摘 要
随着工业自动化进程的加速，工业机器人在制造业中的应用日益广泛，其路径规划作为提高生产效率和作业精度的关键技术备受关注。该方法通过引入自适应调整机制，在保证全局搜索能力的同时增强局部搜索精度，有效解决了静态环境下的路径规划问题。进一步地，考虑到实际工业场景中动态障碍物的存在，构建了基于深度强化学习的动态避障模型，实现了对未知环境的快速响应。实验结果表明，所提出的混合算法较传统算法在寻优速度上提高了约30%，路径长度缩短了15%左右，而动态避障模型能够准确识别并规避障碍物，确保机器人安全稳定运行。本研究不仅为工业机器人路径规划提供了新的解决方案，而且对于推动智能制造技术的发展具有重要意义，创新性地将多种智能算法有机结合，拓展了工业机器人的应用场景，为实现高效、智能的工业生产奠定了坚实基础。
关键词：工业机器人；路径规划；混合智能优化算法
Abstract
With the acceleration of the process of industrial automation, the application of industrial robots in the manufacturing industry is increasingly widespread, and its path planning as a key technology to improve production efficiency and operation accuracy has attracted much attention. By introducing an adaptive adjustment mechanism, this method not only guarantees the global search ability but also enhances the local search precision, and effectively solves the problem of path planning in static environment. Further, considering the existence of dynamic obstacles in the actual industrial scene, a dynamic obstacle avoidance model based on deep reinforcement learning is constructed to realize the rapid response to the unknown environment. The experimental results show that compared with the traditional algorithm, the proposed hybrid algorithm can improve the optimization speed by about 30% and shorten the path length by about 15%. The dynamic obstacle avoidance model can accurately identify and avoid obstacles to ensure the safe and stable operation of the robot. This research not only provides a new solution for the path planning of industrial robots, but also has great significance for promoting the development of intelligent manufacturing technology. It innovatively combines a variety of intelligent algorithms organically, expands the application scenarios of industrial robots, and lays a solid foundation for realizing efficient and intelligent industrial production.
Key words: industrial robot; Path planning; Hybrid intelligent optimization algorithm
目 录
中文摘要 I
英文摘要 II
目 录 III
引 言 1
第1章、路径规划算法的理论基础 2
1.1、常用路径规划算法概述 2
1.2、算法性能评价指标体系 2
1.3、现有算法存在的问题分析 3
第2章、路径规划算法优化策略 5
2.1、基于智能算法的优化方法 5
2.2、多目标优化在路径规划中的应用 5
2.3、算法实时性与鲁棒性的提升 6
第3章、优化算法的应用实践 7
3.1、典型工业场景的应用案例 7
3.2、实际应用中的挑战与应对 7
3.3、应用效果评估与展望 7
结 论 9
参考文献 10
