Collision Avoidance and Trajectory Planning for Autonomous Mobile Robot: A Spatio-Temporal Deep Learning Approach

Abstract

The field of autonomous mobile robots has been gaining significant attention in various industries and research domains. As the future of robotic process automation unfolds, there is an increasing demand for precise robot movement in terms of collision avoidance and trajectory planning. This paper presents a camera-based autonomous mobile robot system that addresses these requirements. The proposed system utilizes a deep learning variational autoencoder with a spatio-temporal model for image analysis processing. This approach enables the system to effectively analyze and understand the visual information. By leveraging deep learning techniques, the system can extract meaningful features and representations from the images, facilitating accurate perception and understanding of the robot's surroundings. This paper contributes to the advancement of autonomous mobile robot systems by proposing a deep learning techniques with reinforcement learning algorithms. The approach offers promising possibilities for enhancing the control and interaction capabilities of mobile robots in real-world scenarios.