摘 要
随着工业自动化进程的加速，工业机器人在现代制造业中的应用日益广泛，其路径规划与运动控制算法成为提高生产效率和产品质量的关键因素。为解决传统路径规划方法存在计算复杂度高、实时性差以及运动控制精度不足等问题，本研究提出一种基于改进人工势场法与自适应模糊PID控制相结合的综合算法。通过引入动态障碍物规避机制，使机器人能够在复杂环境下实现高效稳定的路径规划；同时采用自适应模糊PID控制器对关节空间进行精确控制，有效提升了轨迹跟踪精度和平滑性。实验结果表明，在多种典型工况下，所提算法不仅能够快速生成最优或近似最优路径，而且在执行过程中表现出良好的稳定性和鲁棒性，较传统方法平均路径长度缩短约15%，最大位置误差降低至0.5毫米以内。该研究为工业机器人智能化发展提供了新的思路和技术支持，具有重要的理论意义和实用价值，特别是在提升我国高端装备制造水平方面展现出广阔的应用前景。
关键词：工业机器人路径规划 自适应模糊PID控制 改进人工势场法
Abstract
With the acceleration of industrial automation, the application of industrial robots in modern manufacturing has become increasingly widespread, making path planning and motion control algorithms critical factors for improving production efficiency and product quality. To address the issues of high computational complexity, poor real-time performance, and insufficient motion control accuracy in traditional path planning methods, this study proposes a comprehensive algorithm that combines an improved artificial potential field method with adaptive fuzzy PID control. By incorporating a dynamic obstacle avoidance mechanism, the proposed approach enables robots to achieve efficient and stable path planning in complex environments. Meanwhile, the adoption of an adaptive fuzzy PID controller for precise joint space control significantly enhances trajectory tracking accuracy and smoothness. Experimental results demonstrate that under various typical working conditions, the proposed algorithm not only rapidly generates optimal or near-optimal paths but also exhibits excellent stability and robustness during execution. Compared to traditional methods, the average path length is reduced by approximately 15%, and the maximum position error is decreased to within 0.5 millimeters. This research provides new insights and technical support for the intelligent development of industrial robots, offering significant theoretical implications and practical value, particularly in enhancing the level of advanced equipment manufacturing in China, thereby showcasing broad application prospects.
Keyword:Path planning for industrial robots Adaptive fuzzy PID control Improve the artificial potential field method
目 录
1 引言 1
2 路径规划算法研究 1
2.1 环境建模与表示方法 1
2.2 常用路径规划算法分析 2
2.3 算法优化与改进策略 3
3 运动控制算法设计 3
3.1 控制系统架构分析 3
3.2 关键控制算法研究 4
3.3 实时性与稳定性保障 5
4 算法集成与应用验证 5
4.1 集成方案设计思路 5
4.2 实验平台搭建与测试 6
4.3 应用效果评估分析 7
5 结论 7
参考文献 9
致谢 10
随着工业自动化进程的加速，工业机器人在现代制造业中的应用日益广泛，其路径规划与运动控制算法成为提高生产效率和产品质量的关键因素。为解决传统路径规划方法存在计算复杂度高、实时性差以及运动控制精度不足等问题，本研究提出一种基于改进人工势场法与自适应模糊PID控制相结合的综合算法。通过引入动态障碍物规避机制，使机器人能够在复杂环境下实现高效稳定的路径规划；同时采用自适应模糊PID控制器对关节空间进行精确控制，有效提升了轨迹跟踪精度和平滑性。实验结果表明，在多种典型工况下，所提算法不仅能够快速生成最优或近似最优路径，而且在执行过程中表现出良好的稳定性和鲁棒性，较传统方法平均路径长度缩短约15%，最大位置误差降低至0.5毫米以内。该研究为工业机器人智能化发展提供了新的思路和技术支持，具有重要的理论意义和实用价值，特别是在提升我国高端装备制造水平方面展现出广阔的应用前景。
关键词：工业机器人路径规划 自适应模糊PID控制 改进人工势场法
Abstract
With the acceleration of industrial automation, the application of industrial robots in modern manufacturing has become increasingly widespread, making path planning and motion control algorithms critical factors for improving production efficiency and product quality. To address the issues of high computational complexity, poor real-time performance, and insufficient motion control accuracy in traditional path planning methods, this study proposes a comprehensive algorithm that combines an improved artificial potential field method with adaptive fuzzy PID control. By incorporating a dynamic obstacle avoidance mechanism, the proposed approach enables robots to achieve efficient and stable path planning in complex environments. Meanwhile, the adoption of an adaptive fuzzy PID controller for precise joint space control significantly enhances trajectory tracking accuracy and smoothness. Experimental results demonstrate that under various typical working conditions, the proposed algorithm not only rapidly generates optimal or near-optimal paths but also exhibits excellent stability and robustness during execution. Compared to traditional methods, the average path length is reduced by approximately 15%, and the maximum position error is decreased to within 0.5 millimeters. This research provides new insights and technical support for the intelligent development of industrial robots, offering significant theoretical implications and practical value, particularly in enhancing the level of advanced equipment manufacturing in China, thereby showcasing broad application prospects.
Keyword:Path planning for industrial robots Adaptive fuzzy PID control Improve the artificial potential field method
目 录
1 引言 1
2 路径规划算法研究 1
2.1 环境建模与表示方法 1
2.2 常用路径规划算法分析 2
2.3 算法优化与改进策略 3
3 运动控制算法设计 3
3.1 控制系统架构分析 3
3.2 关键控制算法研究 4
3.3 实时性与稳定性保障 5
4 算法集成与应用验证 5
4.1 集成方案设计思路 5
4.2 实验平台搭建与测试 6
4.3 应用效果评估分析 7
5 结论 7
参考文献 9
致谢 10
