用蚁群算法优化物流机器人路径，兼顾时间、任务和转弯平滑性。
Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints
- 基于蚁群算法，综合考虑时间窗、任务顺序与运动平滑性。
- 路径长度、任务完成时间、转弯次数均优于传统方法。
- 适合动态复杂环境下的物流机器人路径规划使用。
随着物流行业的快速发展，物流车辆的路径规划日益复杂，需同时考虑时间窗、任务顺序和运动平滑性等多重约束。传统路径规划方法难以高效平衡这些相互冲突的需求。本文提出一种基于蚁群优化（ACO）算法的路径规划方法，优化路径长度、任务完成时间、转弯次数和运动平滑性等关键性能指标，以实现物流车辆高效且实用的路径规划。实验结果表明，该方法在效率和适应性方面均优于传统方法。本研究为物流车辆路径规划提供了稳健解决方案，具有在动态受限环境中的实际应用潜力。
原文摘要 · Abstract (English)
With the rapid development of the logistics industry, the path planning of logistics vehicles has become increasingly complex, requiring consideration of multiple constraints such as time windows, task sequencing, and motion smoothness. Traditional path planning methods often struggle to balance these competing demands efficiently. In this paper, we propose a path planning technique based on the Ant Colony Optimization (ACO) algorithm to address these challenges. The proposed method optimizes key performance metrics, including path length, task completion time, turning counts, and motion smoothness, to ensure efficient and practical route planning for logistics vehicles. Experimental results demonstrate that the ACO-based approach outperforms traditional methods in terms of both efficiency and adaptability. This study provides a robust solution for logistics vehicle path planning, offering significant potential for real-world applications in dynamic and constrained environments.
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