Robot-Assisted Shopping Guidance for High-Cost Item Picking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Shopping in large supermarkets can be inefficient and poses safety risks due to the time-consuming nature of navigating multiple shelves and the increased risk of virus transmission in crowded areas, especially during peak times or pandemics.
Innovation Solution
A method and system that utilizes robots to automatically pick up commodities with higher predicted picking costs, while guiding customers to manually retrieve others, optimizing route planning and crowd monitoring to enhance efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If customers manually pick up all commodities themselves, then they can complete the shopping task, but the time spent is excessive and shopping experience deteriorates
Solution Approach 1:
The system segments the shopping task into two parts: high-cost commodities (difficult to find, crowded areas) are picked by robots, while low-cost commodities are picked by customers. This segmentation resolves the contradiction by assigning different tasks to different agents based on their capabilities and the cost-benefit analysis of each commodity.
Solution Approach 2:
The robot acts as an intermediary between the customer and the commodities. Instead of the customer directly searching for all commodities, the robot assists by locating and retrieving specific high-cost commodities, thereby reducing the customer's search time and improving shopping efficiency.
2Object-affected harmful factors
If customers shop during peak times, then they can access all commodities, but crowd congestion increases virus transmission risk
Solution Approach 1:
The robot serves as a mediator that reduces customer exposure to crowded areas by retrieving commodities from high-traffic zones. This intermediary approach maintains shopping safety by minimizing the customer's presence in high-risk areas while still enabling access to all commodities.
Solution Approach 2:
The system replaces the mechanical action of customers physically navigating crowded aisles with an automated robotic system. This substitution reduces human-to-human contact in congested areas, thereby lowering virus transmission risk while maintaining reliable access to commodities.
3Productivity
If robots pick up all commodities, then shopping efficiency improves, but system complexity and cost increase
Solution Approach 1:
The system applies different qualities to different commodities: high-cost commodities receive robot assistance while low-cost commodities are handled by customers. This local differentiation optimizes the system by applying complex robotic operations only where necessary, rather than uniformly across all commodities.
Solution Approach 2:
The system changes the parameter of commodity selection based on picking cost metrics. By dynamically determining which commodities require robot assistance based on calculated picking costs (considering factors like location, crowd density, and time), the system avoids unnecessary complexity while maintaining high efficiency for critical items.
Data Source
AI summary
A shopping guiding with robot assistance. A shopping list (115) of a customer (105) is obtained, wherein the shopping list (115) comprises a plurality of commodities to be picked up. Further, a first set of commodities (125) are determined from the plurality of commodities based on predicted picking up costs for the plurality of commodities, and at least one robot (130) are assigned for automatically picking up the first set of commodities (125). As such, at least one robot (130) may be assigned for picking up commodities with a relative high picking up cost, thereby increasing the efficiency for shopping and decreasing the safety risks.