Robotic Energy Supply Alignment Using Vision and ML
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Solution Overview
Problem
Challenging environmental conditions and human errors pose risks to safe and efficient energy supply to devices, particularly in hazardous environments, leading to malfunctions, waste, and potential hazards.
Innovation Solution
A multifunction gripper device equipped with a camera, cap engagement mechanism, and nozzle engagement mechanism, utilizing machine learning and robotic control to automate energy supply, ensuring precise alignment and operation under various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual fueling operations are used, then operational flexibility is maintained, but human error and safety risks increase
Solution Approach 1:
The system enables self-service fueling operations where the automated fueling system performs all operations including approaching the vehicle, identifying fueling points, opening caps, connecting nozzles, and monitoring the fueling process without human intervention, thereby eliminating human error while maintaining operational flexibility
Solution Approach 2:
Manual mechanical operations are replaced with an automated system that uses cameras for vision, machine learning algorithms for decision-making, and robotic mechanisms for physical operations, substituting human cognitive and manual functions with automated intelligent systems
2Reliability
If automated fueling systems are deployed, then safety is improved, but device complexity increases
Solution Approach 1:
The automated fueling system is designed as a universal platform that can service multiple vehicle types and fuel types through a single standardized mechanism, reducing overall system complexity while maintaining high safety standards across different applications
Solution Approach 2:
A centralized control unit acts as an intermediary that coordinates between the camera system, machine learning algorithms, robotic mechanisms, and fueling operations, simplifying the overall system architecture by providing a single point of control and integration
3Productivity
If precise alignment is required for energy supply, then energy delivery efficiency is improved, but operational difficulty increases
Solution Approach 1:
The system uses cameras to continuously capture images of the vehicle and fueling point, processes these images through machine learning algorithms to determine precise alignment, and adjusts the robotic mechanism in real-time based on feedback, ensuring accurate energy delivery without requiring manual precision
Solution Approach 2:
The system performs preliminary actions by pre-identifying the fueling point location, pre-calculating the approach path, and pre-positioning the robotic mechanism before actual fueling begins, ensuring precise alignment is achieved automatically without operational difficulty
Data Source
AI summary
Disclosed embodiments relate to systems and methods for automatically enabling supply of energy to a machine using a movable member comprising a camera and an energy supply mechanism. Techniques include positioning the movable member to a first orientation angle to capture image data using the camera; accessing a trained machine learning model associated with the machine; identifying, based on the trained machine learning model and the captured image data, an energy input location of the machine; positioning the movable member to a second orientation angle different from the first orientation angle; moving the energy supply mechanism into a position according to the energy input location; and enabling supply of energy via the energy supply mechanism to the machine at the energy input location.