Output Error of ultrasonic​DetectionG​enerator in Simulink

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정호 윤
정호 윤 el 20 de Jul. de 2024
Respondida: Angelo Yeo el 22 de Jul. de 2024
The measurement error that occurred previously is no longer happening.
If no input is provided to the Scenario Reader, no errors occur.
However, when I start providing values through parkEgo in the Scenario, the ultrasonicDetectionGenerator fails to produce any output and continuously encounters errors.
The simulation does not terminate due to the error, but it remains stuck without producing any output, and when I press ctrl+c in the Matlab command window, it always shows an error in the Implement of ultrasonicDetectionGenerator.
Due to the above issue, I am currently using the prerelease version 2024b.
시뮬레이션 중에 오류가 발생하여 시뮬레이션이 종료되었습니다
원인:
An error occurred during the simulation, causing it to terminate.
Cause:
  • An error occurred in the MATLAB System block 'AIV_robot/Environment/Simulation/Ultrasonic Sensor Processing/Ultrasonic5' while calling the 'stepImpl' method of 'ultrasonicDetectionGenerator'.
  • Program interruption (Ctrl+C) was detected.
  6 comentarios
Angelo Yeo
Angelo Yeo el 20 de Jul. de 2024
I found some issues in your Simulink model and fixed it. It runs okay in R2024b Prerelease. Can I contact you via email?
정호 윤
정호 윤 el 21 de Jul. de 2024
Editada: Angelo Yeo el 21 de Jul. de 2024
Thank you very much for your assistance!
My email address is ...
(Edited by Angelo and removed the email address for privacy purpose)

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Angelo Yeo
Angelo Yeo el 22 de Jul. de 2024
First, please check if the path to scenario file for Scenario Reader is correct. The reason you ran into an error from "Ultrasonic Detector" block is that the block cannot receive proper scenarios and cannot generate bus variables out of them. Hence, you should check availability of the scenario file.
Second, the action value output from the Robot Controller is directly connected to the Environment, which creates an algebraic loop. To prevent this, please try adding a unit delay block to your current model.
If you take the above steps, you will see that the reinforcement learning agent gets trained without any problems.
You can find the fixed model in the attachment. Feel free to post further comments or questions if you have.

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