AI Pool Cleaner Navigation for Accurate Debris Detection
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Solution Overview
Problem
Existing automatic pool cleaners operate inefficiently due to random navigation paths and inaccurate debris detection, often failing to identify or misidentifying objects, leading to incomplete cleaning and battery drain.
Innovation Solution
An aquatic cleaning system equipped with imaging devices, a detection system, and a control system that uses a trained model to classify debris and non-debris, optimizing navigation paths based on object identification and mapping the aquatic environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If random navigation algorithms are used, then the pool cleaner can operate with simple control systems, but the cleaning efficiency is low and the cleaner cannot traverse the entire pool before power depletion
Solution Approach 1:
The system continuously captures images of the pool environment, processes them through the trained model to identify debris and non-debris objects, and uses this feedback to dynamically adjust navigation paths. The cleaner receives real-time feedback about object locations and modifies its trajectory to efficiently collect debris while avoiding non-debris items, thereby resolving the contradiction between simple control and high productivity.
Solution Approach 2:
The system performs preliminary classification of objects using the trained model before the cleaner physically reaches them. By pre-identifying debris and non-debris objects in the pool environment, the system can plan optimal collection paths in advance, maximizing cleaning efficiency within the available power constraints without requiring overly complex real-time control systems.
2Device complexity
If simple detection algorithms are used, then the device complexity is reduced, but the detection accuracy is insufficient leading to misidentification of debris and non-debris
Solution Approach 1:
The patent replaces traditional mechanical or simple optical detection systems with an artificial intelligence-based trained model. This model uses machine learning to accurately distinguish between debris and non-debris objects by analyzing image data, achieving high detection precision without requiring overly complex hardware systems. The AI model processes visual information to identify objects with high accuracy, resolving the contradiction between detection accuracy and system complexity.
3Productivity
If the pool cleaner traverses the entire pool to collect all debris, then complete cleaning is achieved, but the power source is depleted before completion
Solution Approach 1:
The system uses the trained model to identify and prioritize debris collection based on its classification of objects. Rather than uniformly traversing the entire pool, the cleaner focuses its energy on collecting identified debris items while avoiding non-debris objects. This selective approach ensures complete cleaning of actual debris within the available power budget, resolving the contradiction between cleaning completeness and power consumption.
4Productivity
If control algorithms use underwater imaging to detect debris, then targeted collection is possible, but the algorithms fail to identify or falsely identify objects
Solution Approach 1:
The patent replaces traditional image processing algorithms with a trained artificial intelligence model that has been specifically trained to distinguish between debris and non-debris objects in underwater environments. This AI model accurately identifies objects by analyzing visual features, eliminating the false identification problems that plague conventional algorithms. The system maintains targeted cleaning efficiency while achieving high identification accuracy through the AI-based detection system.
Data Source
AI summary
An aquatic cleaning system includes a pool cleaner having a housing, a drive system for moving the housing, and a debris basket associated with the housing. The debris basket is designed to receive debris from the aquatic environment. The system includes a data capture system including an imaging device operably coupled to the cleaner. The system also includes a detection system having a storage medium storing a trained model, and a processor designed to receive data elements from the data capture system and input the data elements into the trained model to identify a detected object within the aquatic environment as being debris or non-debris. The system also includes a control system in communication with the detection system and the drive system to move the pool cleaner toward the detected object identified by the detection system when the detected object is identified by the detection system as being debris.