Lesson plan
Asynchronous multi-body framework
Asynchronous multi-body framework (AMBF) is an open-source 3D versatile simulator for robots developed in April 2019. This multi-body framework provides a real-time dynamic simulation of multi-bodies such as robots, free bodies, and multi-link puzzles, paired with real-time haptic interaction with various input devices. The framework integrates a real surgeon master console, haptic or not, to control simulated robots in real-time. This feature results in the simulator being used in real-time training applications for surgical and non-surgical tasks. It offers the possibility to interact with soft bodies to simulate surgical tasks where tissues are subject to deformations. It also provides a Python Client to interact easily with the simulated bodies and train neural networks on real-time data with in-loop simulation. It includes a wide range of robots, grippers, sensors, puzzles, and soft bodies. Each simulated object is represented as an afObject; likewise, the simulation world is represented as an afWorld. Both utilize two communication interfaces: state and command. Through the State command, the object can send data outside the simulation environment, while the Command allows to apply commands to the underlying afObject.…
Article summary
Asynchronous multi-body framework (AMBF) is an open-source 3D versatile simulator for robots developed in April 2019. This multi-body framework provides a real-time dynamic simulation of multi-bodies such as robots, free bodies, and multi-link puzzles, paired with real-time haptic interaction with various input devices. The framework integrates a real surgeon master console, haptic or not, to control simulated robots in real-time. This feature results in the simulator being used in real-time training applications for surgical and non-surgical tasks. It offers the possibility to interact with soft bodies to simulate surgical tasks where tissues are subject to deformations. It also provides a Python Client to interact easily with the simulated bodies and train neural networks on real-time data with in-loop simulation. It includes a wide range of robots, grippers, sensors, puzzles, and soft bodies. Each simulated object is represented as an afObject; likewise, the simulation world is represented as an afWorld. Both utilize two communication interfaces: state and command. Through the State command, the object can send data outside the simulation environment, while the Command allows to apply commands to the underlying afObject.…
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Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
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- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
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