Pool Cleaning Robot Camera for Drowning Detection and Navigation
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Solution Overview
Problem
Current pool cleaning devices and augmented reality games lack effective safety monitoring and navigation systems, particularly in water-related environments, which can lead to inefficiencies in cleaning cycles and increased risk of drowning events.
Innovation Solution
A system utilizing a camera unit with deep learning algorithms for real-time object detection and tracking, capable of distinguishing between individuals and objects, optimizing cleaning cycles and enhancing safety by identifying potential hazards and navigating around obstacles, while also serving as a platform for augmented reality games.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pool cleaning device operates autonomously without safety monitoring, then cleaning productivity is maintained, but safety risks increase leading to potential drowning events
Solution Approach 1:
The pool cleaning robot is equipped with a camera unit that enables multiple functions: primary cleaning operation, safety monitoring through person detection, and augmented reality gaming. This multi-functionality allows the system to maintain cleaning productivity while simultaneously providing safety monitoring capabilities, resolving the contradiction between safety and productivity
2Reliability
If a camera unit with deep learning algorithms is added for safety monitoring, then safety and navigation are improved, but device complexity increases
Solution Approach 1:
The camera unit incorporates deep learning algorithms that enable autonomous person detection, tracking, and hazard identification without requiring constant human intervention or complex external processing systems. The robot independently processes video data to navigate around detected persons and objects, reducing the need for additional complex control systems while maintaining high safety monitoring capabilities
3Speed
If real-time video analytics are processed on-board the robot, then response time for safety monitoring is reduced, but energy consumption increases
Solution Approach 1:
The system pre-loads deep learning models and detection algorithms into the on-board processor before operation. This preliminary preparation enables the robot to perform real-time video analytics with minimal processing delay, achieving fast response times for safety monitoring while avoiding the need for continuous cloud connectivity or external processing that would consume additional energy
Data Source
AI summary
A detection and tracking system and method using a camera unit on a robot, or alternatively a camera mounted inside the pool overlooking the bottom of the pool, for safety monitoring for use in and around water-related environments. The robot is able to propel itself and move throughout the body of water, both on the surface and underwater, and the camera unit functions both on the surface and underwater. The robot optimizes the cleaning cycle of the body of water utilizing deep learning techniques. The robot has localization sensors and software that allow the robot to be aware of the robot's position in the pool. The camera is able to send its video feed live over the internet, the processing is performed in the cloud, and the robot sends and receives data from the cloud. The processing utilizes deep learning algorithms, including artificial neural networks, that perform video analytics.