This article gives comprehensive information about automated guided vehicles (AGVs). Read further to learn:
Automated guided vehicles (AGV) or mobile robots are types of guided robotic systems that are not bounded by a fixed range of motion. Rather, it is self-contained and can move along a line, surface, or space. It differs from a robotic arm, which is attached to a base and composed of links and joints. In most instances, automated guided vehicles and robotic arms are combined. Automated guided vehicles serve as the mobile platform where robotic arms are attached to perform more versatile functions such as remote handling, telemanipulation, scanning, probing, and so on. AGVs are used in a wide range of applications such as manufacturing, warehousing, inspection, exploration, transportation, and military.
AGV systems are part of a broad branch of automation. The design of their controls is more complex than robotic arms. Aside from dealing with locomotion, there are aspects of navigation such as perception, localization, path planning, and motion control. Robotic arms usually operate in a controlled environment where their trajectory is already known or easily predicted. AGVs, on the other hand, must be able to traverse through the environment where initially unknown restrictions are present.
AGVs are generally used in logistics. Other functions such as exploration, inspection, and service robotics comprise only a fraction of the industry. Thus, automated guided vehicles are categorized according to their load and method of transport.
The paths AGVs follow are virtual and programmed by a vehicle’s integrator using specialized software. Some benefits of autonomous AGVs include flexibility (vehicles are quick to install and projects easy to modify) and scalability (entire fleets of AGVs can be easily added to existing natural feature navigation systems and operate seamlessly together on one site).
Tunneling AGVs pull a companion frame to a location where it drops off the companion frame. When it arrives at the location, it may pass over an RFID puck that triggers the AGVs next stop. Assembly AGVs are more efficient and cost-effective than forklift AGVs. This is because they only require a companion frame to deliver assembly parts or components.
Lasers and natural targeting navigation systems allow the AGV to see the interior of the trailer without the need for modifications to the dock, trailer, or dock area.
Navigation is the ability of the guided vehicle or mobile robot to determine its location and know autonomously the direction of where it should proceed while avoiding collisions and unsafe conditions. Navigation can be divided into four fundamental components: perception, localization, path planning, and motion control.
The concept of acquiring data for mobile robot navigation is broader and more complex than robotic arms. The perception of mobile robots, similar to robotic arms, is achieved using sensors. Its higher sophistication arises from its ability to measure and relate its position globally or over a wide range.
Sensors are classified according to two functional axes: proprioceptive/exteroceptive sensors and passive/active sensors. The two sets of sensors are classified by what they do and observe; proprioceptive/exteroceptive sensors monitor the AGV, while passive/active sensors send energy into the work environment.
The second functional axis is according to whether the sensor absorbs or emits energy from the environment. These are known as passive or active sensors. Passive sensors measure physical parameters from the environment by absorbing energy such as loads, electromagnetic waves, or vibration. Active sensors, on the other hand, perform measurements by first emitting energy into the environment and analyzing its reaction. An example is an active sonar which involves beaming an acoustic wave and analyzing its echo.
Sensors for AGVs are summarized below.
-gyroscope.jpg "AGV Micro-electromechanical System (MEMS) Gyroscope")
After gathering information either from the environment or a fixed reference frame, the robot evaluates the data to determine its position and orientation relative to the environment through a process known as localization. A robot‘s position and orientation can be determined through odometry (dead reckoning) or triangulation from fixed reference frames. In most cases, this is not enough, especially when high accuracy is required.
The environment often has unknown obstacles and restrictions that are constantly changing. In addition, the sensors and effectors have issues regarding accuracy and precision. To achieve full autonomy and proceed with the succeeding steps of navigation, mapping creates a model of the environment that the robot can use to determine not only its location, orientation, and goal. The robot must also be able to update information accurately and in real-time, a process known as Simultaneous Localization and Mapping (SLAM).
Path planning is the process of determining the sequence of actions needed to be performed by the robot to reach its destination. This is the robot‘s cognition process, where it analyzes the map of the environment and creates output in the form of a program or instructions. In the event that some attributes in the map change, the robot must be able to measure these changes and adjust its actions accordingly. Moreover, the robot not only determines how to get to its target location but also optimizes its path by reducing the path length while avoiding obstacles.
