用于高效多机器人仓库作业的无模型自适应参数调优
Model-Free Adaptive Parameter Tuning for Efficient Multi-Robot Warehouse Operations
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- Amazon Robotics（亚马逊机器人公司）
- University of Maryland（马里兰大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对多机器人仓库作业参数需随工况动态调整的问题，提出基于极值寻优控制（ESC）的无模型自适应参数调优框架，经仿真验证可平均提升吞吐量5.0%，动态工况下提升8.4%，实现自调优。
中文摘要 AI 辅助
机器人履约中心（FC）将库存存放在密集区块排列的货架（pod）上，要取出埋藏在区块深处的目标货架，需将阻碍的货架移开（即“挖取”）。多机器人规划器使用参数化代价函数控制挖取行为，产生一系列策略：一端是将阻碍货架送往其他区块（在行驶车道使用更多机器人），另一端是在区块内调整货架（避免车道拥堵但增加提取时间）。该序列上的每个点对场地拥堵和吞吐量有不同的后续影响，最优操作点取决于特定设施配置，并随站点需求变化、拥堵模式等操作条件变化，使离线调优不切实际。我们提出一种基于极值寻优控制（ESC）的自适应参数调优框架，该框架根据测得的吞吐量持续调整规划器参数。ESC通过正弦抖动信号扰动参数，并将扰动与性能变化相关联以估计梯度，从而执行无模型优化，使其对大型FC操作中固有的数分钟延迟效应和信用分配挑战具有鲁棒性。仿真研究表明，自适应策略在多种条件下优于固定策略：在地图和机器人车队规模变化的情况下，吞吐量平均提升5.0%；在动态操作条件下，吞吐量提升8.4%。本研究消除了手动参数配置，实现了实时自适应，为FC存储作业提供了自调优范式。
英文摘要
Robotic Fulfillment Centers (FCs) store inventory on shelves (pods) arranged in dense blocks. Retrieving a target pod that is buried deep in a block requires moving obstructing pods out of the way (i.e., digout). Multi-robot planners use parameterized cost functions to control digout behavior, producing a spectrum of strategies: at one extreme, obstructing pods are sent to other blocks (using more robots in travel lanes); at the other, pods are shuffled within the block (avoiding lane congestion but increasing extraction time). Each point on this spectrum has different downstream consequences for floor congestion and throughput. The optimal operating point depends on the specific facility configuration and shifts with operational conditions such as varying station demand and congestion patterns, making offline tuning impractical. We present an adaptive parameter tuning framework based on Extremum Seeking Control (ESC) that continuously adjusts planner parameters in response to measured throughput. ESC performs model-free optimization by perturbing parameters with sinusoidal dither signals and correlating perturbations with performance changes to estimate gradients, making it robust to the multi-minute delayed effects and credit assignment challenges inherent in large FC operations. Simulation studies demonstrate that the adaptive policy improves upon fixed policies across several conditions. We observe an improvement in throughput by an average of 5.0% across map and robot fleet size variations, and by 8.4% under dynamic operating conditions. This work eliminates manual parameter provisioning and enables real-time adaptation, providing a self-tuning paradigm for FC storage operations.
