Path Planning Generator with Metadata through a Domain Change by GAN between Physical and Virtual Environments

Abstract

Increasingly, robotic systems require a level of perception of the scenario to interact in real-time, but they also require specialized equipment such as sensors to reach high performance standards adequately. Therefore, it is essential to explore alternatives to reduce the costs for these systems. For example, a common problem attempted by intelligent robotic systems is path planning. This problem contains different subsystems such as perception, location, control, and planning, and demands a quick response time. Consequently, the design of the solutions is limited and requires specialized elements, increasing the cost and time development. Secondly, virtual reality is employed to train and evaluate algorithms, generating virtual data. For this reason, the virtual dataset can be connected with the authentic world through Generative Adversarial Networks (GANs), reducing time development and employing limited samples of the physical world. To describe the performance, metadata information details the properties of the agents in an environment. The metadata approach is tested with an augmented reality system and a micro aerial vehicle (MAV), where both systems are executed in an authentic environment and implemented in embedded devices. This development helps to guide alternatives to reduce resources and costs, but external factors limit these implementations, such as the illumination variation, because the system depends on only a conventional camera.

Keywords

• autonomous driving
• machine learning
• computer vision
• virtual training