readme.md

peg-in-hole-visual-servoing

Visual servoing for peg-in-hole.

The servoing consists of three nodes.

1. A crop node that captures and crops images from ros image topics.
2. An annotator node that processes the cropped images.
3. A client node that configures the crop node, subscribes to the annotations, and controls the robot(s).

The images can be captured and cropped by one computer, processed by a second computer (with a GPU) and the robot control can happen on a third computer. If the computer that captures images is not the same as the computer that controls the robots, then make sure, the computers clocks are synchronized, eg. with chrony.

Requires a trained model, see peg-in-hole-visual-servoing-model.

install

install opencv
$ pip3 install opencv-python

On the annotator node, torch and torchvision should also be installed.

install visual servoing module
$ pip3 install -e .

ROS is used for communication between the crop and client node.
$ catkin_make --directory ros
$ source ros/devel/setup.bash

on the computer that is connected to the cameras

python3 -m peg_in_hole_visual_servoing.crop

on a computer with GPU

python3 -m peg_in_hole_visual_servoing.annotator --model [model path]

on the computer connected to the robots

import json
from ur_control import Robot
import peg_in_hole_visual_servoing
from transform3d import SceneNode, SceneState

peg_robot = Robot.from_ip('192.168.1.123')
aux_robots = [Robot.from_ip('192.168.1.124')]
image_topics = '/camera_a/color/image_raw', '/camera_b/color/image_raw'

# build the scene structure
peg_robot_tcp, cams_robot_tcp, peg_robot_base, cams_robot_base, \
cam_a, cam_b, table = SceneNode.n(7)
table.adopt(
    peg_robot_base.adopt(peg_robot_tcp),
    cams_robot_base.adopt(cams_robot_tcp.adopt(cam_a, cam_b))
)
# insert necessary transforms from calibrations
state = SceneState()
state[peg_robot_base] = get_table_peg_base_calibration()
state[cams_robot_base] = get_table_cams_base_calibration()
state[cam_a] = get_tcp_cam_a_calibration()
state[cam_b] = get_tcp_cam_b_calibration()

Ks, dist_coeffs = get_camera_intrinsic_calibrations()

### Once, create a servo configuration:
# config_from_demonstration will let you move the robots in place for insertion
# and mark the holes in the images.
config = peg_in_hole_visual_servoing.config_from_demonstration(
        peg_robot=peg_robot, aux_robots=aux_robots,
        peg_tcp_node=peg_robot_tcp, aux_tcp_nodes=[cams_robot_tcp],
        scene_state=state,
        image_topics=image_topics, camera_nodes=[cam_a, cam_b],
        Ks=Ks, dist_coeffs=dist_coeffs
)

# the configuration is json serializable
json.dump(open('servo_config.json', 'w'), config)


### When servoing is needed
config = json.load(open('servo_config.json'))
peg_in_hole_visual_servoing.servo(
        peg_robot=peg_robot, aux_robots=aux_robots,
        peg_tcp_node=peg_robot_tcp, aux_tcp_nodes=[cams_robot_tcp],
        scene_state=state, camera_nodes=[cam_a, cam_b],
        servo_config=config, insertion_direction_tcp=(0, 0, 1)
)