A Deep Neural Network Based Human Following Robot with Fuzzy Control This research aims to build a human following robot system based on a deep neural network algorithm in which a fuzzy controller controls the robot velocity and keeps the target person in the centre position of robotxe2x80x99s view. Firstly, the system utilizes the deep neural network algorithm to detect a target person in the video sequence captured from a real sense D435 depth camera mounted on the mobile robot. Then, the system calculates the centre position of the target human and acquires the depth value of target human. Finally, these data are used as the inputs of a fuzzy controller to control the velocity and steering of the robot during tracking. Especially, the velocity of the robot which is normally limited as a constant in most existing human following robot systems is controlled by a fuzzy controller in this paper. The proposed system is verified through the experiments for a fourwheel steered mobile robot.

Robot Control in Human Environment using Deep Reinforcement Learning and Convolutional Neural Network Deep reinforcement learning (DRL) has been employed in numerous applications where complex decisionmaking is needed. Robot control in a human environment is an example. Such algorithm offers possibilities to achieve endtoend training which learns from image directly. However, training on a physical robotic system under human environments using DRL is inefficient and even dangerous. Several recent works have used simulators for training models before implementing to physical robots. Although simulation provides efficiency to obtain DRL trained models, it poses challenges for the transformation from simulation to reality. Since a human environment is often cluttered, dynamic and complex, the policy trained with simulation images is not applicable for reality. Therefore, in this paper, we propose a DRL method to achieve endtoend training in simulation, as well as to adapt to reality without any further finetune. Firstly, a Deep Deterministic Policy Gradient algorithm (DDPG) is employed to learn policy for robot control. Secondly, a pretrained Convolutional Neural Network algorithm (CNN) is used to visually track the target in image. This technique provides the efficient and safe DRL training in simulation while offering robust application when a real robot is placed in dynamic human environment. Simulation and experiment are conducted for validation and can be seen in the attached video. The results have shown successful demonstration under various complex environments.