Robot Feature Scanning Using Mapped Local Routes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robots lack the ability to efficiently scan and identify features within complex environments, leading to inefficiencies in tracking traffic flow, inventory management, and missed sales due to misplaced items, resulting in significant economic losses.
Innovation Solution
A system and method for configuring a robot to produce a site map, learn local scanning routes, align them with the site map, receive user annotations, and execute these routes while collecting sensor data, allowing for the identification of features such as missing or misplaced items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots are deployed to scan complex environments for features, then productivity and operational efficiency are improved, but the ability to accurately identify and track features such as inventory items and traffic flow remains insufficient
Solution Approach 1:
The patent segments the scanning task into multiple local scanning routes, each covering a specific portion of the environment. The robot divides the overall site map into manageable segments and systematically scans each segment, allowing for more precise feature identification within each localized area while maintaining high productivity across the entire environment.
Solution Approach 2:
The patent implements preliminary action by requiring manual guidance of the robot along the complete site map route before autonomous scanning begins. This preliminary traversal establishes the geometric model and identifies all features in advance, enabling the robot to then efficiently scan for specific features of interest during autonomous operation with higher accuracy.
2Measurement precision
If the robot scans the entire site map manually first, then feature identification accuracy is improved, but the time required for configuration and deployment increases
Solution Approach 1:
The patent performs the time-consuming manual site map traversal as a preliminary action that needs to be done only once during initial configuration. The geometric model and feature locations are established in advance, allowing subsequent autonomous scanning operations to be performed quickly and efficiently without repeating the manual traversal.
Solution Approach 2:
The patent creates a geometric model (a digital copy) of the site map during the preliminary manual traversal. This copied model is then used repeatedly during autonomous scanning operations, eliminating the need to physically re-traverse the site map each time and significantly reducing configuration time for future scanning tasks.
3Productivity
If the robot uses pre-defined scanning routes, then productivity is improved, but adaptability to new environments and changes in the site map deteriorates
Solution Approach 1:
The patent implements dynamics by allowing the robot to learn and adapt to the geometric model of the site map during initial manual guidance. The system dynamically adjusts to the actual layout and features encountered during the preliminary traversal, creating an accurate geometric representation that enables efficient autonomous scanning while adapting to the specific environment rather than requiring pre-defined routes.
Solution Approach 2:
The patent enables self-service by allowing the robot to autonomously navigate and scan using the geometric model it has learned. Once the initial site map is created through manual guidance, the robot independently plans and executes scanning routes, identifies features, and adapts to environmental changes without requiring continuous human intervention or pre-programmed routes.
Data Source
AI summary
Systems and methods for configuring a robot to scan for features are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may be configured to scan for features within an environment by producing various computer-readable maps which may be annotated to facilitate organized and accurate feature scanning.