Robotic cleaner
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Solution Overview
Problem
Existing robotic cleaners lack effective user interaction and localization methods for precise control and navigation within environments, limiting their ability to accurately follow user commands and adapt to specific cleaning tasks.
Innovation Solution
A robotic cleaner system that cooperates with a mobile device, such as a smartphone, to generate and communicate environment maps, allowing the mobile device to localize itself within the map using depth and orientation data, and enable user inputs to define regions and commands through augmented reality elements, which are then translated into precise instructions for the cleaner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional robotic cleaners operate autonomously without mobile device integration, then device complexity is reduced, but user interaction capability and navigation precision deteriorate
Solution Approach 1:
The patent introduces a mobile device as an intermediary between the user and the robotic cleaner. The mobile device receives user inputs, processes them through augmented reality interfaces, and translates them into navigation commands for the cleaner. This mediator approach enhances user interaction capability while keeping the robotic cleaner's internal complexity manageable.
Solution Approach 2:
The system integrates multiple functions across the robotic cleaner and mobile device combination, including autonomous navigation, map generation, augmented reality display, depth sensing, and user input processing. By distributing these functions across multiple components, the system achieves high versatility without concentrating excessive complexity in a single device.
2Measurement precision
If robotic cleaners use basic navigation without depth sensors and augmented reality, then device complexity is lower, but navigation precision and localization accuracy deteriorate
Solution Approach 1:
The patent combines multiple sensing technologies (depth sensors, orientation sensors) with augmented reality display and map generation capabilities into an integrated navigation system. The mobile device merges data from these sensors to achieve precise localization within generated maps, while the augmented reality interface merges virtual elements with the real-world view to enhance positioning accuracy.
Solution Approach 2:
The system replaces traditional mechanical navigation methods with optical and electronic sensing approaches. Depth sensors and orientation sensors substitute for mechanical encoders, while augmented reality and map-based localization replace pure odometry-based navigation, achieving higher precision without mechanical complexity.
3Manufacturing precision
If robotic cleaners lack augmented reality interface, then device complexity is reduced, but user control precision and command execution accuracy deteriorate
Solution Approach 1:
The augmented reality interface creates a virtual copy of the physical environment overlaid with navigational information and control elements. Users interact with this AR copy to issue precise commands, which are then translated into accurate physical actions by the robotic cleaner. This copying approach enables precise control without requiring complex physical interfaces on the cleaner itself.
Data Source
AI summary
A robotic cleaning system may include a robotic cleaner configured to generate a map of an environment and a mobile device configured to communicatively couple to the robotic cleaner, the robotic cleaner configured to communicate the map to the mobile device. The mobile device may include a camera configured to generate an image of the environment, the image comprising a plurality of pixels, a display configured to display the image and to receive a user input while displaying the image, the user input being associated with one or more of the plurality of pixels, a depth sensor configured to generate depth data that is associated with each pixel of the image, an orientation sensor configured to generate orientation data that is associated with each pixel of the image, and a mobile controller configured to localize the mobile device within the map using the depth data and the orientation data.