用优化方法自动设计低成本机器人分拣布局，兼顾效率与可行性。
Budget-optimal multi-robot layout design for box sorting
- 将布局设计转化为网络流约束下的子图优化问题
- 在不同网格分辨率下降低内存占用，优于启发式算法
- 适合物流分拣系统部署，提升机器人布局自动化水平
机器人系统被广泛应用于物流行业以提升运营效率，但机器人工作区的设计仍依赖复杂且手动的过程，限制了系统对需求变化的适应性。本文提出一种计算框架，通过在地板网格上选择性放置固定机器人，以最小化硬件预算来完成从指定输入和输出位置分拣包裹的任务。在保证运动可行性的同时最小化成本，是一个具有非凸运动约束的组合难题。我们提出基于优化的新方法，将布局规划建模为满足网络流约束的子图优化问题。核心思路是预先计算运动可达性图，从而在该基础图上提取最优布局，避免在优化中直接处理运动约束。通过简单的任务分配与路径规划技术验证了方案的运动可行性。我们在多种网格分辨率和输出数量的问题上进行了基准测试，结果表明该算法在内存效率上优于启发式搜索算法。
原文摘要 · Abstract (English)
Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system's flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computational framework to generate a budget-minimizing layout by selectively placing stationary robots on a floor grid to sort packages from given input and output locations. Finding a good layout that minimizes the hardware budget while ensuring motion feasibility is a challenging combinatorial problem with nonconvex motion constraints. We propose a new optimization-based approach that models layout planning as a subgraph optimization problem subject to network flow constraints. Our core insight is to abstract away motion constraints from the layout optimization by precomputing a kinematic reachability graph and then extract the optimal layout on this ground graph. We validate the motion feasibility of our approach by proposing a simple task assignment and motion planning technique. We benchmark our algorithm on problems with various grid resolutions and number of outputs and show improvements in memory efficiency over a heuristic search algorithm.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。