Methods for finding the perimeter of a place using observed coordinates
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Solution Overview
Problem
Current robotic mapping techniques require significant computational power and often rely on inaccurate sensor statistics, leading to poor performance, and may necessitate additional components like beacons, which increase costs and space requirements.
Innovation Solution
A system for discovering a workspace perimeter using a robot equipped with sensors and processors that obtain radial distances to wall surfaces, forming a map by driving within the workspace and expanding it to include all perimeters, with a communication device for displaying maps and receiving inputs for task association and cleaning settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EKF technique is used to map the environment with feature points, then mapping completeness is improved, but computational power requirement increases significantly
Solution Approach 1:
The patent extracts only the essential perimeter information from the environment by detecting wall surfaces and boundary features, rather than processing all feature points. This selective extraction reduces computational load while maintaining mapping accuracy for perimeter discovery.
Solution Approach 2:
Instead of building a complete map first and then extracting perimeter information, the patent inverts the approach by directly detecting perimeter boundaries through wall surface detection and radial distance measurement, eliminating the need for comprehensive feature point processing.
2Reliability
If additional components like beacons are added to the environment, then mapping reliability is improved, but device complexity and cost increase
Solution Approach 1:
The robotic device uses its own sensors and processing capabilities to detect perimeter boundaries and construct maps, eliminating the need for external beacons or additional environmental components. The system serves itself by leveraging onboard radar, cameras, or LIDAR for autonomous perimeter discovery.
Solution Approach 2:
The robotic device's sensors serve multiple functions: they detect obstacles, measure distances, identify wall surfaces, and construct maps simultaneously, eliminating the need for dedicated beacon components while maintaining mapping reliability through multi-functional sensor utilization.
Data Source
AI summary
Provided is a system including a robot and an application of a communication device. The robot includes a medium storing instructions that when executed by a processor of the robot effectuate operations including: obtaining first data indicative of a relative position of the robot in a workspace; actuating the robot to drive within the workspace to form a map including mapped perimeters that correspond with physical perimeters of the workspace while obtaining second data indicative of movement of the robot; and forming the map of the workspace based on at least some of the first data, wherein the map of the workspace expands as new first data are obtained, until all perimeters of the workspace are included in the map. The application is configured to display information, such as the map, and receive user input.