用自适应控制自动调参，让仓库机器人高效协作挖货。
Model-Free Adaptive Parameter Tuning for Efficient Multi-Robot Warehouse Operations
- 基于极值搜索控制，无模型地实时调节机器人调度参数。
- 在动态条件下平均提升5.0%吞吐量，最高达8.4%。
- 适合需要持续优化的智能仓储系统，免去人工调参。
机器人分拣中心将库存存放在密集排列的货架（货箱）区块中。取出深埋货箱需移动阻挡货箱（即‘挖出’操作）。多机器人规划器通过参数化代价函数控制挖出行为，形成策略谱：一端是将阻挡货箱移至其他区块（增加车道机器数），另一端是在区块内混动货箱（避免车道拥堵但延长提取时间）。该谱系上各点对场地拥堵和吞吐量的影响不同。最优运行点取决于具体设施配置，并随站点需求与拥堵模式动态变化，导致离线调参不可行。本文提出一种基于极值搜索控制（ESC）的自适应参数调优框架，可依据实际吞吐量持续调整规划参数。ESC通过正弦扰动信号进行无模型优化，利用扰动与性能变化的相关性估计梯度，对大型分拣中心中存在分钟级延迟和信用分配难题的情况具有鲁棒性。仿真表明，自适应策略在多种工况下优于固定策略：在地图与机器人数量变化下平均提升5.0%吞吐量；在动态运营条件下提升8.4%。本工作消除了手动参数设置，实现实时自适应，为仓储存储操作提供自调优范式。
原文摘要 · Abstract (English)
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.
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