Collaborative Welding Arm Trajectory Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing collaborative welding devices require significant processing time for trajectory optimization, especially for small or medium series production, and lack the ability to optimize trajectories for future tasks, leading to suboptimal performance and increased production time.
Innovation Solution
A collaborative device that allows technicians to easily record trajectories for robotic arms, facilitated by a computer unit that generates an automated program, smoothing movements and optimizing tool orientation and speed based on sensor data, ensuring precise and efficient welds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a technician manually programs robot trajectories by assembling movements from a database or using processing programs, then the robot can perform automated tasks, but the process requires significant processing time and the trajectories are not optimized for future use
Solution Approach 1:
The robot performs self-learning by autonomously executing trajectories and sensing its own movements and the workpiece characteristics. Through this self-service mechanism, the robot automatically optimizes trajectories for future tasks without requiring manual reprogramming, thereby reducing processing time while maintaining automation.
Solution Approach 2:
The system implements feedback loops where the robot's sensors detect actual movements and workpiece characteristics during execution, and this information is fed back to the control unit. The control unit uses this feedback to automatically adjust and optimize trajectories for subsequent tasks, eliminating the need for time-consuming manual reprogramming while preserving automated execution.
2Manufacturing precision
If a technician manually determines and calculates optimal trajectories for complex welding tasks, then the robot can achieve precise welds in hard-to-reach places, but the implementation time becomes prohibitively long for small or medium series production
Solution Approach 1:
The robot autonomously learns optimal welding trajectories by executing movements and using its sensors to detect workpiece geometry and welding characteristics. This self-learning capability allows the system to achieve precise welds in hard-to-reach places without requiring the technician to manually calculate complex trajectories, dramatically reducing implementation time for small or medium series production while maintaining high manufacturing precision.
Solution Approach 2:
The robot performs preliminary learning executions of trajectories during the teaching phase, gathering sensory data about actual movements and workpiece characteristics before automated production. This preliminary action enables the system to pre-optimize trajectories for future tasks, ensuring high weld quality without requiring time-consuming manual calculations during actual production runs.
3Productivity
If the robot executes predetermined trajectories without learning, then automated production can be maintained, but the trajectories lack optimization and may have defects
Solution Approach 1:
The robot autonomously optimizes its own trajectories by executing movements and using sensors to detect actual performance and workpiece characteristics. This self-service learning process allows the system to maintain high production rates while continuously improving trajectory quality, eliminating the defects associated with purely predetermined trajectories without sacrificing productivity.
Solution Approach 2:
The control unit receives feedback from sensors during trajectory execution and uses this information to automatically adjust and optimize trajectories for future tasks. This feedback mechanism enables the system to maintain high production rates while improving manufacturing precision, as the robot learns from actual execution data rather than relying solely on predetermined paths.
4Stability of the object's composition
If the robotic arm motors act as brakes during technician movement, then control stability is maintained, but the technician experiences difficulty moving the tool
Solution Approach 1:
The control system dynamically adjusts motor behavior based on the operational phase. During the teaching phase when the technician moves the tool, the motors are controlled to assist movement rather than brake, improving ease of operation. During automated execution, the system transitions to a more stable control mode, demonstrating dynamic adaptation between different operational requirements.
Solution Approach 2:
The robot's control system autonomously determines when to switch between assistive and stable control modes based on the operational context. By detecting whether the system is in teaching or execution mode, the control unit automatically adjusts motor characteristics to provide assistance during teaching for ease of operation, then transitions to stable control during automated execution, eliminating the need for manual mode switching.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to a collaborative device (1) comprising: - a robotic arm (3) including at least one motor (32); - a tool (4) attached to a free end of the robotic arm (3); - a computer unit (5) connected to the robotic arm (3) to transmit control instructions for the robotic arm (3), characterized in that the device (1) also includes a joint (6) having a flexible connection, the device (1) integrating at least one sensor (7) parameterized to detect forces exerted on the flexible connection, the computer unit (5) being configured to: - receive data from the sensor (7); - translate said data into torques experienced at the level of said motor(s) (32) of the robotic arm (3); - generate instructions for attenuating the torques experienced, - control said motor(s) (32) of the robotic arm (3) with the attenuation instructions.