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.

翻译：随着当代机器人系统复杂性和能力的不断提升，对能够高效处理实时处理、传感器集成、决策制定和控制算法等任务的复杂计算解决方案的需求也在日益增长。传统的串行计算常常无法满足这些要求，这凸显了对高性能计算替代方案的必要性。并行计算，即同时利用多个处理单元来解决计算问题，提供了一个可能的解决方案。各种并行计算架构，例如多核CPU、GPU、FPGA和分布式系统，在处理能力和效率方面提供了显著的提升。通过利用这些架构，机器人系统可以在实时图像处理、传感器融合和路径规划等功能上获得更好的性能。本文强调了并行计算架构在推进机器人技术方面的变革潜力，讨论了这些架构在机器人领域的实际案例研究，并进行了比较分析。文中探讨了与这些架构相关的挑战，并提出了可能的解决方案，以供机器人应用的进一步研究和改进。