Glossary

12.3 Robotics and automation

13 min read•august 20, 2024

Robotics and blend control theory, engineering, and computer science to create intelligent machines. These systems perform tasks autonomously or with minimal human input, revolutionizing industries from manufacturing to healthcare and space exploration.

Robot components include , , control systems, and end effectors. Understanding , dynamics, and control is crucial for designing effective robots. , sensing, and programming enable robots to navigate and interact with their environment autonomously.

Robotics overview

  • Robotics is a multidisciplinary field that combines principles from control theory, mechanical engineering, electrical engineering, and computer science to design and develop intelligent machines capable of performing tasks autonomously or with minimal human intervention
  • The field of robotics has seen significant advancements in recent years, with robots being used in a wide range of applications, from manufacturing and assembly to healthcare and space exploration

Types of robots

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  • are used in manufacturing and assembly lines to perform repetitive tasks with high precision and speed (welding, painting, material handling)
  • Service robots assist humans in various tasks, such as cleaning, delivery, and customer service (vacuum cleaners, delivery drones, receptionist robots)
  • Mobile robots are designed to navigate and operate in different environments, including wheeled robots, legged robots, and aerial robots (Mars rovers, quadruped robots, drones)
  • Collaborative robots, or cobots, are designed to work safely alongside humans in shared workspaces (assembly tasks, quality inspection)

Robot components

  • Actuators are the motors or hydraulic/pneumatic systems that enable robot motion and force generation (electric motors, hydraulic cylinders, pneumatic actuators)
  • Sensors allow robots to perceive their environment and gather information for decision-making (encoders, force/torque sensors, cameras, LiDAR)
  • Control systems process sensor data, execute algorithms, and generate commands for actuators to achieve desired robot behavior (microcontrollers, embedded systems, industrial PCs)
  • End effectors are the tools or devices attached to the robot's arm to interact with the environment (grippers, suction cups, welding torches)

Degrees of freedom

  • (DOF) refer to the number of independent motions a robot can perform in its workspace
  • The number of DOF depends on the robot's mechanical design and the arrangement of its joints (revolute joints, prismatic joints)
  • A higher number of DOF allows for greater flexibility and dexterity in performing tasks but also increases the complexity of control and path planning
  • Industrial robots typically have 6 DOF, allowing them to position and orient their end effector in any desired pose within their workspace

Robot kinematics

  • is the study of robot motion without considering the forces and torques that cause the motion
  • Kinematics deals with the relationship between the robot's joint angles and the position and orientation of its end effector in the workspace

Forward kinematics

  • is the process of determining the position and orientation of the robot's end effector given the joint angles
  • The forward kinematics problem is solved using the robot's kinematic equations, which are derived based on the robot's mechanical structure and joint types
  • Homogeneous transformation matrices are commonly used to represent the relative positions and orientations between robot links and joints
  • The forward kinematics solution is unique for a given set of joint angles

Inverse kinematics

  • is the process of determining the joint angles required to achieve a desired position and orientation of the robot's end effector
  • The inverse kinematics problem is more complex than forward kinematics, as there may be multiple solutions or no solution at all for a given end effector pose
  • Numerical methods, such as the Jacobian-based approach or optimization techniques, are often used to solve the inverse kinematics problem
  • Redundant robots, which have more DOF than necessary for a task, can have an infinite number of inverse kinematics solutions

Denavit-Hartenberg parameters

  • The Denavit-Hartenberg (DH) convention is a systematic method for assigning coordinate frames to the links of a robot and describing their relative positions and orientations
  • DH parameters consist of four variables for each link: link length, link twist, joint offset, and joint angle
  • The DH convention simplifies the derivation of the robot's kinematic equations by following a set of rules for assigning coordinate frames
  • The DH parameters are used to construct the homogeneous transformation matrices between adjacent links, which can be concatenated to obtain the overall forward kinematics solution

