Motion Planning for Robot

－Motion Forecasting with 3D Data－

Abstract

Recently, remarkable advancements in an autonomous mobile robot technology enable increase a situation which humans and robots work in a common space. However, it makes collision risk higher as we enjoy benefit from robots even closer. To avoid this problem, safety robot having capability to forecast human motion and move without clash will be important. We propose a method to forecast a human walking motion using 3D poses estimation and deep learning. In addition, we present what optimizer is better for this task and which is better model training, use 2D datasets or 3D datasets. We collected 3D patterns of motion data from depth camera, Intel Realsense. Also, we considered actually mounting our system on an autonomous mobile robot by using NVIDIA Jetson Xavier NX to obtain data in real time.