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servo_platform

This repository contains packages to interface with a 2 Degrees of Freedom parallel platform. The platform contains 2 Servos and is used to as a test bench to implement different control strategies to accomplish tasks such as controlling the position of a ball on the platform.

The hardware

Servo Platform

  • The parallel platform: It contains 2 high performance Servos and a resistive screen.
  • Microcontroller NodeMcu ESP8266: The microcontroller is used to command the servos through PWM signals. The device also reads the current position of the servos and sends the information to the host. The communication between the host and the microcontroller is done through a serial port every 20ms. The microcontroller has been programmed with PlatformIO and using the Arduino framework. The microcontroller and the servos are powered by a 5V power supply.
  • The host: The host is a computer running Ubuntu 20.04 with docker.
  • Realsense D415 Camera: The camera is used to detect the position of a ball on the plaform. It provides a depth image and a color image. Realsense

The software

  • sp_driver: This package implements a C++ library to communicate with the microcontroller and send commands to the servos from the host.
  • sp_ros_driver: This package uses the previous driver and implements a ROS node to communicate with the microcontroller. There are scripts to interact with the platform and also to use a xbox controller to command it.
  • sp_control: Implementation of control algorithms to perform tasks with the platform. Currently there is only one controller implemented which is a PID algorithm to control the position of a ball on the platform.
  • sp_perception: This package implements different perception algorithms that the controllers can use as a feedback. Currently there is a node to track the position of a ball on the platform. The default tracker is CSRT from OpenCV.
  • sp_visualization: This package implements nodes to visualize the state of the platform and the variable to control. Currently there is a node that reads the state of the PID controller (set point), the position and the bounding box of the ball and creates an image with this information and publishes it. This image can be visualized with rqt_image_view. RQT

Ball on plate

The first controller implemented is a PID controller to control the position of a ball on the platform. Its parameters have been exposed through the dynamic_reconfigure package so they can be tuned in real time. The controller uses the position of the ball on the platform as a feedback. This position is the output of the tracking algorithm. The controller computes the control action and sends it to the ROS driver node. The image is captured from a Realsense located on the top of the platform.

Response against disturbances:

Disturbances

Control diagram

Servo Platform

Next steps

This project has just started and there are many things to do. First of all, the purpose of this project is to have a test bench to implement different control and perception algorithms, which could be based on classical control theory/computer vision or machine learning. Another objective is to find a way to deploy the software as in production. Bearing this in mind, we can classify tasks in short term and long term.

Short term

  • Implement a node to generate step trajectories to fine tune the PID controller.
  • Fill in doxygen documentation.
  • Refactor the PID controller code. Create a PID controller class and the SpBallController will use this class through composition (one instance for x and another for y).
  • Upload the videos to youtube to avoid large files in the repository.
  • Fine tune the parameters. The current response has oscilations and big position error. This could be solved by a better tuning, improving the mechanical clearance or linearizing the system.
  • Create launch file to start the controller easily.
  • Create a configuration for rqt to launch it as a GUI (dynamic reconfigure server, image_view, real time plots).
  • The platform suffers from huge mechanical clearance, which adds more non linearity to the system. Find a way to reduce them.
  • Integration with mediapipe.

Long term

  • Find a tracking algorithm that can run on a GPU, the implementation of the current one uses CPU.
  • Perhaps use the Jetson Nano to deploy the tracking algorithm and release the host laptop from this task.
  • Containerize the application.
  • Implement a simulation of the ball control task so that Agents can learn.
  • Train a RL algorithm to control the ball position and transfer it to the real platform. Perhaps it could use Imitation learning from the current PID controller to learn faster.
  • Use a microcontroller that can run RTOS.