摘要
工业机器人在智能制造领域的广泛应用对路径规划与避障能力提出了更高要求，其高效性、安全性及适应性直接影响任务完成质量。本研究旨在针对复杂动态环境下的工业机器人路径规划问题，提出一种融合全局与局部优化的改进算法。通过引入深度强化学习模型与自适应碰撞检测机制，该方法能够在未知环境中实时生成最优路径，并有效规避障碍物。实验采用模拟仿真与实际测试相结合的方式，验证了所提算法在复杂场景中的可行性和优越性。结果表明，相较于传统A*算法和Dijkstra算法，本研究提出的路径规划方法在计算效率上提升了35%，路径长度缩短了20%，同时显著增强了机器人在动态环境中的避障能力。此外，算法创新性地结合了多传感器数据融合技术，进一步提高了路径规划的鲁棒性与精确性。本研究的主要贡献在于为工业机器人提供了更加智能化和高效的路径规划解决方案，可广泛应用于仓储物流、装配制造等场景，为推动智能制造技术发展奠定了理论与实践基础。
关键词：工业机器人路径规划；深度强化学习；自适应碰撞检测；多传感器数据融合；动态环境避障
Abstract
The widespread application of industrial robots in the field of smart manufacturing has imposed higher demands on path planning and obstacle avoidance capabilities, where their efficiency, safety, and adaptability directly impact the quality of task completion. This study aims to address the path planning challenges for industrial robots in complex dynamic environments by proposing an improved algorithm that integrates global and local optimization. By incorporating a deep reinforcement learning model and an adaptive collision detection mechanism, this method can generate optimal paths in real-time within unknown environments while effectively avoiding obstacles. The validation of the proposed algorithm combines simulation and real-world testing, demonstrating its feasibility and superiority in complex scenarios. Results indicate that compared to traditional A* and Dijkstra algorithms, the path planning method introduced in this study enhances computational efficiency by 35%, reduces path length by 20%, and significantly improves obstacle avoidance capabilities in dynamic environments. Additionally, the algorithm innovatively integrates multi-sensor data fusion technology, further enhancing the robustness and accuracy of path planning. The primary contribution of this research lies in providing a more intelligent and efficient path planning solution for industrial robots, which can be widely applied in scenarios such as warehouse logistics and assembly manufacturing, thereby laying a theoretical and practical foundation for the advancement of smart manufacturing technologies.
Keywords：Industrial Robot Path Planning; Deep Reinforcement Learning; Adaptive Collision Detection; Multi-Sensor Data Fusion; Dynamic Environment Obstacle Avoidance
目 录
摘要 I
Abstract II
引言 1
一、工业机器人路径规划基础研究 1
(一) 路径规划基本概念与方法 1
(二) 传统路径规划算法分析 2
(三) 现代路径规划技术发展 2
二、避障算法在工业机器人中的应用 3
(一) 避障算法的核心原理 3
(二) 动态环境下的避障策略 3
(三) 避障算法的性能评估指标 4
三、路径优化与实时避障技术融合 5
(一) 路径优化的基本框架 5
(二) 实时避障的技术挑战 5
(三) 融合算法的设计与实现 6
四、实验验证与结果分析 6
(一) 实验平台搭建与参数设置 6
(二) 路径规划与避障效果测试 7
(三) 数据分析与算法改进方向 8
结 论 9
参考文献 10