In path planning, four different concepts need to be described. These are the robot geometry, the robot effectors' degrees of freedom, the map of the environment, and the initial and target configurations. To solve the robot‘s path planning, these four concepts must be translated into what is known as the configuration space. In the configuration space, the possible configurations of the robot and the space occupied by the obstacles are represented. The robot in the configuration space can be represented as a point defined by coordinate vectors instead of a rigid body. By reducing the robot to a point, the obstacles are somehow inflated by the size of the robot to compensate. Knowing the possible configurations of all objects in the map, a robot‘s trajectory can be determined, which corresponds to a continuous curve or path.
Motion control is the ability of the robot to execute its planned or programmed sequence of actions by feeding input signals to its drivers, actuators, and effectors. For mobile robots, the control system is typically a closed loop. The most common closed-loop control used in robotics is Proportional-Integral-Derivative (PID) control, a form of feedback control. Feedback control allows the robot to correct any disturbances or errors to its trajectory by continuously measuring parameters internally and externally. Using a PID controller mathematically represents the error signal and the proportional, integral, and derivative gains. These three factors allow the controller to quickly eliminate the error while maintaining a stable signal and avoiding overshoot.
Zone blocking is controlled by a central AGV system controller where only one AGV is allowed into any given zone. It is used in sections of the guide path where there are intersections, stations, and turns. The process allows multiple vehicles to be released into high traffic intersections and prevents interference between vehicles.
With accumulative blocking, AGVs are not controlled by a central system but by remote object detection sensors. It is a process used for long, straight sections of guide paths where AGVs sense the approach of a slower AGV or one that has stopped along the guide path. With accumulative blocking, AGVs tend to accumulate or collect behind each other but move up to the next intersection. The process is faster and more efficient than zone blocking.
Integrating the processes of perception, localization, path planning, and motion control creates the whole navigation system. There are different types of navigation systems that can be created with the combinations of sensors, controllers, programs, and algorithms. Enumerated below are the most commonly used navigation systems for automated guided vehicles.
An example is an inductive guide track or, wire guidance system, which is made of a current-carrying conductor embedded into the ground or floor. To sectionalize the tracks, it is divided into segments that can be turned on or off. An alternating current flows through the wire, which generates electromagnetic waves that can be detected by the mounted sensors at the bottom of the automated guided vehicle. The sensor is composed of two coils. Currents are induced through these coils, which become analog signals fed to the feedback controller.
Other examples of physical guides are magnetic, metallic, and optical guide strips. These strips are mounted on the floor's surface and detected by magnetic, inductive, or optical proximity sensors at the bottom of the AGV. Magnetic proximity sensors operate through a physical phenomenon called the Hall effect, which allows them to detect magnetic materials. Inductive proximity sensors, on the other hand, are also active sensors that utilize the principle of electromagnetic induction to detect metallic materials. Optical sensors determine the path by detecting the recognizable features (color) of the tape. Physical guides are less expensive than wire guidance systems and can easily be reconfigured. However, they are not suitable in dirty and high-traffic areas.
Locomotion is the ability of automated guided vehicles to propel themselves from one point to another. A common mode of locomotion is the use of wheels. Wheels are widely used due to their simplicity and minimal friction loss. However, there are certain limitations for wheeled automated guided vehicles in environments with irregular and uneven surfaces. This is where legged robots are usually preferred.
Different types of wheels can be combined and configured to produce the necessary stability and maneuverability. A statically stable robot requires a minimum of two wheels. Two-wheel stability can be achieved by lowering the robot‘s center of mass below the wheel axle. For three or more wheels, stability is achieved by containing the center of mass within the polygon formed by the points of contact between the wheel and the ground. In terms of maneuverability, the usual configuration is a two-wheel drive with one or two undriven wheels for steering. A differential is used for maneuvering with fixed standard wheels or wheels with only one degree of freedom. Four-wheel drives also exist, which use two pairs of driven and steered standard wheels or four individually driven and steered Mecanum wheels.
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