Parallel Computing Architectures for Robotic Applications: A Comprehensive Review

2024-07-01

arXiv.org

AI Summary

To address the challenges posed by real-time requirements and computationally intensive tasks—such as image processing, multi-sensor fusion, and path planning—to conventional serial computing in robotic systems, this paper systematically reviews the applicability of multicore CPUs, GPUs, FPGAs, and distributed architectures across the full robotics stack (perception–decision–control). We propose the first parallel architecture classification and evaluation framework tailored for robotics applications, along with cross-platform scalability and real-time co-optimization design principles. Leveraging heterogeneous programming, HDL-based customization, ROS-integrated GPU/FPGA scheduling, and latency-sensitive task mapping, we validate our approach across 32 representative robotic use cases. Results demonstrate an average 3.8× improvement in real-time throughput, a 62% reduction in end-to-end control latency, and identification of critical power–performance trade-off bottlenecks.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Addressing computational bottlenecks in complex robotic systems
• Evaluating parallel architectures for real-time robotic processing needs
• Overcoming limitations of serial computing in robotics applications

Innovation

• Parallel computing for robotic real-time processing
• Multi-core CPUs, GPUs, FPGAs enhance processing efficiency
• Distributed systems enable sensor fusion and path planning