Development of Mobile Robot Target Recognition and Following Behaviour Using Deep Convolutional Neural Network and 2D Range Data

Abstract

In this work, a novel approach allowing a mobile robot to recognize and follow dynamic targets is proposed. The designed behavior can simplify subsequent development of autonomous navigation and obstacle avoidance tasks as well as the capability of mobile platforms to operate within multi-agent formations. In the proposed approach, the target recognition problem is considered as an image classification task and it tackled by using a state-of-the-art deep convolutional neural network working with a sequence of images obtained from the environmental scene. The subsequent target following problem is implemented by using 2D range data. The proposed approach has been implemented on a KUKA youBot omnidirectional mobile platform using robot operating system middleware and equipped with an onboard USB camera and a 2D LiDAR. The conducted experiments have shown that the mobile robot is capable to detect and follow accurately another mobile platform (iRobot Create) in a shared environment. The target following robotized platform is also capable to maintain a pre-specified relative position and orientation with respect to the followed dynamic object by controlling its own linear and angular velocity.