What it does
Intelligent shopping guide robots analyze customer preferences to recommend products, navigate and guide in large shopping malls, and allow customers to query product information through voice or touch screens.
Your inspiration
I decided to solve the problems of difficult information search, time-consuming path planning, low efficiency of guide services, and insufficient personalization in traditional shopping because I observed that in large retail environments, customers often feel confused and inconvenient, and traditional manual guides also face challenges such as high labor costs, limited service time, and difficulty in providing personalized advice to thousands of people. The idea for the solution stems from an interest in artificial intelligence, the Internet of Things, and robotics technology, as well as a simple belief that technology should make life
How it works
Intelligent shopping guide robots work collaboratively by integrating multiple technologies. It utilizes sensors such as LiDAR and cameras to sense the environment and accurately locate itself (SLAM technology), ensuring safe navigation. Users can interact through voice or touch screen, and robots use NLP to understand user intentions. Afterwards, by combining database queries, machine learning recommendation algorithms, and knowledge graphs, robots can provide accurate product information, location guidance, and personalized recommendations. The path planning algorithm drives the robot to reach the designated location. The data generated throughout the process will be uploaded to the cloud for analysis, used to optimize robot performance and assist merchants in decision-making. In short, it is an automated shopping guide system that integrates perception, interaction, intelligent decision-making, navigation, and continuous learning.
Design process
The design of this intelligent shopping guide robot began with the observation of pain points such as difficult navigation for customers, inconvenient information acquisition, high labor costs, and low efficiency for merchants in large retail environments. The core goal is to provide real-time, accurate, and personalized shopping guidance and improve merchant efficiency. The design process followed clear steps: first, a requirements analysis was conducted to clarify the needs of users (navigation, queries, recommendations, etc.) and merchants (data collection, efficiency improvement), and corresponding functional modules were planned. Subsequently, technology selection was carried out, selecting mature hardware (such as wheeled chassis, LiDAR, NVIDIA Jetson platform, etc.) and software (ROS, SLAM library, cloud voice services, etc.), and a layered architecture was designed. The prototype production went through two main versions: the first version (V1) focused on verifying the core navigation and basic interaction functions, but had obvious limitations; The second version (V2) has been upgraded in hardware and software, improving environmental adaptability and interaction capabilities, but there are still issues such as unstable navigation and poor speech recognition performanc
How it is different
The unique design of this intelligent shopping guide robot lies in its deep personalized recommendation, multimodal barrier free interaction, edge cloud collaborative architecture, and integrated merchant empowerment module. It provides more accurate product recommendations by combining user history with real-time context, such as region, line of sight, and sensory recognition. It supports multimodal fusion, including advanced gesture recognition and lip language assistance, enabling accessible interaction for a wider range of people, such as hearing-impaired individuals and non-native language users. Its edge cloud collaborative architecture ensures real-time and stable localization decision-making, while utilizing the cloud for deep analysis and optimization.
Future plans
nothing无
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