# Designing and Operations of a 5-DOF Mentor Robotic Arm for Production and Manufacturing Engineering

## Designing and Operations of a 5-DOF Mentor Robotic Arm in Production and Manufacturing Engineering

## Abstract:

Designing and operating a mentor robotic arm in production and manufacturing engineering represents a significant leap in the pursuit of efficiency, precision, and automation within the industrial sector. This innovative technology, combining cutting-edge robotics and sophisticated engineering principles, transforms how products are manufactured, assembled, and tested. This 500-word exploration will explore the intricate world of mentor robotic arms, their design, and their pivotal role in modern manufacturing processes.

### Introduction:

The design of a mentor robotic arm begins with a meticulous understanding of the specific manufacturing tasks it will perform. These tasks range from welding and material handling to assembly and quality control. The arm’s configuration, size, range of motion, and end-effectors are all tailored to suit the requirements of the production line. Moreover, kinematics and dynamics simulations are crucial in optimizing the arm’s design, ensuring it can perform its tasks precisely and quickly.

Regarding structure, mentor robotic arms typically consist of multiple joints and links, resembling a human arm’s skeletal system. These joints are driven by powerful electric or hydraulic actuators, allowing for smooth and precise movement in multiple axes. The arm’s flexibility and agility are achieved through the careful arrangement of these joints, which can be revolute (rotational) or prismatic (linear).

One of the key design considerations is the end-effector, which is the tool or device attached to the arm’s terminal link. The end-effector is carefully selected or custom-designed to suit the specific manufacturing task. For instance, welding applications may include a welding torch, while in material handling, it could be a gripper or vacuum suction cup. The end-effector’s design directly impacts the arm’s versatility and functionality.

The operation of a mentor robotic arm involves a sophisticated interplay of hardware and software. A central computer or controller manages the arm’s movements and coordinates its actions. To enable precise control, sensors, such as encoders and cameras, provide real-time feedback on the arm’s position and the objects it interacts with. Advanced algorithms govern path planning, collision avoidance, and adaptive control, ensuring that the arm can adapt to changing conditions on the production floor.

One of the primary advantages of mentor robotic arms is their ability to work collaboratively with human operators. This human-robot interaction (HRI) is facilitated by advanced safety features, such as force-sensing technology, which allows the robot to detect unexpected contact with an operator and halt its movements to prevent injury. This collaborative aspect of mentor robotic arms makes them well-suited for applications where humans and robots share workspace and tasks.

The applications of mentor robotic arms are vast in manufacturing and production engineering. They excel in repetitive and precise tasks, where human labor may be less efficient or prone to errors. For example, in automotive assembly lines, these robots can effortlessly weld, assemble, and inspect vehicles with unparalleled accuracy and consistency. In electronics manufacturing, they handle delicate components with utmost precision, reducing defects and improving product quality.

The integration of mentor robotic arms in production and manufacturing engineering brings a myriad of benefits. They enhance productivity by operating 24/7 without fatigue, reduce production costs through error reduction, and improve workplace safety by handling hazardous or strenuous tasks. Moreover, they enable manufacturers to quickly adapt to changes in production volume or product variations, ensuring flexibility in today’s dynamic market.

In conclusion, the design and operation of mentor robotic arms represent a remarkable fusion of engineering prowess and automation technology. These versatile and precise machines are revolutionizing production and manufacturing engineering by streamlining processes, enhancing product quality, and ensuring workplace safety. As technology advances, mentor robotic arms will undoubtedly play an increasingly pivotal role in shaping the future of manufacturing industries across the globe.

## Designing and Operations of a Mentor Robotic Arm

### Problem statement

The arm tip of the Robot, called the end effector, has difficulty accurately reaching the desired target due to minor differences in angles of its other joints and more or less movement of its joints. This problem is related to the inverse kinematics of robots. Therefore, we will solve this inverse kinematics of the mentor arm robot after studying it

### Aim

- Make a telerobot to pick and carry the desired object after recognizing it using its visual sensor.
- The user can remotely control it.

