MCU-Based Wheeled Robot SLAM for Low-Power Real-Time Mapping
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Solution Overview
Problem
Existing robotic devices face challenges with high computational costs, slow response times, and high battery power consumption due to their reliance on technologies like Robot Operating System (ROS) or Linux for Simultaneous Localization and Mapping (SLAM) and other AI applications.
Innovation Solution
A battery-operated wheeled device equipped with electric motors and sensors that captures radial distances to objects, transforms this data into a bird's eye view, and generates a partial map in real-time, using a processor to iteratively complete a full map as new sensor data is captured.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ROS or Linux is used for SLAM and AI applications, then functionality and intelligence are improved, but computational cost and power consumption increase
Solution Approach 1:
The patent extracts the SLAM algorithm from the ROS/Linux environment and implements it directly on a microcontroller unit (MCU). This removes the heavy operating system layer and associated computational overhead, retaining the core SLAM functionality while dramatically reducing power consumption and computational requirements.
Solution Approach 2:
The patent replaces expensive, power-intensive computing platforms (ROS/Linux on CPUs) with a simpler, more efficient MCU-based system. This substitution uses lighter computational resources that consume less power while still delivering the necessary SLAM functionality for autonomous navigation.
2Adaptability or versatility
If ROS or Linux is used for SLAM and AI applications, then functionality and intelligence are improved, but computational intensity increases
Solution Approach 1:
The patent extracts the essential SLAM computational core from the complex ROS/Linux ecosystem and implements it on a microcontroller. This extraction eliminates unnecessary computational layers while preserving the fundamental localization and mapping capabilities.
Solution Approach 2:
The patent replaces the mechanical/software complexity of ROS/Linux with a streamlined MCU-based processing architecture. This substitution simplifies the computational system while maintaining the ability to perform SLAM and basic AI functions.
3Adaptability or versatility
If ROS or Linux is used for SLAM and AI applications, then functionality and intelligence are improved, but response time decreases
Solution Approach 1:
The patent extracts the SLAM processing from the multi-layered ROS/Linux architecture to a direct MCU implementation. This removal of intermediate layers eliminates processing delays and enables real-time response to environmental changes and navigation decisions.
Data Source
AI summary
Some aspects include a method for operating a wheeled device, including: capturing, by a primary sensor coupled to the wheeled device, primary sensor data indicative of a plurality of radial distances to objects; transforming, by a processor of the wheeled device, the plurality of radial distances from a perspective of the primary sensor to a perspective of the wheeled device; generating, by the processor, a partial map of visible areas in real-time at a first position of the wheeled device based on the primary sensor data and some secondary sensor data, wherein: the partial map is a bird's eye view; and the processor iteratively completes a full map of the environment based on new sensor data captured by sensors as the wheeled device performs work within the environment and new areas become visible to the sensors; and executing, by the wheeled device, a movement path to a second position.