Robot dynamics

  • is the study of the forces and torques that cause robot motion, taking into account the robot's mass, inertia, and external forces acting on it
  • Understanding robot dynamics is crucial for designing effective control systems and optimizing robot performance

Lagrangian formulation

  • The is an energy-based approach to deriving the for a robot
  • It considers the difference between the robot's kinetic energy and potential energy, called the Lagrangian
  • The Lagrangian equations of motion are obtained by applying the Euler-Lagrange equation to the Lagrangian function
  • This approach is well-suited for robots with complex structures and multiple degrees of freedom

Newton-Euler formulation

  • The is a recursive method for deriving the equations of motion based on Newton's laws of motion and Euler's equations for rigid body dynamics
  • It involves computing the forces and torques acting on each link of the robot, starting from the base and propagating towards the end effector
  • The Newton-Euler formulation is computationally efficient and suitable for real-time control applications
  • It provides a systematic way to account for the effects of gravity, friction, and external forces on the robot's motion

Equations of motion

  • The equations of motion describe the relationship between the joint torques and the resulting motion of the robot
  • They take the form of a set of second-order differential equations, relating the joint angles, velocities, and accelerations to the applied joint torques
  • The equations of motion include terms for the robot's mass matrix, Coriolis and centrifugal forces, gravity forces, and friction forces
  • These equations are used to design controllers that compute the required joint torques to achieve desired robot motions while compensating for dynamic effects

Robot control

  • involves designing algorithms and systems to regulate the motion and force output of a robot to achieve desired tasks
  • Control theory concepts, such as feedback control, stability analysis, and optimization, are applied to robot control problems

Joint space control

  • focuses on controlling the individual joint angles of the robot to track desired joint trajectories
  • PID (Proportional-Integral-Derivative) control is a common approach for joint space control, where the controller computes joint torques based on the error between the desired and actual joint angles
  • Feedforward control can be added to compensate for the robot's dynamics, such as gravity and friction, to improve tracking performance
  • Joint space control is suitable for tasks that primarily involve motion planning in the robot's joint space, such as point-to-point movements

Operational space control

  • , also known as Cartesian space control, focuses on controlling the position and orientation of the robot's end effector in the task space
  • The control law is formulated in the operational space, and the computed forces are then mapped to joint torques using the robot's Jacobian matrix
  • Operational space control allows for more intuitive specification of tasks and can handle constraints in the task space, such as obstacle avoidance
  • Examples of operational space control include impedance control, where the robot behaves like a mass-spring-damper system, and

Hybrid position/force control

  • Hybrid position/force control is a technique that allows a robot to simultaneously control the position and force of its end effector in different directions
  • The task space is decomposed into position-controlled and force-controlled subspaces, and separate control laws are applied to each subspace
  • This approach is useful for tasks that involve contact with the environment, such as assembly, grinding, or polishing
  • The control law ensures that the desired position trajectories are followed in the position-controlled directions while maintaining the desired contact forces in the force-controlled directions

Path planning

  • Path planning is the process of generating a collision-free path for a robot to move from its current configuration to a desired goal configuration in its workspace
  • It involves representing the robot's environment, identifying obstacles, and searching for feasible paths that satisfy certain criteria, such as shortest distance or minimum energy consumption

Configuration space

  • , or C-space, is a mathematical representation of all possible configurations (positions and orientations) that a robot can attain in its workspace
  • The dimensions of the C-space correspond to the degrees of freedom of the robot
  • Obstacles in the workspace are mapped into the C-space, creating regions that the robot must avoid
  • Path planning algorithms search for paths in the C-space that connect the start and goal configurations while avoiding obstacles

Sampling-based methods

  • are a class of path planning algorithms that rely on randomly sampling the C-space to build a graph or tree of feasible configurations
  • These methods are particularly effective for high-dimensional C-spaces and complex environments
  • Examples of sampling-based methods include Rapidly-exploring Random Trees (RRT), Probabilistic Roadmaps (PRM), and their variants
  • These algorithms incrementally expand the graph or tree by adding new configurations and connecting them to existing ones until a path is found

