Cleaning Robot Performance Tracking Using Sensor-Based Clean Scoring
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Solution Overview
Problem
Conventional cleaning robots and machines lack the ability to quantify and interpret their cleaning performance effectively, requiring manual inspection and resource-intensive efforts to determine the cleanliness of an area.
Innovation Solution
A network system that monitors and scores the cleaning performance of robots or machines by obtaining operational data, computing a clean score based on various factors such as area cleaned, time spent, and set goals, and providing reports through a user interface for fleet management and maintenance purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional cleaning robots and machines operate autonomously, then productivity is improved, but measurement precision of cleaning performance deteriorates
Solution Approach 1:
The system implements feedback by continuously collecting operational data from sensors during cleaning operations and using this data to compute clean scores. The feedback loop processes data from multiple sensors (wheel encoders, cameras, LIDAR) and provides quantitative performance metrics that enable autonomous robots to self-evaluate their cleaning effectiveness without manual inspection.
Solution Approach 2:
The patent replaces manual mechanical inspection with automated sensor-based measurement systems. Instead of human operators physically checking cleaned areas, the system uses sensors, cameras, and computational algorithms to automatically detect and measure cleaning performance, substituting mechanical human labor with electronic detection and processing systems.
2Measurement precision
If manual inspection is performed to determine cleanliness, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The cleaning robot performs self-assessment of its cleaning performance through integrated sensors and computational processing. The system automatically collects operational data, processes it through algorithms, and generates clean scores without requiring external human inspection, enabling the robot to self-evaluate and report its performance in real-time.
Solution Approach 2:
Manual inspection is replaced with automated optical and sensor-based detection systems. The patent uses cameras, LIDAR, and other sensors to capture images and data of cleaned surfaces, then processes these through image recognition and analysis algorithms to automatically determine cleanliness levels, eliminating the need for human inspectors.
3Measurement precision
If comprehensive operational data is collected, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses multi-functional sensors and processing units that serve multiple purposes. The same sensors and computational hardware used for navigation and obstacle detection are also utilized for cleaning performance measurement, eliminating the need for separate dedicated measurement devices and reducing overall system complexity while maintaining comprehensive data collection.
Solution Approach 2:
The patent combines multiple data collection functions into a unified processing system. Operational data from wheel encoders, sensors, and cameras—originally collected for navigation and operation—are merged and integrated with cleaning performance measurement, allowing the system to achieve comprehensive measurement without adding separate complex subsystems.
Data Source
AI summary
The present disclosure provides systems and methods for tracking and scoring robot or machine performance. The robot or machine performance may comprise a metric that can be computed based on operational data for the robot or machine.