### Objective

- We will study robotics and fabricate a mentor arm robot
- We will use a jaw as its end effector to hold things along with a camera

### Literature Review

We have studied different research papers about mentor arm robots and inverse kinematics.

The process used to identify the position of the Robot’s end effector and the angles of joints in the robot arm is called forward kinematics. This can be calculated through different techniques. One of these is the transformation matrix and Jacobi’s matrix.

The transformation matrix allows arbitrary linear transformation to be represented consistently. It can operate linear transformations like rotation and reflection.

Unlike forward kinematics(IK), there is another technique called inverse kinematics, which is related to calculating the size angles of joints of the robot arm and its end effector concerning the position of a given target. To obtain inverse kinematics is challenging, and different mathematical techniques are used to solve it.

We will use Jacobi’s matrix to solve the IK problem. The gradient technique is mainly used to solve the IK problem, but we preferred this technique. After a study of different research papers, we concluded that Jacobi’s method will be more accurate than the gradient method.

Tele-robotics is the study of Robots that a wireless system can remotely control. The internet can also control it. But we will use a wireless controller because it is better than the internet. As telerobot using the internet have a delay in actions. This delay depends on the following reasons.

- Time required to complete the previous command
- Time for request processing
- The amount of data sent and received by Robot
- Connection link speed

But a wireless controller will be more effective in this thing. We can cancel the previous command and initiate the new command. There will be no issue with connection speed and the amount of data sent and received.

## Methodology – Robotic Arm

### Theoretical studies

Here are some essential Points to understand the whole working of our Project that we have to cover in the Theoretical Studies. With the fundamental studies of these points, the Implementation of the Robot will be easy for us:

**Degree of Freedom:**

In statistics, the number of degrees of freedom is the number of values in the final statistics calculations that are free to vary. The number of parameters that determine a physical system’s state is vital to analyzing bodies’ systems in mechanical engineering and robotics engineering.

A single particle in a plane consists only of one degree of freedom, but it has six degrees of freedom for the point or a body in space.

Our Robot consists of 5 degrees of freedom mechanism. Where the distribution works as follows:

Sr. No |
Axis |
Body Parts |
Angular Momentum |

1 | Axis-0 | Waist | 210 Degrees |

2 | Axis-1 | Shoulder | 180 Degrees |

3 | Axis-2 | Elbow | 230 Degrees |

4 | Axis-3 | Left Wrist | 320 Degrees |

5 | Axis-4 | Right Wrist | 320 Degrees |

**Forward Kinematics:**

**Forward kinematics** refers to using the kinematic equations of a robot to compute the end-effector’s position from specified values for the joint parameters.

Educations for Forward Kinematics:

The kinematics equations for the series chain of a robot are obtained using a __rigid transformation__ [Z] to characterize the __relative movement__ allowed at each __joint__ and a separate rigid transformation [X] to define the dimensions of each link. The result is a sequence of rigid transformations alternating joint and link transformations from the base of the chain to its end link, which is equated to the specified position for the end link,

[ T ] = [ Z 1 ] [ X 1 ] [ Z 2 ] [ X 2 ] … [ X n − 1 ] [ Z n ] , {\displaystyle [T]=[Z_{1}][X_{1}][Z_{2}][X_{2}]\ldots [X_{n-1}][Z_{n}],\!} [**T] = [Z _{1}][X_{1}][Z_{2}][X_{2}]…[Zn][Xn-1]**

Where [T] is the transformation locating the end link. These equations are called the kinematics equations of the serial chain.

In our Project (Mentor Robotic Arm), the forward kinematics equations define the trajectory of the end-effector of a robot reaching for parts to do a specific action. The essential concept of forward kinematic animation is that the positions of particular parts of the model at a specified time are calculated from the position and orientation of the object, together with any information on the joints of an articulated model. So, for example, if the object to be animated is an arm with the shoulder remaining at a fixed location, the tip of the thumb would be calculated from the angles of the shoulder, elbow, wrist, thumb, and knuckle joints.

