An automated guided vehicle is the most famous robot used in foreign companies to transport automatic products.. History The history of Automation Guide Vehicles AGVs traces back to the
INTRODUCTION
History
The history of Automation Guide Vehicles (AGVs) traces back to the mid-20th century, with the development of early automated material handling systems Here's a brief overview of key milestones in the history of AGVs:
1950s: The concept of AGVs began to emerge in the 1950s, primarily driven by the need for automation in manufacturing and material handling industries Early AGVs were simple vehicles guided by wires embedded in the floor or by magnetic tape
1960s: AGV technology continued to evolve in the 1960s, with the introduction of more sophisticated guidance systems such as radio frequency (RF) and optical navigation These advancements enable AGVs to navigate more complex environments and perform a wider range of tasks in industrial settings
1970s: The 1970s saw further advancements in AGV technology, including the development of onboard computers and sensors for obstacle detection and navigation AGVs began to gain traction in industries such as automotive manufacturing, where they were used for parts delivery and assembly line logistics
1980s: During the 1980s, AGVs became more widespread in industries such as warehousing, distribution, and logistics The introduction of laser guidance systems allows AGVs to navigate without the need for physical infrastructure like wires or tapes, making them more flexible and adaptable to changing environments
1990s: In the 1990s, AGV technology continued to mature, with improvements in sensors, software, and communication systems AGVs became more integrated with other automation technologies such as robotics and warehouse management systems, enabling seamless operation in complex industrial environments
2000s: The 21st century brought significant advancements in AGV capabilities, driven by innovations in artificial intelligence, machine learning, and sensor technology AGVs became smarter, more agile, and more autonomous, capable of performing a wide range of tasks with minimal human intervention
2010s and Beyond: In recent years, AGVs have seen widespread adoption across various industries worldwide, driven by the need for increased efficiency, productivity, and safety Today's AGVs are equipped with advanced features such as real-time data analytics, predictive maintenance, and collaborative robotics, enabling them to revolutionize the way goods are transported and managed in the digital age
Overall, the history of AGVs reflects a trajectory of continuous innovation and technological advancement, with these vehicles evolving from simple guided carts to sophisticated autonomous systems that are reshaping the future of transportation, logistics, and manufacturing.
Introduction about AGV
An automatic guided vehicle system (AGVS) consists of one or more computer- controlled, wheel-based load carriers (normally battery-powered) that run on the plant or warehouse floor (or outdoors on a paved area) without the need for an onboard operator or driver (MHI)
An automated guided vehicle or automatic guided vehicle (AGV) is a mobile robot that follows markers or wires in the floor or uses vision or lasers They are most often used in industrial applications to move materials around a manufacturing facility or a warehouse
The term "automated guided vehicle" (AGV) is a general one that encompasses all transport systems capable of functioning without driver operation The term
"driverless" is often used in the context of automatic guided vehicles to describe industrial trucks, used primarily in manufacturing and distribution settings, that would conventionally have been driver-operated
A materials handling system that uses automated vehicles such as carts, pallets or trays which are programmed to move between different manufacturing and warehouse stations without a driver These systems are used to increase efficiency, decrease damage to goods and reduce overhead by limiting the number of employees required to complete the job
Each type of goods has different requirements when storing and transporting, so there are multiple shipping methods In the factory, there are usually 3 types of AGV, this type of AGV covers most transportation jobs
In simple terms, these robots are portable and autonomous cargo delivery systems that are able to travel around a warehouse or facilities performing different AGV navigation technologies These fascinating robots are mainly used for industrial transport of goods and heavy materials around warehouses or storage facilities
A unit load AGV is a powered, wheel based transport vehicle that carries a discrete load, such as an individual item or items contained on a pallet or in a tote or similar temporary storage medium
An Automatic Guided Forklift also known as ALT is a Self-Driving computer- controlled Forklift So a forklift moving around and transporting goods by its own without human intervention, it's just a driverless forklift It is the typical automated guided vehicle (agv) with forks The automated forklift trucks are increasingly becoming a must in manufacturing premises and warehouses where operations are highly standardized, repetitive, and easily accomplished without need of human intervention Forklift robots are widely used in warehouses for high rack management
