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 method for navigating and mapping a workspace using a robotic device equipped with both exteroceptive and proprioceptive sensors, allowing it to switch between modes of operation based on sensor availability, and utilizing depth measurements from imaging devices to create a spatial map with reduced computational costs and minimal additional components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EKF technique is used to estimate pose and position with large state vector and covariance matrix, then mapping accuracy is improved, but computational power required increases significantly
Solution Approach 1:
The patent extracts and removes unnecessary components from the EKF system - specifically eliminating the covariance matrix and reducing the state vector to only essential elements (robot position, orientation, and range to nearest wall). This extraction maintains sufficient mapping accuracy while dramatically reducing computational power requirements.
Solution Approach 2:
Instead of maintaining a complete global map with all features, the patent applies local quality by only tracking locally relevant information - the nearest wall and immediate surroundings. This localized approach provides sufficient accuracy for navigation tasks while reducing computational burden.
2Loss of information
If distance sensor rotates 360-degrees to measure distances to objects, then complete environment mapping is achieved, but robotic device must rotate before performing work
Solution Approach 1:
The patent applies preliminary action by having the robot perform a single 360-degree rotation at the beginning to establish initial perimeter coordinates and create a coarse map. Subsequent operations use incremental updates rather than complete re-mapping, reducing total time loss.
Solution Approach 2:
The patent ensures continuity of useful action by enabling the robot to perform work tasks simultaneously with incremental map updates. Rather than completing all mapping before work, the system continuously updates the perimeter map during operation, eliminating idle time between mapping and work phases.
3Loss of information
If occupancy map tracks all points including perimeters, empty spaces, and spaces beyond perimeters, then comprehensive environment representation is achieved, but computational costs increase
Solution Approach 1:
The patent extracts only the essential perimeter coordinates from the complete occupancy map, removing unnecessary tracking of empty spaces and areas beyond perimeters. This extraction maintains sufficient environmental representation for navigation while reducing computational costs.
Solution Approach 2:
The patent applies partial action by tracking only the minimum necessary information - perimeter coordinates and nearest wall distances - rather than completely mapping all spaces. This partial approach provides sufficient navigation capability with reduced computational burden.
4Measurement precision
If beacons are placed within environment for mapping, then mapping accuracy is improved, but costs and space requirements increase
Solution Approach 1:
The patent applies self-service by enabling the robot to use its own sensors and movement data to establish perimeter coordinates and create maps, without requiring external beacons or markers. The robot serves itself for localization and mapping, eliminating additional components.
Solution Approach 2:
The patent makes the robot's existing sensors multi-functional - using the same distance sensor and imaging devices for both navigation and mapping tasks. This universality eliminates the need for specialized beacon systems while maintaining mapping capability.
Data Source
AI summary
Provided is a method for navigating and mapping a workspace, including: obtaining a stream of spatial data indicative of a robot's position in a workspace, the stream of spatial data being based on at least output of a first sensor; obtaining a stream of movement data indicative of the robot's displacement in the workspace, the stream of movement data being based on at least output of a second sensor of different type than the first sensor; navigating along a path of the robot in the workspace based on the stream of spatial data; while navigating, mapping at least part of the workspace based on the stream of spatial data to form or update a spatial map in memory; and switching to a second mode of operation if the stream of spatial data is unavailable due to the first sensor becoming impaired or inoperative.