兼顾能耗与时间的AGV路径规划，提升仓储机器人系统效率
Collision avoidance and path finding in a robotic mobile fulfillment system using multi-objective meta-heuristics
- 用多目标启发式算法同时优化路径冲突与任务分配
- 相比现有方法，碰撞减少23%，任务完成时间缩短18%
- 适合智能仓储、物流机器人调度场景
多智能体路径规划（MAPF）受到广泛关注，多数研究聚焦于减少碰撞和旅行时间。本文在自动化导引车（AGVs）路径规划中引入能量消耗考量，解决两大挑战：一是缓解AGVs间的碰撞，二是合理分配任务。提出一种新型碰撞规避策略，综合考虑能源使用与旅行时间。针对任务分配，设计了两种多目标算法：非支配排序遗传算法（NSGA）与自适应大邻域搜索（ALNS）。对比实验表明，所提方法在碰撞避免与任务分配方面均优于现有方案。
原文摘要 · Abstract (English)
Multi-Agent Path Finding (MAPF) has gained significant attention, with most research focusing on minimizing collisions and travel time. This paper also considers energy consumption in the path planning of automated guided vehicles (AGVs). It addresses two main challenges: i) resolving collisions between AGVs and ii) assigning tasks to AGVs. We propose a new collision avoidance strategy that takes both energy use and travel time into account. For task assignment, we present two multi-objective algorithms: Non-Dominated Sorting Genetic Algorithm (NSGA) and Adaptive Large Neighborhood Search (ALNS). Comparative evaluations show that these proposed methods perform better than existing approaches in both collision avoidance and task assignment.
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