Towing vehicles, or tugger automatic guided vehicles, pull one or more non- powered, load-carrying vehicles behind them in a train-like formation Sometimes called driverless trains, powered towing vehicles travel on wheels Tugger automatic guided vehicles are often used for transporting heavy loads over longer distances They may have several drop-off and pick-up stops along a defined path through a warehouse or factory
Structure of AGV system
AGV application
Automation Guide Vehicles (AGVs) have found application in various industries around the world, revolutionizing traditional methods of transportation, logistics, and manufacturing Here are some notable applications of AGVs across different sector
Manufacturing Industry: AGVs are extensively used in manufacturing plants for material handling, parts delivery, and assembly line operations These vehicles transport raw materials and components between workstations, increase efficiency and reduce the need for manual labor AGVs also facilitate just-in-time manufacturing processes, optimizing production schedules and minimizing inventory storage costs
Warehousing and Distribution Centers: AGVs play a crucial role in modern warehouses and distribution centers by automating inventory management, order picking, and goods transportation They navigate through aisles, locate items, and transport them to designated locations with precision and efficiency AGVs enable warehouses to operate around the clock, speeding up order fulfillment and reducing labor costs
Figure 1.6 Warehousing and Distribution AGV
Logistics and Supply Chain Management: In the logistics industry, AGVs streamline the movement of goods within warehouses, ports, and transportation hubs These vehicles are equipped with advanced navigation systems that enable them to navigate complex environments, avoid obstacles, and optimize route planning AGVs enhance supply chain efficiency by minimizing transit times, reducing errors, and
Figure 1.7 Logistic and Supply Chain AGV
Healthcare Facilities: AGVs are increasingly being deployed in hospitals and healthcare facilities to automate the delivery of medications, medical supplies, and equipment These vehicles transport items between different departments, such as pharmacy, laboratories, and patient rooms, while adhering to strict hygiene and safety protocols AGVs help healthcare providers streamline operations, reduce human errors, and improve patient care
Agriculture and Farming: AGVs are starting to find applications in agriculture for tasks such as crop monitoring, harvesting, and transportation These vehicles can navigate fields autonomously, identify ripe crops, and collect produce without the need for human intervention AGVs have the potential to revolutionize farming practices by increasing efficiency, reducing labor costs, and minimizing environmental impact
Figure 1.9 Agriculture and Farming AGV
Overall, Automation Guide Vehicles have become indispensable tools for enhancing efficiency, productivity, and safety across various industries worldwide As technology continues to advance, we can expect AGVs to play an even greater role in reshaping the future of transportation and logistics.
Research nationally and internationally about AGV
During the epidemic, the globe has seen a tremendous increase in autonomous mobile robots As the globe prepares to reconnect in the post-pandemic era, autonomous mobile robots will play a role in industry, hospitality, and healthcare Automation has become more important in the retail, manufacturing, warehouse, and restaurant industries Automation has introduced varying degrees of autonomy into the picture
For example, in factories, autonomous mobile robots have taken over labor-intensive operations such as picking, sorting, and transporting These robots handle millions of components without the need for human intervention, enhancing material flow
In healthcare, robots can assist with duties such as cleaning and sterilization, medicine, food, and garbage distribution, and others when human presence is not required
The field of mobile robots with many navigation sensors and cameras is being studied by many domestic units Issues of high-speed image processing, multi-sensor coordination, spatial positioning and mapping, orbital motion design avoiding obstructions for mobile robots have been published in the National Mechatronics conferences in 2002, 2004, 2006, 2008 and 2010 Robotic Vision studies are of interest both in industrial robots and mobile robots, particularly in the field of robotic identification and control on the basis of visual information
Along with the construction of robots, the published scientific research on robots by Vietnamese scientists is very diverse and closely follows the research directions of the world Robotics studies in Vietnam are heavily involved in issues of kinetics, dynamics, orbital design, sensor information processing, actuation, control and intelligence development for robots Studies in robotic kinetics and dynamics are of interest both in the civil and military faculties of mechanics, machine-building at universities and research institutes in mechanics and machine-building
