Parallel Computing Architectures for Robotic Applications: A Comprehensive Review

TL;DR

This paper reviews various parallel computing architectures like multi-core CPUs, GPUs, FPGAs, and distributed systems, highlighting their benefits, challenges, and applications in enhancing robotic system performance.

Contribution

It provides a comprehensive overview of parallel computing architectures in robotics, including case studies, comparisons, and discussions on challenges and future research directions.

Findings

Parallel architectures significantly improve real-time processing in robotics.

Case studies demonstrate performance gains with GPUs and FPGAs.

Challenges include integration complexity and power consumption.

Abstract

With the growing complexity and capability of contemporary robotic systems, the necessity of sophisticated computing solutions to efficiently handle tasks such as real-time processing, sensor integration, decision-making, and control algorithms is also increasing. Conventional serial computing frequently fails to meet these requirements, underscoring the necessity for high-performance computing alternatives. Parallel computing, the utilization of several processing elements simultaneously to solve computational problems, offers a possible answer. Various parallel computing designs, such as multi-core CPUs, GPUs, FPGAs, and distributed systems, provide substantial enhancements in processing capacity and efficiency. By utilizing these architectures, robotic systems can attain improved performance in functionalities such as real-time image processing, sensor fusion, and path planning. The transformative potential of parallel computing architectures in advancing robotic technology has been underscored, real-life case studies of these architectures in the robotics field have been discussed, and comparisons are presented. Challenges pertaining to these architectures have been explored, and possible solutions have been mentioned for further research and enhancement of the robotic applications.