SmartArm is a series of reconfigurable modular robotic manipulators using full closed-loop perception control and adaptive control to achieve the same repeatability with much more expensive robotic manipulators. Using multi-sensor system and removable structure, it will adapt to multi-environment tasks. Smartarm is fully built by my own and I will do further research based on this platform. Smartarm uses ROS noetic as the middleware and the physical structure was designed through Fusion 360 Autodesk.
The first GIF below shows the reaction speed test of end effector tracking with Smartarm's sonic localization system, which is illustrated in the next sector. The second GIF below shows Smartarm's motion planning & execute pipeline using MoveIt! APIs with my own code adjustments. I also created a MoveIt! function called asyncPlanAndMove() after changing MoveIt! source code to reach my planning goal.
With the benefit of popular open-source frame, Smartarm is suitable for academic and industrial use. I will keep developing this series of robotic manipulators.
I combined my previous sonic localization system (developed when I was in Harbin Institute of Technology) and IMU together with extended Kalman Filtering. The accuracy of the localization is now about 0.5mm in xyz axis and about 0.01rad in 3 rotation axis.
This sensor system uses STM32F103RCT6 as the control unit. I developed DMA(direct memory access) and hardware boosting to calculate the sensors' data faster. The sampling rate is 1000Hz.
Currently I am developing a real-time dynamic controller of Smartarm. It transits the position signal generated by the main compute unit (Linux) into force signal (joint space). I used adaptive feedforwared control (AFC) to learn the dynamic parameter of the manipulator. For some repeatable tasks like pick and place, I will also develop iterative learning control (ILC) to improve control accuracy.
It will also contains real-time interpolation algorithm to smooth the generated trajectory.
I also developed a MoveIt! simulation&control environment for Smartarm. I developed my own MoveIt! move_group function to fit the motion planning algorithm. The function move() in MoveAction's client has been adjusted as smartarm_move to fulfill the fast generation of the trajectory.
The structure above shows Smartarm's MoveIt! functions w.r.t. sensor system and the main control loop. I will continue to develop the low-level real-time controller to improve the performance of Smartarm.