A few famous companies that manufacturing AGV, AMR in Viet Nam can be mentioned as: o Robot AGV Perbot Uniduc: Uniduc has successfully designed and applied AGV robotic systems in factories across the country This is also considered as one of the strengths of the company, with a variety of products with low to high loads, diverse design o AGV Yaskawa: since 2010 with 2 main products are towing rows and roaring robots Has an average load of 500kg speed of 10m/min o KuKa Group: more than 40 years of experience in the field of automation; in the production of AGV robots the company promotes product diversity o AGV Omron: With over 40 years of experience Omron's AGV has a wide selection of products loading from >500 kg which is quite large Speed 0.5, / s Battery selectable for model charging time 6h/8h/10h The clock and HMI display have a convenient button
Figure 1.10 Omron and KuKa AGV products
Object and scope of the study
Research on Automation Guide Vehicles (AGVs) in the field of freight transport is an area that is attracting the attention of researchers and freight experts around the world In this study, AGVs were surveyed and analyzed to understand the features, performance, and applications of each type in the freight transport process
Common types of AGVs in research include AGVs with sensor-guided positioning, self-guided AGVs with GPS, self-guided AGVs with laser scanners, and self-guided AGVs with cameras Each of these types of AGVs has its own advantages and limitations, and this study focuses on comparing and evaluating them to select the type of vehicle that best suits the specific requirements of each cargo transport process
The goal of this research is to provide detailed information and accurate analysis of AGVs for decision support in deploying and optimizing freight transportation systems By clearly understanding the advantages and disadvantages of each type of AGVs and applying them in specific freight transport environments, this research contributes to improving the efficiency and effectiveness of the freight transport process, while creating theoretical basis for the further development of AGVs technology in this field
Transporting large amounts of heavy products from one location to another will no longer be an issue with the assistance of an AGV The robot will be in charge of autonomously transporting big boxes across a large facility such as a warehouse or factory
Using Lidar technology, a way to measure distance by continuously emitted lasers to objects and recording the laser's response time to the receiver Once Lidar has completed the ambient scan, it sends data back to the computer using ROS to set up a map of the surrounding area and save it in the library We will also mark specific destinations on the map and save it
Then, just by selecting destinations that are already stored on the computer, ROS will provide the appropriate route to guide and send a signal to the raspberry to start the encoder engines are being controlled and the robot will begin to move to the destination.
Graduation project structure
This graduation project is divided into six sections, each of which has the following specific contents:
Chapter 1: INTRODUCTION - In this chapter our team present an overview of AGVs (Automation Guide Vehicles) and detail the research object and objectives of the report It will gain a foundational understanding of AGVs and the specific focus of the research conducted in the report That can expect to learn and explore in the subsequent sections of the chapter Overall, it serves as a roadmap for the chapter's content, guiding readers through the key topics and objectives to be covered
Chapter 2: FUNDAMENTAL THEORY - This chapter presents for the relevant theories and the applications to the topic
Chapter 3: CALCULATION DESIGN - This chapter presents for the method for choosing equipments and desgin the product
Chapter 4: IMPLEMENT, EXPERIMENT AND RESULT ANALYSES - This chapter presents starting the programme, collects the result number then evaluation
Chapter 5: CONCLUSION - This chapter is the summary of the topic and shows the product development directions in the future.
FUNDAMENTAL THEORY
Robot’s Kinematics and Robot’s Dynamics
Differential drive, often known as independent steering, is the driving system used by mobile robots The number of the robot's propulsion wheels determines how this system operates This mechanism typically comprises two wheels mounted on a single axle Each wheel includes a driving wheel to prevent the robot from overturning and may be moved separately forward or backward We can turn the robot left or right by varying the velocity of a wheel Additionally, the robot will revolve around an object on either side of the wheel axle The Instantaneous Center of Curvature, or ICC, is the name given to that place in space
Figure 2.1 Instantaneous Center of Curvature
When we change the speed of the two wheels, we will change the robot's trajectory
Since the angular speed around the ICC at 2 wheels is the same, we can write:
In which: l: distance hold 2 wheels
𝑅: distance from ICC to the midpoint between 2 wheels
𝑉𝑟 : is the right wheel velocity & 𝑉𝑙 is the left wheel velocity ω: is the angular velocity around the ICC
So that we can define 𝑅 and ω according to the formula:
