How Do Humans Adjust Their Motion Patterns in Mobile Robots Populated Retail Environments?

Luo, Yue; Chen, Yuhao; Yerebakan, Mustafa Ozkan; Hao, Shuai; Grimaldi, Nicolas; Yang, Chizhao; Hayes, Read; Hu, Boyi

doi:10.1109/ichms56717.2022.9980607

Cited by 3 publications

(2 citation statements)

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“…Sharing space with mobile robots in close proximity induce in individuals physical responses to some extent, as demonstrated by [4]. Luo et al [5] in particular, with a biomechanics analysis, showed that shoppers spent more time walking between shelves with a reduced walking speed and have deteriorated walking stability with the mobile robot in the same space. Robots should not interfere in any way with the customer shopping journey, hence knowing and predicting the customer behavior is fundamental.…”

Section: Introductionmentioning

confidence: 97%

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Retail Robot Navigation: A Shopper Behavior-Centric Approach to Path Planning

Galdelli,

Pietrini,

Mancini

et al. 2024

IEEE Access

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In the ever-evolving landscape of retail, understanding shopper behavior is pivotal for optimizing sales and effectively managing product availability and placement. This study explores the integration of autonomous mobile robots into the shelf inspection process, leveraging advancements in automation, information, and robotics technology. Performing mapping tasks, these robots incorporate insights into customer behavior by exploiting various sources of behavioral data, including trajectories and product interactions. Motivated by the complex and dynamic nature of modern stores, our research seeks to bridge the gap in retail inventory management. Our unique contribution lies in the development of a novel path planning method for robots, specifically tailored for an automated inventory management system. By focusing on customer trajectories and product interactions, we aim to enhance the arrangement and positioning of products within retail spaces. Our research is motivated by the need to address the challenges faced by retailers in optimizing store layouts and product placements. The proposed strategy utilizes a heatmap and a vision-based system to analyze spatial and temporal patterns of shopper behavior. This information is then employed to optimize robot navigation in both highly and less-visited areas. Trajectories and product interactions data from real store installations were utilized in simulation, providing valuable insights into optimal planning for mobile robots to visit Points of Interest (PoI). The active shopping cart tracking system generated heatmaps, while a vision-based system collected shopper-products interactions data. Subsequently, our approach was deployed on a real retail robot for inventory management, and the path planning source code was released. Our findings demonstrate that the path planned by our approach not only avoids collisions with static store sections but also optimizes paths in areas with significant customer-shelf activity.

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Section: Introductionmentioning

confidence: 97%

“…FIGURE 4: Final Solution Heatmap-TSP (1) considering distance matrix as in (4) in the problem formulation and with the addition of(5) …”

mentioning

confidence: 99%