AI-Supported Robot Trajectory Optimization

The aim of the Experiment is to reduce existing errors allowing to increase the level of product quality, the main device is the robot arm, AI methods are applied on the device. Below are the needs to which AI technologies can be implemented:

• Generate the post-processing machining trajectory for individual workpiece. Use less external measurement sensors and devices to realize the tool center point (TCP) trajectory compensation.
• Trajectory optimization (compensation) can suit in different machining situations, which have different cutting speed, feed rate, workpiece material, workpiece geometry, machining trajectory etc.
• Transformation from simulation to the real hardware, the AI model is expected to keep the performance on real hardware. The challenge is to clarify in which subtasks AI can be implemented, evaluate the effectiveness of the AI methods, ensure that all AI-techniques have good compatibility to the current company platform and ensure that the entire toolchain including all AI modules is stable and robust.