We have 3 cases for this model as follows:
• 𝑉 𝑟 = 𝑉 𝑙 : the robot moves in a straight line linearly 𝑅 is infinite and 𝜔 = 0
• 𝑉 𝑟 = - 𝑉 𝑙 : 𝑅 = 0: robots rotate around the wheel axle midpoint – robots rotate in place
• 𝑉𝑟 = 0: The robot rotates towards the right wheel, now 1 Similarly, for 𝑉𝑙 2
0, the robot will rotate towards the left wheel → When one wheel rotates faster than the other, the robot will move towards the slow wheel
Represent the coordinate system of the robot where (𝑥 𝑅 , 𝑦 𝑅 ) is the global coordinate system, (𝑥 𝐿 , 𝑦 𝐿 ) is the local coordinate system attached to the car center
At the mass center, which is situated in the middle of the robot, the robot symmetrically crosses the axis perpendicular to the wheel axis Along with two passive front and rear wheels that aid in the robot's steady motion on a level surface, the robot is equipped with two driving wheels that are fastened to the axle and are each separately operated by two engines
The robot's system model is depicted in the figure below, where r is the wheel radius and L is the distance between two wheels This model has two limitations: Monday is a non-vertical sliding robot, and there is no side sliding between two active wheels and the ground The following equation describes these two constraints:
In which, (𝑥𝑐, 𝑦𝑐) is the coordinates of the robot's center, 𝜃 is the angle between the robot's x axis and the global x axis, 𝜑𝑟, 𝜑𝑙 is the angle position of the cake on the right and the cake on the left
𝑀(𝑞)𝑞 + 𝐶(𝑞) + 𝐺(𝑞) + 𝜏𝑑 = 𝐵(𝑞)𝜏 + 𝐴 𝑇 (𝑞)𝜏 (10) Where M (q) is inertia, is the matrix containing the terms centrifugal and Coriolis
G(q) is the gravity matrix B(q) is the input variable matrix τ is the input moment s the gravity matrix B(q) is the input variable matrix
𝐴𝑇(q) is the Jacobian matrix associated with constraints λ is the binding force vector q is the state vector representing the general coordinates
B(q) 1 (cos(cos 𝛳) cos(cos 𝛳) sin(sin𝛳) sin(sin𝛳 1 - 1)) (13)
Where m is the mass of the robot, 𝜏𝑙 and 𝜏𝑟 is the inertial torque generated in turn by 2 wheels left and right When mobile robots are bound in fixed spaces:
SLAM TECHNOLOGY (Simultaneous Localization and Mapping)
The method our team used to create maps using ROS, the algorithm is SLAM (Simultaneous Localization and Mapping)
SLAM (Simultaneous Localization and Mapping) is an algorithm for self-driving cars that can create a map and simultaneously locate the vehicle within that map Using SLAM methods, cars can map unknown environments Engineers use map data to perform tasks such as route planning and obstacle avoidance
For example, imagine a household robot vacuum cleaner Without SLAM, it moves randomly around the room and may not be able to clean the entire floor surface This method also uses a lot of power, which drains your battery faster Robots using SLAM, on the other hand, can use information such as wheel rotation speed and data from image sensors such as cameras to calculate the amount of movement required This is called localization The robot can also use cameras and other sensors simultaneously to create a map of obstacles around it to avoid repeatedly cleaning the same area This is called mapping
Figure 2.4 Benefits of SLAM for Cleaning Robots
SLAM is useful in many other applications such as navigating a fleet of mobile robots to arrange shelves in a warehouse, parking a self-driving car in an empty spot, or delivering a package by navigating a drone in an unknown environment MATLAB and Simulink provide SLAM algorithms, functions, and analysis tools to develop various applications You can implement simultaneous localization and mapping along with other tasks such as sensor fusion, object tracking, path planning, and path following
SLAM works by combining data from multiple sensors to create a map of an environment and to determine the robot’s location within that map The sensors used can vary depending on the type of robot and the environment it’s navigating through For example, a robot navigating through an indoor environment might use cameras and lidar, while a robot navigating through an outdoor environment might use GPS and sonar
The data collected by the sensors is processed and combined using algorithms that create a map of the environment and determine the robot’s location within that map This process is complex and requires a high degree of computational power, but recent advances in machine learning and artificial intelligence have made it easier and more efficient
Broadly speaking, there are two types of technology components used to achieve SLAM The first type is sensor signal processing, including front-end processing, which is largely dependent on the sensors used The second type is pose-graph optimization, including the back-end processing, which is sensor-agnostic
As the name suggests, visual SLAM (or vSLAM) uses images acquired from cameras and other image sensors Visual SLAM can use simple cameras (wide angle, fish-eye, and spherical cameras), compound eye cameras (stereo and multi cameras), and RGB-D cameras (depth and ToF cameras)
Visual SLAM can be implemented at low cost with relatively inexpensive cameras In addition, since cameras provide a large volume of information, they can be used to detect landmarks (previously measured positions) Landmark detection can also be combined with graph-based optimization, achieving flexibility in SLAM implementation
