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，其运动规划与路径优化成为提高生产效率和产品质量的关键因素。本研究聚焦于工业机器人运动规划与路径优化算法，旨在解决传统算法中存在的计算复杂度高、实时性差等问题。通过引入改进的RRT（快速随机树）算法与A*算法相结合的方法，在保证路径全局最优性的前提下，显著提高了搜索效率并降低了计算成本。实验结果表明，该算法能够在复杂环境下实现高效、稳定的路径规划，平均计算时间较传统方法缩短约30%，路径长度优化率达到25%以上。此外，针对多机器人协作场景，提出了一种基于优先级调度的分布式路径规划策略，有效避免了机器人之间的碰撞问题，实现了任务分配的最优化。本研究不仅为工业机器人提供了更加智能高效的运动规划方案，还为相关领域的理论研究和技术发展提供了新的思路与方法，具有重要的学术价值和实际应用前景。
关键词：工业机器人运动规划；路径优化算法；RRT与A*结合；多机器人协作；优先级调度策略
Abstract
With the acceleration of industrial automation, the application of industrial robots in manufacturing has become increasingly widespread, making motion planning and path optimization critical factors for improving production efficiency and product quality. This study focuses on the motion planning and path optimization algorithms for industrial robots, aiming to address issues such as high computational complexity and poor real-time performance in traditional algorithms. By integrating an improved Rapidly-exploring Random Tree (RRT) algorithm with the A* algorithm, this research significantly enhances search efficiency and reduces computational costs while ensuring global optimality of the path. Experimental results demonstrate that this approach achieves efficient and stable path planning in complex environments, with an average reduction in computation time of approximately 30% and a path length optimization rate exceeding 25%. Furthermore, for multi-robot collaboration scenarios, a priority-based distributed path planning strategy is proposed, effectively avoiding collisions between robots and optimizing task allocation. This study not only provides a more intelligent and efficient motion planning solution for industrial robots but also offers new insights and methodologies for theoretical research and technological development in related fields, highlighting its significant academic value and practical application prospects.
Keywords：Industrial Robot Motion Planning; Path Optimization Algorithm; Rrt And A* Combination; Multi-robot Collaboration; Priority Scheduling Strategy
目 录
摘要 I
Abstract II
一、绪论 1
(一) 工业机器人运动规划研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与技术路线 2
二、运动规划基础理论与模型构建 2
(一) 工业机器人运动学建模 2
(二) 环境感知与建图技术 3
(三) 路径表示与评价指标体系 4
三、关键路径优化算法研究 4
(一) 基于几何的路径规划算法 4
(二) 智能优化算法应用 5
(三) 实时避障路径规划策略 6
四、算法实现与实验验证 6
(一) 仿真平台搭建与测试 7
(二) 实际工业场景应用案例 7
(三) 性能分析与结果讨论 8
结 论 10
参考文献 11
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，其运动规划与路径优化成为提高生产效率和产品质量的关键因素。本研究聚焦于工业机器人运动规划与路径优化算法，旨在解决传统算法中存在的计算复杂度高、实时性差等问题。通过引入改进的RRT（快速随机树）算法与A*算法相结合的方法，在保证路径全局最优性的前提下，显著提高了搜索效率并降低了计算成本。实验结果表明，该算法能够在复杂环境下实现高效、稳定的路径规划，平均计算时间较传统方法缩短约30%，路径长度优化率达到25%以上。此外，针对多机器人协作场景，提出了一种基于优先级调度的分布式路径规划策略，有效避免了机器人之间的碰撞问题，实现了任务分配的最优化。本研究不仅为工业机器人提供了更加智能高效的运动规划方案，还为相关领域的理论研究和技术发展提供了新的思路与方法，具有重要的学术价值和实际应用前景。
关键词：工业机器人运动规划；路径优化算法；RRT与A*结合；多机器人协作；优先级调度策略
Abstract
With the acceleration of industrial automation, the application of industrial robots in manufacturing has become increasingly widespread, making motion planning and path optimization critical factors for improving production efficiency and product quality. This study focuses on the motion planning and path optimization algorithms for industrial robots, aiming to address issues such as high computational complexity and poor real-time performance in traditional algorithms. By integrating an improved Rapidly-exploring Random Tree (RRT) algorithm with the A* algorithm, this research significantly enhances search efficiency and reduces computational costs while ensuring global optimality of the path. Experimental results demonstrate that this approach achieves efficient and stable path planning in complex environments, with an average reduction in computation time of approximately 30% and a path length optimization rate exceeding 25%. Furthermore, for multi-robot collaboration scenarios, a priority-based distributed path planning strategy is proposed, effectively avoiding collisions between robots and optimizing task allocation. This study not only provides a more intelligent and efficient motion planning solution for industrial robots but also offers new insights and methodologies for theoretical research and technological development in related fields, highlighting its significant academic value and practical application prospects.
Keywords：Industrial Robot Motion Planning; Path Optimization Algorithm; Rrt And A* Combination; Multi-robot Collaboration; Priority Scheduling Strategy
目 录
摘要 I
Abstract II
一、绪论 1
(一) 工业机器人运动规划研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与技术路线 2
二、运动规划基础理论与模型构建 2
(一) 工业机器人运动学建模 2
(二) 环境感知与建图技术 3
(三) 路径表示与评价指标体系 4
三、关键路径优化算法研究 4
(一) 基于几何的路径规划算法 4
(二) 智能优化算法应用 5
(三) 实时避障路径规划策略 6
四、算法实现与实验验证 6
(一) 仿真平台搭建与测试 7
(二) 实际工业场景应用案例 7
(三) 性能分析与结果讨论 8
结 论 10
参考文献 11
