Implementation of Neural-based Motion planner for Motion planning of Panda Arm robot. There are two variations of Motion planner:
- Motion planning in a single familiar environment.
- Motion planning in multiple unknown environments.
The Project report can be found here.
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Data Generation.
- Any Classical Motion Planner can be used to generate dataset. Here we use OMPL Motion Planner to generate RRT*.
- We use MoveIt Platform to integrate Motion planning with visualization and simulation.
- Custom dataset class.
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MPNet algorithm.
- Network architecture for end-to-end and modular trained networks.
- Software stack for training and testing the algorithm.
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Python visualization files to compare performance of MPNet with the expert demonstrator.
- In case of motion planning in a single known environment, we only need the absolute initial and final states that completely define the configuration of the robot.
- In case of motion planning in multiple unknown environments, we consider the point cloud data along with the initial and final configurations. we train the network using 7 environments and test it's performance on the trained environments. In addition to that we test the performance on 3 new environments.
- For training to generate motion plans in a single environment.
- Open RViz
roslaunch panda_moveit_config demo.launch
. - Run the joint state planner to generate the dataset.
python panda_arm_motion_planning/scripts/joint_space_planning.py
. You can tweak the parameters according to your choice.
- Open RViz