Microcontroller Real-Time SLAM for Fast-Response Cleaning Robots
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Solution Overview
Problem
Autonomous robots face challenges with high computational costs and slow response times due to the use of ROS or Linux for real-time SLAM and path planning, leading to inefficient battery usage and poor user experience, especially in mass production and consumer environments.
Innovation Solution
A Light Weight Real Time SLAM Navigational Stack that utilizes a single microcontroller to execute SLAM, path planning, obstacle avoidance, and cleaning operations concurrently, with a scheduler prioritizing tasks and preempting lower priority ones, allowing for faster boot-up times and reduced battery consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ROS or Linux is used to run SLAM and path planning applications, then the robot can perform complex autonomous navigation tasks, but the computational cost increases and response time becomes slow
Solution Approach 1:
The patent extracts the real-time control functionality from the heavy ROS/Linux operating system and implements it in a lightweight microcontroller. The microcontroller handles time-critical SLAM and path planning tasks independently, while the PC provides high-level application support. This separation allows the robot to maintain autonomous navigation capabilities while achieving real-time response performance.
Solution Approach 2:
The patent introduces a microcontroller as an intermediary between the PC running ROS/Linux and the robot's motor controllers. The microcontroller receives high-level commands from the PC and executes real-time navigation decisions locally, acting as a mediator that enables both complex autonomous tasks and fast response times without requiring the full ROS/Linux stack for real-time operations.
2Adaptability or versatility
If ROS or Linux is used for real-time decision making, then the robot can execute complex navigation algorithms, but battery power consumption increases
Solution Approach 1:
The patent extracts the real-time decision-making functionality from the power-intensive ROS/Linux environment and relocates it to a low-power microcontroller. The microcontroller executes SLAM and path planning algorithms with minimal power consumption, while the PC remains in sleep mode or performs only lightweight communication tasks, dramatically reducing overall battery usage while maintaining navigation algorithm capability.
Solution Approach 2:
The patent employs a simple, low-cost microcontroller for real-time navigation tasks instead of relying on the expensive and power-hungry ROS/Linux stack. The microcontroller provides sufficient computational capability for real-time SLAM and path planning at a fraction of the power cost, enabling extended operational duration on battery power.
3Adaptability or versatility
If a PC is used for SLAM and path planning, then the robot can perform advanced autonomous functions, but boot-up time increases
Solution Approach 1:
The patent extracts the essential autonomous navigation functionality from the slow-booting PC environment and implements it in a microcontroller that boots up almost instantly. The microcontroller handles all time-critical navigation tasks independently, allowing the robot to become operational immediately without waiting for a PC to boot and initialize complex operating systems.
Solution Approach 2:
The microcontroller is pre-programmed with all necessary SLAM and path planning algorithms, enabling it to execute autonomous navigation tasks independently without requiring a PC to boot up or initialize. The robot's core navigation brain is self-contained and ready for immediate operation, eliminating boot-up delays entirely.
Data Source
AI summary
Some aspects include a method for operating a cleaning robot, including: capturing LIDAR data; generating a first iteration of a map of the environment in real time; capturing sensor data from different positions within the environment; capturing movement data indicative of movement of the cleaning robot; aligning and integrating newly captured LIDAR data with previously captured LIDAR data at overlapping points; generating additional iterations of the map based on the newly captured LIDAR data and at least some of the newly captured sensor data; localizing the cleaning robot; planning a path of the cleaning robot; and actuating the cleaning robot to drive along a trajectory that follows along the planned path by providing pulses to one or more electric motors of wheels of the cleaning robot.