Autonomous Package Delivery Car Robot System with 2D Point Cloud Reconstruction and Object Detection as A Logistics Delivery Robot

Abstract

This research develops an autonomous package delivery car robot system using the A* algorithm on 2D Point Cloud data from LIDAR and object detection with YOLO. The pathfinding system using the A* algorithm successfully navigates the robot with a success rate of 100%, an average processing time of 1.6 ms, and an average cost of 8.09. The robot is designed with specifications of operating temperature of -40°C to +85°C, operating time of 180 minutes, and maximum payload capacity of 5 kg. Object detection testing using YOLOv4-tiny on 2D Point Cloud resulted in an Average Precision (AP) of 100% for cars and humans, 92.74% for motorcycles, with a detection sensitivity of 68.3%. The distance prediction system has 97% accuracy, with an average prediction difference of 0.092 meters. This research shows that the application of the A* algorithm to 2D Point Cloud data improves the efficiency and accuracy of robot movement, and makes a significant contribution to the development of autonomous vehicles for logistics applications.