Optimization-based methods

  • formulate the path planning problem as an optimization problem, seeking to minimize a cost function while satisfying constraints
  • The cost function can represent various criteria, such as path length, energy consumption, or smoothness
  • Constraints include collision avoidance, joint limits, and dynamic feasibility
  • Examples of optimization-based methods include gradient-based techniques, such as sequential quadratic programming (SQP), and stochastic optimization, such as particle swarm optimization (PSO)
  • These methods can generate optimal or near-optimal paths but may be computationally expensive for high-dimensional problems

Sensing and perception

  • enable robots to gather information about their environment and internal states, which is essential for decision-making, control, and interaction
  • Sensors can be classified into , which measure the robot's internal states, and , which measure the external environment

Proprioceptive sensors

  • Proprioceptive sensors provide information about the robot's internal states, such as joint angles, velocities, and torques
  • Encoders are commonly used to measure joint angles and velocities by tracking the rotation of the robot's joints (optical encoders, magnetic encoders)
  • Force/torque sensors measure the forces and torques applied to the robot's joints or end effector, enabling force control and collision detection
  • Inertial Measurement Units (IMUs) combine accelerometers and gyroscopes to estimate the robot's orientation and motion in space

Exteroceptive sensors

  • Exteroceptive sensors gather information about the robot's external environment, such as the presence of objects, distances, and visual features
  • Cameras are widely used for visual perception, providing rich information about the environment (RGB cameras, depth cameras, stereo cameras)
  • LiDAR (Light Detection and Ranging) sensors use laser beams to measure distances and create 3D point clouds of the environment
  • Ultrasonic sensors and infrared sensors are used for proximity detection and obstacle avoidance
  • Tactile sensors, such as pressure-sensitive skin or capacitive sensors, enable robots to sense contact and forces when interacting with objects

Sensor fusion

  • is the process of combining data from multiple sensors to obtain a more accurate and comprehensive understanding of the environment
  • Probabilistic techniques, such as Kalman filters and particle filters, are commonly used for sensor fusion
  • These techniques estimate the robot's state (position, orientation, velocity) by integrating information from different sensors and accounting for their uncertainties
  • Sensor fusion can help overcome the limitations of individual sensors and provide a robust perception of the environment, even in the presence of noise or occlusions

Robot programming

  • Robot programming involves developing software and algorithms to control the behavior and actions of robots
  • It encompasses a wide range of tasks, from low-level motion control to high-level task planning and decision-making

Robot operating system (ROS)

  • The is an open-source framework for robot software development
  • ROS provides a set of libraries, tools, and conventions that simplify the process of creating complex robotic systems
  • It is based on a publish-subscribe architecture, where nodes (software modules) communicate with each other through messages published on topics
  • ROS supports a wide range of programming languages (C++, Python) and has a large community and ecosystem of packages for various robotic applications

Robot programming languages

  • Various programming languages are used for robot programming, depending on the level of abstraction and the specific requirements of the application
  • C++ is commonly used for low-level control and real-time applications due to its performance and direct access to hardware
  • Python is popular for high-level scripting, prototyping, and rapid development, thanks to its simplicity and extensive libraries
  • MATLAB and Simulink are widely used in academia and research for robot modeling, , and control design
  • Domain-specific languages, such as VAL (Variable Assembly Language) or RAPID, are used for industrial robot programming and provide a higher level of abstraction

Simulation environments

  • are software tools that allow developers to model, simulate, and test robot systems in a virtual environment before deploying them on physical robots
  • They provide a safe and cost-effective way to validate robot designs, algorithms, and control strategies
  • Popular robot simulation environments include Gazebo (integrated with ROS), V-REP (Virtual Robot Experimentation Platform), and Webots
  • These environments support physics-based simulation, sensor modeling, and realistic rendering, enabling developers to create accurate and detailed simulations of robotic systems

