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

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.

I. Introduction

Robots have found extensive use in various industries, leading to the emergence of different research branches focused on robotic applications. In the past decade, there has been a significant shift towards autonomous robots, which has attracted considerable attention from researchers and the public alike. Prior to the rapid development of automation in the past ten years, robots were predominantly operated manually. Autonomous mobile robots (AMRs) have gained popularity due to their ability to move and perform tasks with a high degree of autonomy. The pursuit of achieving a high level of autonomy in robot applications has become increasingly valuable. AMRs can operate in environments without the need for human intervention or electro-mechanical assistance, which has expanded their potential and applicability across various domains[1]. One crucial aspect of AMRs is route planning, particularly in terms of collision avoidance during tasks that involve moving from a starting point to an endpoint [2].