Lightweight SLAM Navigation Stack for Battery-Powered Wheeled Robots
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Solution Overview
Problem
Robotic devices face challenges with high computational cost, slow response, and high battery power consumption due to the use of Robot Operating System (ROS) or Linux for real-time interactions, which are not suitable for mass production and consumer acceptance, particularly in environments with dynamic obstacles.
Innovation Solution
A method for a battery-operated wheeled device using a primary sensor to capture radial distances, transform data to a device perspective, generate a real-time partial map, and iteratively complete a full map while executing movement paths, utilizing a lightweight real-time navigational stack to enhance computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ROS or Linux is used for real-time interactions, then the robotic device can perform complex functions such as SLAM, path planning, and vision processing, but the computational cost increases, response time slows down, and battery power consumption increases
Solution Approach 1:
The system divides computational tasks into two segments: real-time critical tasks (sensor data acquisition, immediate obstacle detection, basic navigation) executed on a lightweight real-time operating system, and non-real-time tasks (complex SLAM algorithms, detailed path planning, vision processing) executed on ROS/Linux or offloaded to cloud computing. This segmentation allows the robot to maintain real-time responsiveness while reducing overall computational overhead and power consumption.
Solution Approach 2:
The patent introduces a real-time operating system as an intermediary layer between the hardware sensors/actuators and the high-level ROS/Linux applications. This intermediary handles time-critical operations with deterministic response times, while allowing complex but non-time-critical functions to run on less resource-constrained systems, thereby reducing the real-time computational burden and power consumption.
2Adaptability or versatility
If ROS or Linux is used for real-time interactions, then the robotic device can perform complex functions such as SLAM, path planning, and vision processing, but the response time increases and real-time guarantees are lost
Solution Approach 1:
The system divides computational tasks into two segments: real-time critical tasks (sensor data acquisition, immediate obstacle detection, basic navigation) executed on a lightweight real-time operating system, and non-real-time tasks (complex SLAM algorithms, detailed path planning, vision processing) executed on ROS/Linux or offloaded to cloud computing. This segmentation allows the robot to maintain real-time responsiveness while reducing overall computational overhead and power consumption.
Solution Approach 2:
The patent introduces a real-time operating system as an intermediary layer between the hardware sensors/actuators and the high-level ROS/Linux applications. This intermediary handles time-critical operations with deterministic response times, while allowing complex but non-time-critical functions to run on less resource-constrained systems, thereby reducing the real-time computational burden and power consumption.
3Speed
If more processing power is used to compensate for lack of real-time decision making, then real-time capability is improved, but computational cost and response time worsen, particularly when CPU becomes busy
Solution Approach 1:
The patent extracts non-real-time critical computational functions (complex SLAM, detailed path planning, object recognition) from the real-time decision-making loop and executes them separately on ROS/Linux or cloud systems. This extraction allows the real-time operating system to focus solely on time-critical operations with minimal computational resources, achieving fast real-time responses without requiring high computational power.
Solution Approach 2:
The system divides computational tasks into two segments: real-time critical tasks (sensor data acquisition, immediate obstacle detection, basic navigation) executed on a lightweight real-time operating system, and non-real-time tasks (complex SLAM algorithms, detailed path planning, vision processing) executed on ROS/Linux or offloaded to cloud computing. This segmentation allows the robot to maintain real-time responsiveness while reducing overall computational overhead and power consumption.
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.