Collaborative Robotic Arm Torque Attenuation for Trajectory Teaching
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Solution Overview
Problem
Current collaborative welding devices require significant processing time and are not optimized for trajectories, especially in small or medium series production, as they rely on technician-defined sequences and lack the ability to anticipate future tasks, leading to suboptimal robot performance and difficulty in replicating complex inclinations needed for accessing hard-to-reach weld locations.
Innovation Solution
A collaborative device with a robotic arm, a tool, and a computer unit that includes a flexible joint with a sensor to detect forces during manual operation, allowing the generation of attenuation instructions to control the robotic arm's motors, enabling the recording and replication of trajectories with improved speed and smoothness, facilitating both manual and automatic modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the robot performs tasks sequentially based on pre-defined trajectories, then the program can be created using digital methods, but the trajectories are not optimised and processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimized trajectories in a database before actual production. The computer unit calculates optimal trajectories in advance based on part geometry and welding parameters, so that during production, the robot can directly execute these pre-optimized paths without real-time computation delays.
Solution Approach 2:
The system dynamically adapts trajectory optimization based on future task anticipation. The computer unit analyzes the production sequence and optimizes trajectories considering upcoming operations, allowing the robot to adjust its motion paths dynamically rather than following fixed sequential commands, thereby reducing total processing time.
2Extent of automation
If the technician defines trajectories manually, then the robot can perform tasks automatically, but some trajectories cannot be optimised for future tasks
Solution Approach 1:
The computer unit performs preliminary optimization of trajectories before the robot executes them. By analyzing the complete production sequence in advance, the system calculates optimal paths that consider future tasks, storing these pre-optimized trajectories in the database for the robot to execute automatically, thereby achieving both automation and optimization.
Solution Approach 2:
The system implements feedback mechanisms where the computer unit continuously monitors robot execution and compares actual performance with optimized trajectories. This feedback loop allows the system to refine and re-optimize trajectories based on actual production data, improving productivity while maintaining automatic operation.
3Manufacturing precision
If the welding torch must have a well-defined inclination for difficult locations, then weld quality improves, but the trajectory becomes difficult to compute or determine
Solution Approach 1:
The computer unit performs preliminary calculation of complex trajectories with specific inclination requirements before production. By pre-computing the optimal paths that achieve the required torch angles for difficult-to-access locations, the system stores these complex trajectories in the database, allowing the robot to execute them automatically without real-time computation complexity.
Solution Approach 2:
The computer unit acts as an intermediary between the simple robotic execution and the complex welding requirements. It translates the need for specific torch inclinations into computable trajectory commands, serving as a mediator that converts manufacturing precision requirements into executable robot paths that the robot can follow accurately.
4Productivity
If optimization is dedicated for large series production, then identical parts can be manufactured efficiently, but implementation time is significant which is not available for small or medium series
Solution Approach 1:
The system dynamically adapts its optimization approach based on production volume. For small or medium series, it uses rapid trajectory calculation methods that provide good enough optimization quickly. For large series, it applies more computationally intensive optimization algorithms that achieve superior efficiency. This dynamic adaptation allows the system to balance implementation time against productivity gains according to the specific production context.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution allows for efficient and accurate trajectory recording and replication, reducing production time, improving weld quality, and enabling the creation of identical work phases in series, while maintaining desired speed and quality control, even in complex or hard-to-reach areas.
Implementation Method 1
a sensor parameterised to detect forces exerted on the flexible connection when the tool is moved by the technician
Implementation Method 2
translate said data into torques applied at said motor(s) of the robotic arm; generate instructions for attenuating the applied torques; control said motor(s) of the robotic arm with the attenuation instructions
Data Source
AI summary
A collaborative device includes: a robotic arm including at least one motor; a tool secured to a free end of the robotic arm; a computer unit connected to the robotic arm to transmit instructions for controlling the robotic arm; and a joint having a flexible connection. The device integrates at least one sensor parameterised to detect forces exerted on the flexible connection. The computer unit is configured to: receive data from the sensor; translate the data into torques applied at the motor(s) of the robotic arm; generate instructions for attenuating the applied torques; and control the motor(s) of the robotic arm with the attenuation instructions.