[ad_1]
1. Introduction:
This text primarily introduces the sensible utility of LIMO Cobot by Elephant Robotics in a simulated situation. You might have seen earlier posts about LIMO Cobot’s technical circumstances, A[LINK], B[LINK]. The rationale for writing one other associated article is that the unique testing surroundings, whereas demonstrating primary performance, typically seems overly idealized and simplified when simulating real-world functions. Due to this fact, we purpose to make use of it in a extra operationally constant surroundings and share a few of the points that arose at the moment.
2. Evaluating the Outdated and New Situations:
First, let’s have a look at what the previous and new situations are like.
Outdated Situation: A easy setup with a couple of obstacles, comparatively common objects, and a subject enclosed by limitations, roughly 1.5m*2m in measurement.
New Situation: The brand new situation accommodates a greater diversity of obstacles of various shapes, together with a hollowed-out object within the center, simulating an actual surroundings with street steering markers, parking areas, and extra. The dimensions of the sphere is 3m*3m.
The change in surroundings 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 answerable for SLAM mapping, utilizing the gmapping algorithm to map the terrain, navigate, and finally obtain the perform 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.
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 are not any goal objects, then terminate this system.
Subsequent, let’s comply with the sensible execution course of.
Mapping:
First, it’s essential 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 note the interface as proven within the determine.
At this level, you’ll be able to swap the controller to distant management mode to manage the LIMO for mapping.
After developing the map, it’s essential run the next instructions to save lots of the map to a specified listing:
1. Change to the listing the place you need 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 perform 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 perform 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 see 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 Purpose“ and choose the vacation spot on the map for navigation.
The navigation take a look at additionally proceeds easily.
Subsequent, we are going to transfer on to the half concerning the static robotic arm’s greedy perform.
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 digicam are set.
Initialize the digicam parameters based mostly on the digicam getting used.
def __init__(self, mtx: np.ndarray, dist: np.ndarray, marker_size: int):self.mtx = mtxself.dist = distself.marker_size = marker_sizeself.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):”””It 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 imagemarker_size – is the dimensions of the detected markersmtx – is the digicam matrixdistortion – is the digicam distortion matrixRETURN 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 data, and eventually, the item’s coordinates are returned to the robotic arm to execute the greedy.
Robotic Arm Motion and Greedy Operation
Primarily 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]]).Tmat = 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, will probably be corrected:
if end_effector_z_height shouldn’t be 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 arraynew_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 surroundings.
#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 purpose in goal_1:
x_goal, y_goal, orientation_z, orientation_w = purpose
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 Scenario:
Once we initially tried mapping with out enclosing the sphere, frequent errors occurred throughout navigation and localization, and it failed to fulfill our necessities for a simulated situation.
Navigation Scenario:
Within the new situation, one of many obstacles has a hole construction.
Throughout navigation from level A to level B, LIMO could fail to detect this impediment and assume it may cross by, damaging the unique impediment. This concern arises as a result of LIMO’s radar is positioned low, scanning solely the empty area. Attainable 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 Scenario:
Within 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 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. Your entire simulated situation coated 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 purposeful 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
Gefällt mir:Gefällt mir Wird geladen …
Ähnliche Beiträge
[ad_2]