Robots-Weblog | Exploring Elephant Robotics LIMO Cobot

1. Introduction:

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

2. Evaluating the Outdated and New Eventualities:

First, let’s take a look at what the outdated and new situations are like.

Outdated State of affairs: A easy setup with a couple of obstacles, comparatively common objects, and a area enclosed by obstacles, roughly 1.5m*2m in measurement.

New State of affairs: The brand new situation incorporates a greater diversity of obstacles of various shapes, together with a hollowed-out object within the center, simulating an actual setting with street steering markers, parking areas, and extra. The dimensions of the sector is 3m*3m.

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

3. Evaluation of Sensible Circumstances:

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

The method 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 liable 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 liable for the duty of greedy objects. A digital camera and a suction pump actuator are put in on the finish of the robotic arm. The digital camera captures the actual scene, and the picture is processed by the OpenCV algorithm to seek out the coordinates of the goal object and carry out the greedy operation.

Total 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.

Subsequent, let’s comply with the sensible execution course of.

Mapping:

First, it is advisable 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 software will open, and you will notice 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 establishing the map, it is advisable to run the next instructions to save lots of the map to a specified listing:

1. Change to the listing the place you wish to save the map. Right 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. Begin 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 on „2D Pose Estimate, “ then click on on the placement the place LIMO is on the map. After beginning navigation, you will discover that the form scanned by the laser doesn’t overlap with the map. You want to 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 take a look at additionally proceeds easily.

Subsequent, we’ll 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 procure the place of the goal object, we processed Aruco codes. Earlier than beginning, guarantee the precise 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 data.

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 dimensions of the detected markers
mtx - is the digital camera matrix
distortion - is the digital camera 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 item’s data, and eventually, 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 will likely 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 top effector’s API to suction the item.

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

#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 objective in goal_1:
        x_goal, y_goal, orientation_z, orientation_w = objective
        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 top 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 may possibly cross by means of, damaging the unique impediment. This situation arises as a result of LIMO’s radar is positioned low, scanning solely the empty house. Potential 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 take into account impediment avoidance for the item. Sooner or later, when setting particular positions for greedy, this example must be thought-about.

5. Conclusion

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

Credit

Elephant Robotics