Industrial automation

  • refers to the use of robots, control systems, and information technologies to automate manufacturing processes and improve efficiency, quality, and safety
  • It involves the integration of various components, such as robots, sensors, actuators, and control systems, to create automated production lines and factories

Automated manufacturing systems

  • are designed to perform various tasks in the production process, such as material handling, machining, assembly, and inspection
  • They consist of robots, conveyor systems, automated guided vehicles (AGVs), and computer numerical control (CNC) machines
  • These systems are programmed to execute specific sequences of operations and can adapt to changes in product designs or production volumes
  • Examples of automated manufacturing systems include flexible manufacturing systems (FMS), which can handle a variety of parts and products, and transfer lines, which are dedicated to high-volume production of a single product

Programmable logic controllers (PLCs)

  • are industrial computers used to control and automate manufacturing processes
  • They are designed to be robust, reliable, and capable of operating in harsh industrial environments
  • PLCs execute programs in a cyclic manner, reading inputs from sensors and switches, processing the control logic, and updating outputs to actuators and machines
  • They are programmed using ladder logic, a graphical programming language that represents control logic as a series of relay contacts and coils
  • PLCs communicate with other devices, such as robots, sensors, and human-machine interfaces (HMIs), using industrial communication protocols (Modbus, Profibus, EtherNet/IP)

Supervisory control and data acquisition (SCADA)

  • systems are used to monitor and control large-scale industrial processes, such as manufacturing plants, power grids, and water distribution systems
  • They provide a centralized view of the entire system, allowing operators to monitor real-time data, detect anomalies, and issue control commands
  • SCADA systems consist of remote terminal units (RTUs) or PLCs that collect data from field devices, a communication network that transmits the data, and a central supervisory computer that processes and displays the information
  • They often incorporate graphical user interfaces (GUIs) and alarm systems to alert operators of critical events or deviations from normal operating conditions
  • SCADA systems enable remote monitoring and control of industrial processes, improving efficiency, safety, and decision-making

Mobile robotics

  • deals with the design, control, and applications of robots that can move and navigate in their environment
  • Mobile robots are used in a wide range of applications, such as exploration, surveillance, transportation, and service robotics

Wheeled vs legged robots

  • Wheeled robots are the most common type of mobile robots, using wheels or tracks for locomotion
  • They are relatively simple to design and control, and can achieve high speeds and efficiency on flat and smooth surfaces (differential drive robots, car-like robots, omnidirectional robots)
  • Legged robots, on the other hand, use articulated legs for locomotion, mimicking the movement of animals or humans
  • They can navigate uneven and rough terrains, climb stairs, and adapt to different environments (biped robots, quadruped robots, hexapod robots)
  • Legged robots are more complex to design and control than wheeled robots, requiring advanced algorithms for gait generation, balance, and stability control

Localization and mapping

  • Localization is the process of estimating a mobile robot's position and orientation within its environment
  • It is a crucial capability for , as the robot needs to know where it is to plan its motions and actions
  • Common localization techniques include odometry (estimating motion based on wheel encoders), inertial navigation (using IMUs), and landmark-based methods (detecting and matching visual or geometric features)
  • Mapping refers to the process of creating a representation of the robot's environment, such as an occupancy grid map or a topological map
  • Simultaneous (SLAM) is a popular approach that allows a robot to build a map of an unknown environment while simultaneously localizing itself within that map

Autonomous navigation

  • Autonomous navigation enables mobile robots to plan and execute paths to reach desired goals while avoiding obstacles and adapting to changes in the environment
  • It involves several components, such as perception (sensing and interpreting the environment), localization, mapping, path planning, and motion control
  • Navigation strategies can be classified into reactive (making decisions based on current sensor data) and deliberative (planning paths based on a global map)
  • Examples of autonomous navigation algorithms include potential field methods, graph-based search (A*, D*), and sampling-based planners (RRT

Key Terms to Review (49)

