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<p>In warehouse logistics, manual person-to-goods (PTG) order picking often limits scalability due to travel time, idle time, and errors. We present and experimentally evaluate a modular human-robot collaborative picking system that integrates a Warehouse Management System (WMS), a Task Scheduling Engine (TSE), and a fleet of Autonomous Mobile Robots (AMRs). The AMRs autonomously handle long-distance navigation and transport, while human pickers focus on picking tasks, enabling deployment without major infrastructure changes. The experimental evaluation took place in a real warehouse, with 20 novice pickers and 2 AMRs, with identical order sets for both AMR-assisted and manual picking. The results show that the AMR-assisted system reduced average order cycle time by 25% (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{253 \pm 28}\)</EquationSource> </InlineEquation> s vs. <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{316 \pm 40}\)</EquationSource> </InlineEquation> s) and picker idle time by 60% (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\varvec{9.8 \pm 4.1}\)</EquationSource> </InlineEquation> s vs. <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\varvec{24.6 \pm 7.2}\)</EquationSource> </InlineEquation> s), leading to a 46% (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\varvec{162 \pm 18}\)</EquationSource> </InlineEquation> vs. <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\varvec{111 \pm 14}\)</EquationSource> </InlineEquation> [lines h<InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(^{\varvec{-1}}\)</EquationSource> </InlineEquation>]) increase in throughput. The error rate decreased from <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\varvec{1.4\%}\)</EquationSource> </InlineEquation> to <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\varvec{0.6\%}\)</EquationSource> </InlineEquation>, and the perceived workload, measured by the NASA Task Load Index (NASA-TLX) score, dropped by 27% (<InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(\varvec{44.7 \pm 6.5}\)</EquationSource> </InlineEquation> vs. <InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(\varvec{61.9 \pm 7.8}\)</EquationSource> </InlineEquation>). These results indicate that human-AMR collaboration can meaningfully improve productivity, accuracy, and worker experience in PTG operations while preserving flexibility. The system’s modularity and scalability make it suitable for small and medium-sized warehouses seeking performance gains without extensive retrofitting.</p>

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Human-robot collaborative picking system for agile warehouses

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Alexios Papadimitriou,
  • Dimitrios Folinas,
  • Ioannis Kostavelis

摘要

In warehouse logistics, manual person-to-goods (PTG) order picking often limits scalability due to travel time, idle time, and errors. We present and experimentally evaluate a modular human-robot collaborative picking system that integrates a Warehouse Management System (WMS), a Task Scheduling Engine (TSE), and a fleet of Autonomous Mobile Robots (AMRs). The AMRs autonomously handle long-distance navigation and transport, while human pickers focus on picking tasks, enabling deployment without major infrastructure changes. The experimental evaluation took place in a real warehouse, with 20 novice pickers and 2 AMRs, with identical order sets for both AMR-assisted and manual picking. The results show that the AMR-assisted system reduced average order cycle time by 25% ( \(\varvec{253 \pm 28}\) s vs. \(\varvec{316 \pm 40}\) s) and picker idle time by 60% ( \(\varvec{9.8 \pm 4.1}\) s vs. \(\varvec{24.6 \pm 7.2}\) s), leading to a 46% ( \(\varvec{162 \pm 18}\) vs. \(\varvec{111 \pm 14}\) [lines h \(^{\varvec{-1}}\) ]) increase in throughput. The error rate decreased from \(\varvec{1.4\%}\) to \(\varvec{0.6\%}\) , and the perceived workload, measured by the NASA Task Load Index (NASA-TLX) score, dropped by 27% ( \(\varvec{44.7 \pm 6.5}\) vs. \(\varvec{61.9 \pm 7.8}\) ). These results indicate that human-AMR collaboration can meaningfully improve productivity, accuracy, and worker experience in PTG operations while preserving flexibility. The system’s modularity and scalability make it suitable for small and medium-sized warehouses seeking performance gains without extensive retrofitting.