Monocular SLAM is when vSLAM uses a single camera as the only sensor, which makes it challenging to define depth This can be solved by either detecting AR markers, checkerboards, or other known objects in the image for localization or by fusing the camera information with another sensor such as inertial measurement units (IMUs), which can measure physical quantities such as velocity and orientation Technology related to vSLAM includes structure from motion (SfM), visual odometry, and bundle adjustment
Visual SLAM algorithms can be broadly classified into two categories Sparse
SLAM Dense methods use the overall brightness of images and use algorithms such as DTAM, LSD-SLAM, DSO, and SVO
Figure 2.7 Point cloud registration for RGB-D SLAM
Light detection and ranging (lidar) is a method that primarily uses a laser sensor (or distance sensor)
Compared to cameras, ToF, and other sensors, lasers are significantly more precise and are used for applications with high-speed moving vehicles such as self- driving cars and drones The output values from laser sensors are generally 2D (x, y) or
3D (x, y, z) point cloud data The laser sensor point cloud provides high-precision distance measurements and works very effectively for map construction with SLAM Generally, movement is estimated sequentially by matching the point clouds The calculated movement (traveled distance) is used for localizing the vehicle For lidar point cloud matching, registration algorithms such as iterative closest point (ICP) and normal distributions transform (NDT) algorithms are used 2D or 3D point cloud maps can be represented as a grid map or voxel map
On the other hand, point clouds are not as finely detailed as images in terms of density and do not always provide sufficient features for matching For example, in places where there are few obstacles, it is difficult to align the point clouds and this may result in losing track of the vehicle's location In addition, point cloud matching generally requires high processing power, so it is necessary to optimize the processes to improve speed Due to these challenges, localization for autonomous vehicles may involve fusing other measurement results such as wheel odometry, global navigation satellite system (GNSS), and IMU data For applications such as warehouse robots, 2D lidar SLAM is commonly used, whereas SLAM using 3-D lidar point clouds can be used for UAVs and automated driving
Although SLAM is used for some practical applications, several technical challenges prevent more general-purpose adoption Each has a countermeasure that can help overcome the obstacle
• Localization errors accumulate, causing substantial deviation from actual values
SLAM estimates continuous motion, which is subject to some degree of error Errors accumulate over time and lead to significant deviations from the actual value Additionally, map data may become corrupted or distorted, making further exploration difficult Taking care of the case of driving along a square aisle as an example As errors accumulate, the robot's start and end points will no longer match This is called the loop closure problem
It is important to be aware of loop closures and determine how to correct or cancel the accumulated errors The solution is to remember some features of previously visited places as landmarks to minimize the localization error Pose diagrams are created to correct errors By solving error minimization as an optimization problem, more accurate map data can be generated This type of optimization is called bundle adjustment in Visual SLAM
• Localization fails and the position on the map
Imagery and point cloud mapping do not take into account the robot's motion characteristics In some cases, this approach can produce discontinuous position estimates
This type of localization error can be prevented by using recovery algorithms or by fusing the motion model with multiple sensors and performing computations based on sensor data There are several ways to use motion models with sensor fusion
Path Planning with ROS
This project uses ROS to control LiDAR sensors, scan and map the environment, and set fixed targets to create closed working routes for AGVs ROS is installed on a personal laptop with the main operating system Ubuntu and connected to LiDAR (map scanning) and Arduino (motor control)
ROS (Robot Operating System) is an open-source framework that helps researchers and developers build and reuse code between robotic applications It is also a set of tools, libraries, and protocols designed to make it easier to create complicated and resilient robot behavior across a wide range of automated systems
ROS is a software package that enables the rapid and easy development of autonomous robotic systems ROS should be viewed as a set of tools for developing new solutions or modifying old ones This system has many drivers and developed algorithms that are frequently used in automation robots Components of the ROS system include
Nodes: which represent one process running the ROS graph Nodes oversee the management of devices or computing technology, and each node performs specific tasks Topics or services can be used for communication between nodes ROS software comes packaged A single package is typically created to perform a single type of operation and can span one or more nodes
