Vision-Guided Actuator Head for Remote Multi-Control Switching
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Solution Overview
Problem
Current devices, such as switch bots, require manual placement on each control to change the state of external systems, lacking flexibility for multiple control management without physical movement, and necessitate add-on stickers for position changes.
Innovation Solution
An automated control activation system employing a machine learning-enabled camera embedded in an actuator head connected to an adjustable jointed arm, which identifies and aligns with control orientations to change states automatically, eliminating the need for physical placement and enabling remote operation of multiple controls using extended reality devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual placement of switch bots on each control is used, then control state change is achieved, but device complexity and installation effort increase
Solution Approach 1:
The patent uses vision sensors to create a digital model of the control panel layout, replacing the need for physical switch bots on each control. The system captures images of the control panel, identifies control positions and orientations through image processing, and uses this digital representation to guide the robotic arm for automatic switch activation, thereby eliminating manual placement complexity
Solution Approach 2:
The patent replaces the mechanical placement and operation of multiple individual switch bots with an automated vision-guided robotic arm system. The mechanical system of manually placing switches on each control is substituted by an optical-mechanical integration where vision sensors detect control positions and a robotic arm executes the switching action, reducing overall device complexity
2Adaptability or versatility
If add-on stickers are used for position changes, then control management flexibility is improved, but device complexity and installation requirements increase
Solution Approach 1:
The patent implements a dynamic control management system where the robotic arm can adapt to different control panel layouts and positions in real-time. The vision system continuously identifies control positions, and the robotic arm dynamically adjusts its movement path and positioning, providing flexibility without requiring static add-on stickers or manual reconfiguration
Solution Approach 2:
The patent creates a universal control management system that can handle multiple types of controls (lights, curtains, AC, TV) and various panel layouts through a single robotic arm platform. The vision-guided system recognizes different control types and positions, enabling one device to perform multiple functions across different environments without requiring device-specific modifications or stickers
3Adaptability or versatility
If multiple devices are deployed for multiple controls, then control coverage is improved, but device complexity and cost increase
Solution Approach 1:
The patent merges the functions of multiple individual control devices into a single integrated robotic arm system. Instead of deploying separate switch bots for each control, the system combines vision sensing, control panel analysis, and robotic actuation into one unified platform that can manage multiple controls across different external systems, thereby reducing overall device complexity and cost
4Ease of operation
If physical movement to control location is required, then control activation is achieved, but loss of time and operational efficiency decrease
Solution Approach 1:
The patent implements a self-service control activation system where the robotic arm autonomously navigates to control positions and activates switches without human intervention. The vision system automatically identifies control locations, the robotic arm independently positions itself, and the system self-corrects positioning errors, eliminating the time loss associated with manual travel and operation while maintaining ease of control activation
Data Source
AI summary
Changing state of an external system control is provided. An orientation of a control corresponding to an external system is identified utilizing a machine learning-enabled camera. An actuator head is moved to align with the orientation of the control corresponding to the external system utilizing the machine learning-enabled camera. A current state of the control corresponding to the external system is changed to a user-desired state utilizing the actuator head.