Robots-Weblog | Exploring Elephant Robotics LIMO Cobot

Navigation:

1. First, begin the radar by coming into the next command within the terminal:

roslaunch limo_bringup limo_start.launch pub_odom_tf:=false

2. Begin the navigation operate by coming into the next command within the terminal:

roslaunch limo_bringup limo_navigation_diff.launch

Upon success, this interface will open, displaying the map we simply created.

Click on on „2D Pose Estimate, “ then click on on the placement the place LIMO is on the map. After beginning navigation, you’ll find that the form scanned by the laser doesn’t overlap with the map. You should manually right this by adjusting the precise place of the chassis within the scene on the map displayed in rviz. Use the instruments in rviz to publish an approximate place for LIMO. Then, use the controller to rotate LIMO, permitting it to auto-correct. When the form of the laser scan overlaps with the shapes within the map’s scene, the correction is full, as proven within the determine the place the scanned form and the map overlap.

Click on on „2D Nav Objective“ and choose the vacation spot on the map for navigation.

The navigation check additionally proceeds easily.

Subsequent, we are going to transfer on to the half in regards to the static robotic arm’s greedy operate.

Figuring out and Buying the Pose of Aruco Codes

To exactly determine objects and acquire the place of the goal object, we processed Aruco codes. Earlier than beginning, guarantee the particular parameters of the digital camera are set.

Initialize the digital camera parameters primarily based on the digital camera getting used.

def __init__(self, mtx: np.ndarray, dist: np.ndarray, marker_size: int):
self.mtx = mtx
self.dist = dist
self.marker_size = marker_size
self.aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250)
self.parameters = cv2.aruco.DetectorParameters_create()

Then, determine the item and estimate its pose to acquire the 3D place of the item and output the place info.

def estimatePoseSingleMarkers(self, corners):
"""
This may estimate the rvec and tvec for every of the marker corners detected by:
corners, ids, rejectedImgPoints = detector.detectMarkers(picture)
corners - is an array of detected corners for every detected marker within the picture
marker_size - is the scale of the detected markers
mtx - is the digital camera matrix
distortion - is the digital camera distortion matrix
RETURN record of rvecs, tvecs, and trash (in order that it corresponds to the previous estimatePoseSingleMarkers())
"""
marker_points = np.array([[-self.marker_size / 2, self.marker_size / 2, 0],
[self.marker_size / 2, self.marker_size / 2, 0],
[self.marker_size / 2, -self.marker_size / 2, 0],
[-self.marker_size / 2, -self.marker_size / 2, 0]], dtype=np.float32)
rvecs = []
tvecs = []
for nook in corners:
retval, rvec, tvec = cv2.solvePnP(marker_points, nook, self.mtx, self.dist, False,
cv2.SOLVEPNP_IPPE_SQUARE)
if retval:
rvecs.append(rvec)
tvecs.append(tvec)

rvecs = np.array(rvecs)
tvecs = np.array(tvecs)
(rvecs - tvecs).any()
return rvecs, tvecs

 

The steps above full the identification and acquisition of the item’s info, and at last, the item’s coordinates are returned to the robotic arm to execute the greedy.

Robotic Arm Motion and Greedy Operation

Based mostly on the place of the Aruco marker, calculate the goal coordinates the robotic arm wants to maneuver to and convert the place right into a coordinate system appropriate for the robotic arm.

def homo_transform_matrix(x, y, z, rx, ry, rz, order="ZYX"):
rot_mat = rotation_matrix(rx, ry, rz, order=order)
trans_vec = np.array([[x, y, z, 1]]).T
mat = np.vstack([rot_mat, np.zeros((1, 3))])
mat = np.hstack([mat, trans_vec])
return mat

If the Z-axis place is detected as too excessive, it is going to be corrected:

if end_effector_z_height is just not None:  
p_base[2] = end_effector_z_height

After the coordinate correction is accomplished, the robotic arm will transfer to the goal place.

# Concatenate x, y, z, and the present posture into a brand new array
new_coords = np.concatenate([p_base, curr_rotation[3:]]) 
xy_coords = new_coords.copy()

Then, management the tip effector’s API to suction the item.

The above completes the respective features of the 2 robots. Subsequent, they are going to be built-in into the ROS atmosphere.

#Initialize the coordinates of level A and B
    goal_1 = [(2.060220241546631,-2.2297520637512207,0.009794792000444471,0.9999520298742676)] #B
    goal_2 = [(1.1215190887451172,-0.002757132053375244,-0.7129997613218174,0.7011642748707548)] #A
    #Begin navigation and hyperlink the robotic arm
    map_navigation = MapNavigation()
    arm = VisualGrasping("10.42.0.203",9000)
    print("join profitable")

    arm.perform_visual_grasp(1,-89)
    # Navigate to location A and carry out the duty
        for aim in goal_1:
        x_goal, y_goal, orientation_z, orientation_w = aim
        flag_feed_goalReached = map_navigation.moveToGoal(x_goal, y_goal, orientation_z, orientation_w)
        if flag_feed_goalReached:
            time.sleep(1)
            # executing 1 seize and setting the tip effector's Z-axis top to -93.
            arm.unload()
            print("command accomplished")
        else:
            print("failed")

4. Issues Encountered

Mapping State of affairs:

After we initially tried mapping with out enclosing the sector, frequent errors occurred throughout navigation and localization, and it failed to fulfill our necessities for a simulated situation.

Navigation State of affairs:

Within the new situation, one of many obstacles has a hole construction.

Throughout navigation from level A to level B, LIMO might fail to detect this impediment and assume it could cross by, damaging the unique impediment. This difficulty arises as a result of LIMO’s radar is positioned low, scanning solely the empty area. Doable options embody adjusting the radar’s scanning vary, which requires intensive testing for fine-tuning, or adjusting the radar’s top to make sure the impediment is acknowledged as impassable.

Robotic Arm Greedy State of affairs:

Within the video, it’s evident that our goal object is positioned on a flat floor. The greedy didn’t contemplate impediment avoidance for the item. Sooner or later, when setting particular positions for greedy, this case must be thought of.

5. Conclusion

General, LIMO Cobot carried out excellently on this situation, efficiently assembly the necessities. The whole simulated situation lined a number of core areas of robotics, together with movement management of the robotic arm, path planning, machine imaginative and prescient recognition and greedy, and radar mapping navigation and fixed-point cruising features of the cellular chassis. By integrating these useful modules in ROS, we constructed an environment friendly automated course of, showcasing LIMO Cobot’s broad adaptability and superior capabilities in advanced environments.

Credit

Elephant Robotics

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