.png){kind=icon}
.png){kind=icon}
Return
Model predictive pose optimization for energy efficient robotic machining
.png){kind=icon}
OA
AI
DOI：10.1007/s11044-025-10127-4.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
One focus of aviation research in recent years has been the reduction of greenhouse gases through weight savings in aircraft construction, e.g. by using lightweight materials such as CFRP components. Lighter aircraft lead to less fuel consumption, which impacts the global carbon footprint and is an important step towards CO2-neutral flying. In order to achieve national climate goals, the environmental impact should already be assessed at the production stage. Most recently, hybrid kinematics consisting of serial kinematics (industrial robots on linear rails) and parallel kinematics (hexapod) mounted to the robot’s end effector, have been studied at Fraunhofer IFAM for the purpose of machining distorted CFRP-components. This work analyzes to which extent the redundant degrees of freedom of such hybrid kinematics can be exploited to determine energetically advantageous poses during machining. Therefore, the inverse kinematics are solved within an Optimal Control Problem (OCP) including the forward kinematics as well as minimizing the system’s energy consumption while following a reference trajectory.
Keywords:
Model predictive control
Pose optimization
Energy efficiency
Robotic machining
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.4
Papers:
152
Citations:
2.5K