Robot Battery Dismantling With Vision-Guided Fastener Removal
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Solution Overview
Problem
The increasing demand for electric vehicle batteries has led to a substantial amount of waste batteries, necessitating a safe and efficient method for dismantling them. Current methods rely on manual processes that require expertise and precision.
Innovation Solution
A battery processing system that utilizes a robot apparatus to automate the transportation, storage, and dismantling of batteries. This system includes methods for determining the processing process based on battery information, training an AI engine to control the robot, and controlling the robot using the trained AI engine to perform tasks such as gripping connectors, disconnecting electric wires, and removing buffer materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual dismantling methods are used, then flexibility and adaptability to different battery types are maintained, but productivity is low and the process requires high expertise
Solution Approach 1:
The system uses vision sensors and AI algorithms to automatically identify battery types, locate components, and plan dismantling trajectories without human intervention. The robot apparatus autonomously adapts to different battery configurations through image recognition and automated decision-making, eliminating the need for manual expertise while maintaining flexibility.
Solution Approach 2:
The system changes operational parameters dynamically based on detected battery characteristics. The control device adjusts gripping forces, movement speeds, and dismantling sequences according to real-time vision data and battery identification, enabling automated adaptation to various battery types without reconfiguring the entire system.
2Reliability
If automated robot systems are implemented, then productivity and safety are improved, but device complexity increases
Solution Approach 1:
The system replaces manual mechanical operations with automated robotic mechanisms controlled by vision systems and AI algorithms. The robot apparatus uses sensors to detect battery states and automatically executes safe dismantling procedures, reducing human exposure to hazards while managing complexity through software-based control rather than complex mechanical designs.
Solution Approach 2:
The vision sensor system and AI control algorithm act as intermediaries between the human operator and the robotic dismantling process. These intermediaries translate visual information into automated control decisions, enabling safe operation without direct human involvement in dangerous tasks while keeping the system architecture relatively simple and modular.
3Manufacturing precision
If manual processing is used, then device complexity is low, but manufacturing precision and component identification accuracy are insufficient
Solution Approach 1:
The system replaces manual visual inspection and component location with automated vision sensors and image recognition algorithms. These sensors capture high-resolution images of battery components, and AI algorithms automatically identify connector positions, wire routes, and buffer material locations with precision exceeding manual capabilities, while the automation level is managed through software control.
4Productivity
If automated processing is implemented, then productivity increases, but the extent of automation requires advanced AI and control systems
Solution Approach 1:
The AI control system operates autonomously to identify battery types, plan dismantling sequences, and control robot movements without continuous human input. The system processes vision data in real-time to make automated decisions about component removal sequences, enabling high productivity through self-directed operation rather than human-in-the-loop control.
Solution Approach 2:
The system performs preliminary battery identification and component location using vision sensors before the actual dismantling begins. AI algorithms pre-plan the dismantling trajectory and sequence based on detected battery characteristics, allowing the robot to execute high-speed automated operations without real-time decision delays, thus increasing throughput while managing AI computational requirements.
Data Source
AI summary
A system and method to control the operation of a robot apparatus. The method comprises: receiving a control signal; obtaining sensing data associated with a battery; identifying a location of a first fastening member fixing the first component based on the sensing data and setting the location as a first target location; determining a first trajectory for a first end effector to reach a first location corresponding to the first target location; controlling the first end effector to perform a first task of dismantling the first fastening member from the battery at the first location; setting a second target location based on the sensing data and determining a second trajectory for a second end effector to reach a second location corresponding to the second target location; and controlling the second end effector to perform a second task of disconnecting the first component at the second location.