面向FPGA机器人的移动机器人控制算法开发
Development of control algorithms for mobile robotics focused on their potential use for FPGA-based robots
AI总结：
本文研究移动机器人控制算法在FPGA上的实现与优化，涵盖PID和深度学习等方法，提升导航与避障性能，并指出标准化评估需求，为自主性改进提供方向。
摘要：
本文研究了移动机器人控制算法的开发与优化，重点关注其在现场可编程门阵列（FPGA）中的实现。文章深入探讨了经典控制方法（如PID）和现代技术（包括深度学习），并阐述了它们在从工业自动化到医疗护理等领域的应用。研究强调了将这些算法嵌入FPGA所面临的实际挑战和取得的进展，FPGA因其高速处理和并行计算能力，为移动机器人带来了显著优势。通过对各种控制策略的分析，论文展示了机器人性能的提升，特别是在导航和避障方面。文章强调了FPGA在动态环境中提高控制算法效率和适应性方面的关键作用。此外，研究还讨论了在现实应用中对这些算法进行基准测试和性能评估的困难，提出了对标准化评估标准的需求。本工作的贡献在于全面考察了控制算法在基于FPGA的移动机器人中的潜力，为未来提高机器人自主性和运行效率的研究方向提供了见解。
英文摘要：
This paper investigates the development and optimization of control algorithms for mobile robotics, with a keen focus on their implementation in Field-Programmable Gate Arrays (FPGAs). It delves into both classical control approaches such as PID and modern techniques including deep learning, addressing their application in sectors ranging from industrial automation to medical care. The study highlights the practical challenges and advancements in embedding these algorithms into FPGAs, which offer significant benefits for mobile robotics due to their high-speed processing and parallel computation capabilities. Through an analysis of various control strategies, the paper showcases the improvements in robot performance, particularly in navigation and obstacle avoidance. It emphasizes the critical role of FPGAs in enhancing the efficiency and adaptability of control algorithms in dynamic environments. Additionally, the research discusses the difficulties in benchmarking and evaluating the performance of these algorithms in real-world applications, suggesting a need for standardized evaluation criteria. The contribution of this work lies in its comprehensive examination of control algorithms' potential in FPGA-based mobile robotics, offering insights into future research directions for improving robotic autonomy and operational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
