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.
Highlights
Robotic systems were instrumental in developing tasks that require precision, decreased time, high demand, and cost reduction
A robotic system can get advanced features based on the definition of artificial intelligence (AI): it is the field of science that helps machines in the ability to improve their functions; in areas of logic, reasoning, planning, learning, and perception [3]
Due to the auto-encoder generating a sequence of nodes corresponding to a vector, we suggest metrics to compare vectors between a expected vector x with an generated vector y, such as euclidean distance (14), Manhattan distance, (15) and cosine similarity distance (16)
Summary
Robotic systems were instrumental in developing tasks that require precision, decreased time, high demand, and cost reduction. One of the elementary issues of autonomous robotic systems is the path planning problem This problem consists of the perception of an environment to generate a path that avoids obstacles. Likewise, it has mainly two modules based on the definition of AI. Due to the high requirements in complex systems, the end-to-end approach has an essential role because this approach consists of reducing external elements such as sensors [6] Another approach to minimize the resources is the transfer learning approach. This development uses a virtual environment to train agents [9] Another technology that enriches experience is augmented reality (AR), which combines the real and virtual, adding extra information using virtual items.
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