Manipulation and Grasping

 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:

            Introduction

Robotics and Automation

Sensors-Perception-Programming-Applications

Principles of Robotics and Automation

Hardware & Software in Robotics

sensing and perception

motion planning and control

manipulation and grasping

navigation and mapping

Human-robot interaction

Advanced topics

Applications of robotics

Questions and Answers

Research