**Inverse Kinematics:**

**Inverse kinematics** refers to the use of the kinematics equations of a robot to determine the joint parameters that provide a desired position of the end-effector. Specification of the movement of a robot so that its end-effector achieves a desired task is known as motion planning. Inverse kinematics transforms the motion plan into joint actuator trajectories for the Robot.

An analytic solution to an inverse kinematics problem is a closed-form expression that takes the end-effector pose as input and gives joint positions as output; analytical inverse kinematics solvers can be significantly faster than numerical solvers and provide more than one solution for a given end-effector pose.

We have to solve the inverse kinematics equations to properly recognize the component’s position for properly working the Robot. There are different techniques to solve these inverse kinematics Equations, and the best approach is the Jacobi Method.

**Machine Vision (Object Recognition):**

**Machine vision** (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automated inspection, process control, and robot guidance in Industry. The scope of MV is broad. MV is related to, though distinct from, computer vision. Machine vision methods are defined as both the process of defining and creating an MV solution and as the technical process that occurs during the operation of the solution.

**Imaging:**

While conventional (2D visible light) imaging is most commonly used in MV, alternatives include imaging various infrared bands, line scan imaging, 3D imaging of surfaces, and X-ray imaging. The imaging device (e.g., camera) can either be separate from the main image processing unit or combined, in which case the combination is generally called a smart camera or smart sensor. When separated, the connection may be made to specialized intermediate hardware, a frame grabber using either a standardized (Camera Link, CoaXPress) or a custom interface.

**Image Processing:**

- After an image is acquired, it is processed. Machine vision image processing methods include. Here are some techniques and Steps for the image processing:
- Stitching/Registration:
- Filtering
- Threshold ing:
- Pixel counting:
- Segmentation:
- In
__painting__ - Edge
__detection__: - Color Analysis:
- Blob
__discovery & manipulation__ - Neural
__net__processing: - Pattern
__recognition__ - Barcode, Data Matrix, and “2D barcode” reading
- Optical
__character recognition__ - Gauging/Metrology
- Comparison against target values to determine a “pass or fail” or “go/no go” result.

**End Effector:**

In robotics, an **end effector** is a device at the end of a robotic arm designed to interact with the environment. The exact nature of this device depends on the application of the Robot.

End effectors may consist of a gripper or a tool. When referring to robotic prehension, there are four general categories of robot grippers, these are:

- Impactive – jaws or claws that physically grasp by direct impact upon the object.
- Ingressive – pins, needles, or hackles that physically penetrate the object’s surface (used in textile, carbon, and glass fiber handling).
- Astrictive–suction forces are applied to the object’s surface (whether by vacuum, magneto- or
__electro-adhesion)__. - Constitutive requires direct contact for adhesion (such as glue,
__surface tension,__or freezing).

**The force required to grip the object**

Though numerous forces are acting over the body that the robotic arm has lifted, the main force acting there is the frictional force. The gripping surface can be made of a soft material with a high coefficient of friction so that the object’s texture is not damaged. The robotic gripper must withstand not only the weight of the object but also acceleration and the motion caused by the object’s frequent movement. The following formula is used to find out the force required to grip the object.

W = μ F n {\displaystyle W=\mu Fn}

**W=ufn**

Where:

W {\displaystyle \,W} | W is | the force required to grip the object, |

μ {\displaystyle \,\mu } | u is | the coefficient of friction, |

n {\displaystyle \,n} | n is | the number of fingers in the gripper and |

F {\displaystyle \,F} | F is | The weight of the object. |

**Trajectory Generation (Path Planning):**

A trajectory in multidimensional space that describes the desired motion of a manipulator. A trajectory refers to a time history of position, velocity, and acceleration for each degree of freedom. This problem includes the human interface problem of specifying a trajectory or path through space. ln order to make the description of manipulator motion easy for a human user of a robot system, the user should not be required to write down complicated functions of space and time to specify the task.

Sometimes, it is necessary to specify the motion in more detail than simply stating the desired final configuration. One way to include more elements in a path description is to give a sequence of chosen via points or intermediate points between the initial and final positions. Thus, in completing the motion, the tool frame must pass through a set of middle positions and orientations described by the points.

