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.

翻译：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。研究涵盖PID等经典控制方法及深度学习等现代技术，探讨了从工业自动化到医疗护理等领域的应用。通过分析多种控制策略，本文展示了在将算法嵌入具有高速处理与并行计算能力的FPGA时面临的实际挑战与技术进展，这些特性为移动机器人带来了显著优势。研究重点阐述了FPGA在提升动态环境中控制算法效率与自适应性的关键作用，并指出算法在导航与避障方面对机器人性能的改进。此外，本文讨论了在真实场景中基准测试与评估这些算法性能的难点，提出需要建立标准化评估标准。本研究的价值在于全面审视了控制算法在基于FPGA的移动机器人中的应用潜力，为提升机器人自主性与运行效率的未来研究方向提供了见解。