Overview and Motivation
The goal here was to create dynamically reconfigurable parameters that sets the desired configuration of the robot for the trajectory generator real time.
- Languages: C++, Python
- Framework: ROS2
- Library : OROCOS Toolchain, KDL, Reflexxes
- Skills : Trajectory Generation, Inverse Kinematics, UR5 Manipulator
Approach
Used the urdf description from Universal Robotics to solve the inverse kinmeatics problem. The ROS parameters were exposed to RQT to enable them to be adjusted by a GUI. The parameters that were dynamically reconfired were desired cartesian position, \([x,\ y,\ z,\ roll,\ pitch,\ yaw]\) desired joint angles, \([\theta_{1},\ \theta_{2},\ \theta_{3},\ \theta_{4},\ \theta_{5},\ \theta_{6}]\), and velocity sclaing parameter to adjust the velocity of the robot from 0-100%.
A RTT component was implemented. This RTT component has two operations; one to trigger a new joint
trajectory that moves the robot, from wherever the robot is, to the desired position.
And another one to move the robot by producing a Cartesian trajectory that moves the
robot from its current position to the desired Cartesian frame. Inverse kinematics was computed at every time step using the KDL library.
Result
The algorithm was tested on a real UR5.
Challenges
- Used
rcl_interfaces library to declare parameters that need to be dynamically reconfired. It was challenging to keep track of the changing variables - was solved using a callback that checked for changed variables.
\robot_description topic publishes the description only once when it is initiated. This was a challenge because by the time deployer starts running it would miss the description that was published - solved by adding a delay to the robot_description_node in the launch file.- Vibrations when implementing the algorithm on the real UR5 - solved by increasing the number of steps in the IK solver method.