been meticulously identified as Hard-to-Detect-Objects by LIDAR (HDOL). YOLO5 has been utilized to detect HDOL using a depth camera. HDOL objects are incorporated into the environmental map, circumventing the constraints posed by LIDAR.
Further, HDOL-aware coverage path planning has been proposed using boustrophedon motion with an A* algorithm to navigate the robot in the environment safely. Real-world experiments have validated the applicability of the proposed method for ensuring robot safety.