The Basic purpose of trajectory generation is to create a smooth pathway for the Robot to do a specific task with a humanistic response. As the Robots move with the serial flow, they have very little attraction with their flow of doing and performing things. Therefore, Path Planning is crucial for working with a robot effectively and efficiently with a smooth flow like a human arm.

#### Experimental setup

We have a learning mentor arm robot for studying inverse kinematics we’ll use it to perform different experiments on it. We’ll also simulate this Robot in MATLAB and control it using Webots software.

### Methods of Analysis for Robotic Arm

We want to study Robot’s inverse kinematics using Jacobi’s Matrix.

Jacobian matrices are a helpful tool used throughout robotics and control theory. A Jacobian defines the dynamic relationship between two different representations of a system. For example, suppose we have a 2-link robotic arm. In that case, there are two prominent ways to describe its current position: 1) the end-effector position and orientation (which we will denote ), and 2) the set of joint angles (which we will denote ). The Jacobian for system relates how the movement of the elements causes the movement of the features. You can think of a Jacobian as a transform matrix for velocity.

Formally, a Jacobian is a set of partial differential equations:

With a bit of manipulation, we can get a neat result:

or

Where and represent the time derivatives of and. This tells us that the end-effector velocity equals the Jacobian, , multiplied by the joint angle velocity.

Why is this important? This relates to our desire to control operational (or task) space. We’re interested in planning a trajectory in a different space than the one we can control directly. In our robot arm, control is effected through motors that apply torque to the joint angles. Still, we’d like to plan our trajectory regarding end-effector position (and possibly orientation), generating control signals regarding forces to apply in space. Jacobians allow us a direct way to calculate the control signal in the space we control (torques), given a control signal in one we don’t (end-effector forces).

#### Results Expected

In the end, we’d have a mentor robot arm, which would be more accurate and precise in picking and placing the objects.

#### Utilization of Results

This effective and precise Robot can be used in the Industry for picking, dropping, and welding painting purposes.

**Work Schedule Plan**

Collection of literature Two Weeks

Study of Literature Two Weeks

Analysis of Proposed Scheme One Month

Preparation of Scheme/Model One Month

Implementation of Scheme/Model One Month

Analysis and Simulation One Month

Result Formulation Two Weeks

Final Write-up & Thesis Submission Two Weeks

### 6.1 Proposed Time Schedule

Activity | Jan
06 |
Feb
06 |
Mar 06 | Apr
06 |
May
06 |
Jun
06 |
||

Collection of Literature | ||||||||

Study of Literature | ||||||||

Analysis of the Proposed Scheme | ||||||||

Preparation of Schemes / Model | ||||||||

Implementation of Schemes/Model | ||||||||

Analysis & Simulation | ||||||||

Result Formulation | ||||||||

Final Write-up & Thesis Submission |

**Budget Description**

Our supervisor will provide experimental setup and funding.

**References**

[1] Samuel R. Buss, “Introduction to Inverse Kinematics with Jacobian Transpose, Pseudoinverse, and Damped Least Squares methods”, *Department of Mathematics University of California, San Diego La Jolla, CA 92093-0112 sbuss@math.ucsd.edu*

April 17, 2004

[2] Jirayus Chaichawananit and Saiyan Saiyod, “Solving Inverse Kinematics Problem of Robot Arm with Adjustable Snap-width A-Star Algorithm”, *Department of Computer Science, Faculty of Science Khon Kaen University Khon Kaen, Thailand **James.chaichawa@gmail.com**, **saiyan@kku.ac.th*

[3] Arthur Lismonde and Olivier Bruls and Valentin Sonneville, “Solving the inverse dynamics of a ﬂexible 3D robot for a trajectory tracking task”,

*Multibody and Mechatronic Systems Laboratory University of Liege Liege, Belgium Email: {alismonde, o.bruls}@ulg.ac.be*

*Aerospace Engineering University of Maryland College Park, MD, USA Email: **vspsonn@umd.edu*

[4] Jacobian method details are available online at https://studywolf.wordpress.com/2013/09/02/robot-control-jacobians-velocity-and-force/

[5] https://en.wikipedia.org/