Robotic Path Planning Algorithms for Additive Manufacturing Using Advanced Simulation Tools

Robotics-based additive manufacturing (AM), or 3D printing, enables flexible printing systems. This paper analyses path planning algorithms for robotic manipulators aiming at dynamic AM environments. Using the cuRobo library, the study evaluates the path planning algorithm MotionGen and Model Predictive Control (MPC) using NVIDIA’s Isaac Sim with ROS2 and MoveIt2. Docker provided a modular development environment, and an Intel RealSense camera was used to enable real-world and real-time obstacle detection. Results show that MotionGen outperforms MPC in energy consumption and time efficiency, generating smoother and more efficient trajectories, more suitable for real-time AM contexts. The project shows the potential of advanced robotic control algorithms to optimize AM, using NVIDIA’s Isaac platform. Future work will focus on applying this to real robots.