The paper deals with the generation of optimal trajectories for industrial robots in machining and additive manufacturing applications. The proposed method uses an Ant Colony algorithm to solve a kinodynamic motion planning problem. It exploits the kinematic redundancy that is often present in these applications to optimize the execution of trajectory. At the same time, the robot kinematics and dynamics constraints are respected and robot collisions are avoided. To reduce the computational burden, the task workspace is discretized enabling the use of efficient network solver based on Ant Colony theory. The proposed method is validated in robotic milling and additive manufacturing real-world scenarios.
Optimal robot motion planning of redundant robots in machining and additive manufacturing applications
Faroni M.;
2019-01-01
Abstract
The paper deals with the generation of optimal trajectories for industrial robots in machining and additive manufacturing applications. The proposed method uses an Ant Colony algorithm to solve a kinodynamic motion planning problem. It exploits the kinematic redundancy that is often present in these applications to optimize the execution of trajectory. At the same time, the robot kinematics and dynamics constraints are respected and robot collisions are avoided. To reduce the computational burden, the task workspace is discretized enabling the use of efficient network solver based on Ant Colony theory. The proposed method is validated in robotic milling and additive manufacturing real-world scenarios.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