Topics: In ROS, topics are streams of data that nodes use to exchange information These are used to send repeated messages of the same type This can be a sensor reading or motor speed
Each subject is registered with a unique name and message type Nodes can connect to it to publish or subscribe to messages A node cannot publish and subscribe to a particular topic at the same time, but there is no limit to the number of different nodes that can publish or subscribe to that topic
Services: Service communication is similar to a client/server approach In this configuration, nodes (servers) register services with the system Other nodes can then request this service and receive responses Unlike topics, services can also include data in requests, allowing two-way communication
Parameter server: A parameter server is a database shared between nodes that allows shared access to static or semi-static information Data that does not change frequently and is accessed infrequently, such as the distance between her two fixed points in the environment or the weight of the robot, is a good candidate to store in a parameter server
Path planning for a mobile robot involves determining the sequence of operations collisions with objects Path planning algorithms include Dijkstra's algorithm, A* or A- star, D* or dynamic A*, artificial potential field methods, and visibility graph methods Path planning algorithms can be based on graphs or occupancy grids
Diagram-based methods show where the robot is and how it can move between different locations In this format, vertices represent locations, such as rooms in a building, and edges define paths between vertices, such as doors that connect rooms
Additionally, each edge can be assigned a weight that indicates the complexity of the travel path, such as the width of the door or the energy required to open it The trajectory is determined by determining the shortest path between two vertices One of the vertices is the robot's current position and the other is the target.
Dynamic Window Approach (DWA)
The Dynamic Window Approach (DWA)’s method works following the principle:
1 Discretely test within the robot's control space (dx,dy,dtheta)
2 For each inspected speed, perform a forward reenactment from the robot's current state to foresee what would happen on the off chance that the inspected speed was connected for a few (brief) period of time
3 Assess (score) each direction coming about from the forward reenactment, employing a metric that joins characteristics such as nearness to deterrents, nearness to the objective, vicinity to the worldwide way, and speed Dispose of unlawful directions (those that collide with obstacles)
4 Choose the highest-scoring direction and send the related speed to the versatile base
The goal of DWA is to produce a (v, ω) pair which represents a circular trajectory that is optimal for robot’s local condition DWA reaches this goal by searching the velocity space in the next time interval The velocities in this space are restricted to be admissible,which means the robot must be able to stop before reaching the closest obstacle on the circular trajectory dictated by these admissible velocities Also, DWA will only consider velocities within a dynamic window, which is defined to be the set of velocity pairs that is reachable within the next time interval given the current translational and rotational velocities and accelerations
The Energetic Window Approach has two essential objectives: to compute a substantial speed look space and to select the finest speed The look space is built from the set of speeds that result in a secure direction (i.e., empower the robot to halt some time recently colliding), given the set of velocities that the robot can accomplish within the following time cut given its elements ('dynamic window') The perfect speed is utilized to optimize the robot's clearance, velocity, and heading closest to the target
So in DWA we are going have a few basic step to discover the most excellent speed to make direction for our robot:
1 To begin with, depending on our show position and the target, we may compute the required speed to the objective (For case, in case we are distant absent, travel quickly; in the event that we are near, go gradually)
2 Given the vehicle elements, select the passable speeds (direct 'v' and precise 'w')
3 Go through all of the conceivable speeds
4 Decide the closest deterrent for the arranged robot speed for each speed (i.e., collision discovery along the direction) for each speed
5 Decide in the event that the remove between the robot and the closest impediment is inside the robot's breaking distance In case the robot cannot halt in time, dispose of the proposed robot speed
6 In case the speed is 'acceptable,' we may presently calculate the values for the objective work In this situation, we're talking almost the robots' course and clearance
7 Decide the 'cost' of the recommended speed Set this as our best alternative in the event that the taken a toll is lower than anything else in this way distant
Finally, set the expecting direction of the robot to the best recommended speed.
