Robots-Blog | Exploring Elephant Robotics LIMO Cobot

0
256


1. Introduction:

This article primarily introduces the sensible software of LIMO Cobot by Elephant Robotics in a simulated situation. You could have seen earlier posts about LIMO Cobot’s technical circumstances, A[LINK]B[LINK]. The purpose for writing one other associated article is that the unique testing atmosphere, whereas demonstrating fundamental performance, typically seems overly idealized and simplified when simulating real-world purposes. Therefore, we goal to make use of it in a extra operationally constant atmosphere and share a number of the points that arose at the moment.

2. Comparing the Old and New Scenarios:

First, let’s have a look at what the outdated and new situations are like.

Old Scenario: A easy setup with a couple of obstacles, comparatively common objects, and a discipline enclosed by limitations, roughly 1.5m*2m in measurement.

New Scenario: The new situation incorporates a greater diversity of obstacles of various shapes, together with a hollowed-out object within the center, simulating an actual atmosphere with highway steerage markers, parking areas, and extra. The measurement of the sphere is 3m*3m.

The change in atmosphere is critical for testing and demonstrating the comprehensiveness and applicability of our product.

3. Analysis of Practical Cases:

Next, let’s briefly introduce the general course of.

The course of is especially divided into three modules: one is the performance of LIMO PRO, the second is machine imaginative and prescient processing, and the third is the performance of the robotic arm. (For a extra detailed introduction, please see the earlier article https://robots-blog.com/2024/05/16/exploring-elephant-robotics-limo-cobot/.)

LIMO PRO is especially answerable for SLAM mapping, utilizing the gmapping algorithm to map the terrain, navigate, and finally obtain the operate of fixed-point patrol.

myCobot 280 M5 is primarily answerable for the duty of greedy objects. A digicam and a suction pump actuator are put in on the finish of the robotic arm. The digicam captures the true scene, and the picture is processed by the OpenCV algorithm to search out the coordinates of the goal object and carry out the greedy operation.

Overall course of:

1. LIMO performs mapping.⇛

2. Run the fixed-point cruising program.⇛

3. LIMO goes to level A ⇛ myCobot 280 performs the greedy operation ⇒ goes to level B ⇛ myCobot 280 performs the putting operation.

4. ↺ Repeat step 3 till there aren’t any goal objects, then terminate this system.

Next, let’s observe the sensible execution course of.

Mapping:

First, it’s essential to begin the radar by opening a brand new terminal and getting into the next command:

roslaunch limo_bringup limo_start.launch pub_odom_tf:=false

Then, begin the gmapping mapping algorithm by opening one other new terminal and getting into the command:

roslaunch limo_bringup limo_gmapping.launch

After profitable startup, the rviz visualization device will open, and you will note the interface as proven within the determine.

At this level, you’ll be able to change the controller to distant management mode to regulate the LIMO for mapping.

After developing the map, it’s essential to run the next instructions to save lots of the map to a specified listing:

1. Switch to the listing the place you need to save the map. Here, save the map to `~/agilex_ws/src/limo_ros/limo_bringup/maps/`. Enter the command within the terminal:

cd ~/agilex_ws/src/limo_ros/limo_bringup/maps/

2. After switching to `/agilex_ws/limo_bringup/maps`, proceed to enter the command within the terminal:

rosrun map_server map_saver -f map1

This course of went very easily. Let’s proceed by testing the navigation operate from level A to level B.

Navigation:

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

roslaunch limo_bringup limo_start.launch pub_odom_tf:=false

2. Start the navigation operate by getting 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 „2D Pose Estimate, “ then click on on the situation the place LIMO is on the map. After beginning navigation, you will see that that the form scanned by the laser doesn’t overlap with the map. You must 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 „2D Nav Goal“ and choose the vacation spot on the map for navigation.

The navigation check additionally proceeds easily.

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

Identifying and Acquiring the Pose of Aruco Codes

To exactly determine objects and acquire the place of the goal object, we processed Aruco codes. Before beginning, guarantee the precise parameters of the digicam are set.

Initialize the digicam parameters primarily based on the digicam 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 thing and estimate its pose to acquire the 3D place of the thing and output the place data.

def estimatePoseSingleMarkers(self, corners):
"""
This will 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 dimensions of the detected markers
mtx - is the digicam matrix
distortion - is the digicam distortion matrix
RETURN checklist of rvecs, tvecs, and trash (in order that it corresponds to the outdated 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 thing’s data, and eventually, the thing’s coordinates are returned to the robotic arm to execute the greedy.

Robotic Arm Movement and Grasping Operation

Based 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 just isn't 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 thing.

The above completes the respective features of the 2 robots. Next, 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
    #Start navigation and hyperlink the robotic arm
    map_navigation = MapNavigation()
    arm = VisibleGrasping("10.42.0.203",9000)
    print("join profitable")

    arm.perform_visual_grasp(1,-89)
    # Navigate to location A and carry out the duty
        for objective in goal_1:
        x_goal, y_goal, orientation_z, orientation_w = objective
        flag_feed_goalReached = map_navigation.transferToGoal(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 peak to -93.
            arm.unload()
            print("command accomplished")
        else:
            print("failed")

4. Problems Encountered

Mapping Situation:

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

Navigation Situation:

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

During navigation from level A to level B, LIMO could fail to detect this impediment and assume it will probably move by means of, damaging the unique impediment. This concern arises as a result of LIMO’s radar is positioned low, scanning solely the empty area. Possible options embrace adjusting the radar’s scanning vary, which requires in depth testing for fine-tuning, or adjusting the radar’s peak to make sure the impediment is acknowledged as impassable.

Robotic Arm Grasping Situation:

In the video, it’s evident that our goal object is positioned on a flat floor. The greedy didn’t think about impediment avoidance for the thing. In the long run, when setting particular positions for greedy, this example must be thought-about.

5. Conclusion

Overall, LIMO Cobot carried out excellently on this situation, efficiently assembly the necessities. The total 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 cell chassis. By integrating these purposeful modules in ROS, we constructed an environment friendly automated course of, showcasing LIMO Cobot’s broad adaptability and superior capabilities in advanced environments.

Credits

Elephant Robotics

Elephant Robotics



LEAVE A REPLY

Please enter your comment!
Please enter your name here