6. Robot manipulation and grasping
• Robotic arms and grippers
• Kinematics and dynamics of manipulation
• Grasping and manipulation algorithms
Robot Manipulation and Grasping:
Robot manipulation and grasping are important aspects of robotics and automation that enable robots to interact with the environment and perform various tasks. Robot manipulation involves controlling the robot's arm and gripper to grasp and manipulate objects, while grasping and manipulation algorithms involve determining how to grasp an object and how to manipulate it to achieve a desired outcome.
Robotic arms and grippers:
Robotic arms and grippers are the primary means by which
robots interact with the environment. Robotic arms consist of a series of links
and joints that enable the arm to move and manipulate objects, while grippers
are devices that are attached to the end of the arm and used to grasp and hold
objects. There are many different types of robotic arms and grippers, including
Cartesian, cylindrical, and articulated arms, and parallel, suction, and
electric grippers.
Kinematics and dynamics of manipulation:
Kinematics and dynamics of manipulation involve
understanding how the robot's arm and gripper move and interact with objects.
Kinematics involves studying the motion of the robot's arm and gripper without
considering the forces that cause the motion, while dynamics involves studying the
motion of the robot's arm and gripper and the forces that cause the motion.
Some common kinematics and dynamics algorithms include forward kinematics,
inverse kinematics, and Jacobian matrices.
Example for Inverse Kinematics:
python code
import numpy as np
# Define robot arm link lengths
L1 = 1.0
L2 = 1.0
# Define target position and orientation
target_pos = np.array([1.0, 1.0])
target_orientation = 0.0
# Calculate inverse kinematics to determine joint angles
theta2 = np.arccos((target_pos[0]**2 + target_pos[1]**2 - L1**2 - L2**2) / (2 * L1 * L2))
theta1 = np.arctan2(target_pos[1], target_pos[0]) - np.arctan2((L2 * np.sin(theta2)), (L1 + L2 * np.cos(theta2)))
theta3 = target_orientation - theta1 - theta2
print("Joint angles:", theta1, theta2, theta3)
Grasping and manipulation algorithms:
Grasping and manipulation algorithms involve determining how
to grasp an object and how to manipulate it to achieve a desired outcome. Some
common grasping and manipulation algorithms include force-closure grasping, impedance
control, and reinforcement learning.
Example for Force-Closure Grasping:
python code
import numpy as np
# Define object pose and shape
object_pose = np.array([0.0, 0.0, 0.0])
object_shape = np.array([[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
# Define gripper pose and shape
gripper_pose = np.array([1.0, 1.0, 0.0])
gripper_shape = np.array([[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 0.5]])
# Check if force-closure grasping is possible
M = object_shape @ object_pose.T - gripper_shape @ gripper_pose.T
if np.all(np.linalg.eigvals(M + M.T) > 0):
print("Force-closure grasping is possible.")
else:
print("Force-closure grasping is not possible.")
Also Read:
Sensors-Perception-Programming-Applications
Principles of Robotics and Automation