Control Theory
Control theory in the context of AGVs (Automation Guide Vehicles) is a fundamental aspect of designing and implementing these autonomous vehicles Control theory involves the development of algorithms and systems that govern the behavior and movement of AGVs, ensuring they operate efficiently, safely, and effectively in various environments Key components of control theory applied to AGVs include
Control algorithms are used to plan optimal paths for AGVs to navigate from one point to another while avoiding obstacles and adhering to predefined constraints This involves techniques such as dynamic path planning, trajectory optimization, and collision avoidance
Feedback control systems continuously monitor the AGV's position, orientation, and velocity and adjust control inputs in real-time to maintain desired performance This involves sensors, such as encoders, cameras, and lidar, providing feedback to the control system for error correction and stabilization
The PID Controller is a mechanism used in feedback control loops to maintain a process parameter at a certain level automatically About 90% of all automatic control systems have this universal mechanism
In simple terms, the PID algorithm regulates a process variable by calculating a control signal that is the sum of three terms: proportional, integral, and derivative Hence its name As a result, it can return a process variable into the acceptable range
For self-driving cars equipped with microcontrollers, ultrasonic sensors, and motor controllers, the problem is how to keep the car running in a straight line
→ The correct choice to solve this problem is to use the PID algorithm
The role of components in PID controller:
• P (Proportional Response): tuning involves correcting a target proportional to the difference Thus, the target value is never achieved because as the difference approaches zero, so too does the applied correction
• I (Integral Response): The integral component accumulates the error term over time This gradual accumulation ensures that even small errors contribute to its increase Its purpose is to steadily diminish steady-state error
• D (Derivative Response): If the process variable increases quickly, the derivative component makes the output fall The derivative response varies in proportion to the process variable's rate of change
The principle of preventing this vibration is to gradually reduce the change in engine speed (i.e reduce engine acceleration) as the engine speed reaches the required point It also means that the change in error decreases as the engine speed approaches the required speed That change is calculated according to the formula: ∆𝑡 = 𝑒 − 𝑒𝑡 With e being the error at present and et is the error at the previous time
Figure 2.16 PID controller function parameter
Crystal formula pulse width changes as follows:
Adaptive control techniques are employed to adjust control parameters and strategies based on changing environmental conditions, such as varying terrain, obstacles, or payload This enables AGVs to adapt their behavior and optimize performance in real-time
Based on the principle of work, they are divided into 2 types:
• Friction transmission: belt transmission, friction gear,…
• Joint feeding transmission: gears, screws,…
A belt drive is a frictional drive that transmits power between two or more shafts using pulleys and an elastic belt In most cases, it is powered by friction but it may also be a positive drive It can operate at wide ranges of speed and power requirements It is also highly efficient
• Structure: o Belt transmission includes active belt gear, passive belt gear, straps, stretch belt o Straps made of PE plastic or woven fabric Straps usually have 2 or more layers The contact layer is usually made from chromium, steel, cast iron to have a greater friction coefficient The remaining layer is usually plastic with little expansion, high tensile strength
A chain drive helps transmit mechanical power (rotational motion) in machines
A chain drive, as the name implies, is made up of a series of pin-jointed links covered by an endless chain wrapped around two or more sprockets The holes in the chain links fit over the sprocket teeth The chain wrapped around the shaft's sprocket revolves with the main mover, transferring mechanical power to the driven shaft by applying mechanical force to it
• Structure: master chain, passive chain, chain chain, and chain plate
Gear drive technology is used when you need to transmit considerable power over a short distance with a steady velocity ratio The mechanism of gear drives is simple–the teeth are cut on the blanks of the gear wheel and mesh with one another to transmit altered torque and rotation
CALCULATION DESIGN
Design requirements and transmission selection
Autonomous robots can transport up to 5 kilograms of mass in an area of a workshop according to pre-programmed and repeated routes every day
Robots need to be firmly designed, withstand the load on demand but still ensure the speed and flexibility required to transport goods
Moderate height, the robot does not need to be too big to easily dodge obstructions, fit design to suit the working environment in the workshop
Therefore, we set up this target parameter:
Robots that operate and move mainly in the workshop to transport goods will often have people passing back and forth, sometimes there will be tight places so there will need flexibility in the process of going straight, turn left or right We need to choose the right mechanisms for adjusting the speed and rotation of the motor so that the robot can run more optimally
4-wheel drive Simplest Coarse Yes
Center drive & casters Simple Precise Yes
Steer drive motor Medium Very precise No
Rear drive Rack Steer Complex Precise Difficult
→ So that our team decided choose the second option is 2 center drives and casters
Requirement: o Light, durable, good load capacity o Wheel diameter