Actuators: Actuators are devices that convert energy into motion, enabling systems to perform specific actions. They play a crucial role in various applications, driving movements in mechanical systems and controlling various functions, from robotic arms to aircraft controls. Understanding actuators is essential for grasping how automation and aerospace technologies operate, as they serve as the interface between control signals and physical movement.
Automated manufacturing systems: Automated manufacturing systems refer to the use of technology, particularly robotics and computer-controlled machinery, to produce goods with minimal human intervention. These systems enhance productivity, improve product quality, and reduce manufacturing costs by integrating various automation technologies such as robotics, computer-aided design (CAD), and real-time monitoring.
Automation: Automation refers to the use of technology to perform tasks with minimal human intervention, often through control systems and robotics. It enhances efficiency and accuracy in processes by enabling machines to carry out repetitive or complex tasks that were once performed by humans. This integration of technology not only streamlines operations but also has a significant impact on productivity and the workforce.
Autonomous navigation: Autonomous navigation refers to the capability of a system, particularly robots and vehicles, to navigate and operate without human intervention. This involves using a combination of sensors, algorithms, and control systems to perceive the environment, plan paths, and execute movements effectively, enabling machines to make decisions based on real-time data.
Autonomous robots: Autonomous robots are machines designed to perform tasks in environments without direct human control. These robots leverage artificial intelligence, sensors, and advanced algorithms to make decisions and navigate their surroundings, allowing them to carry out complex operations in various applications, such as manufacturing, exploration, and service industries.
Configuration Space: Configuration space refers to the abstract mathematical space that represents all possible states of a system, particularly in robotics and automation. In this context, it helps in visualizing the positions and orientations of a robot as it moves through its environment, allowing for effective planning and control of its movements. This concept is crucial in determining how a robot can navigate and interact with obstacles in its workspace.
Control System: A control system is a set of devices or algorithms designed to manage and regulate the behavior of other systems, ensuring they operate within desired parameters. These systems can include feedback loops, sensors, and actuators that work together to maintain stability and optimize performance in applications such as robotics and automation. By analyzing system behavior, control systems adapt to changes, making them essential for efficient operation in complex environments.
Degrees of freedom: Degrees of freedom refer to the number of independent parameters or variables that can vary in a system without violating any constraints. This concept is critical in understanding the capabilities and limitations of systems in robotics and automation, as it helps define how a robotic arm or mechanism can move and interact with its environment.
Denavit-Hartenberg Parameters: Denavit-Hartenberg parameters are a standardized way to represent the joint parameters of robotic arms and mechanisms. These parameters simplify the modeling of robotic systems by providing a systematic method for defining the relationships between adjacent links and joints through a set of four parameters for each joint: link length, link twist, joint angle, and joint offset. This approach enhances understanding of robot kinematics and makes it easier to analyze and control robotic movements.
Equations of motion: Equations of motion are mathematical equations that describe the relationship between the motion of an object and the forces acting upon it. They are fundamental in understanding how robotic systems and automated processes behave in response to inputs, and they help predict the future position and velocity of moving objects over time based on initial conditions and applied forces.
Exteroceptive Sensors: Exteroceptive sensors are devices that detect external stimuli from the environment, providing critical information about surrounding conditions. These sensors play a vital role in enabling robotics and automation systems to interact effectively with their surroundings, facilitating tasks such as navigation, object detection, and obstacle avoidance. By capturing data from the external environment, exteroceptive sensors support decision-making processes in automated systems.
Feedback loop: A feedback loop is a process in which the outputs of a system are circled back and used as inputs to influence the operation of that same system. This mechanism is essential for self-regulation and can enhance stability, control, and adaptability in dynamic systems. Feedback loops are crucial for adjusting system performance based on measured outputs, which is vital in understanding system behavior and optimizing control strategies.
Forward kinematics: Forward kinematics is the computational process of determining the position and orientation of the end effector of a robotic arm based on its joint parameters. This involves using mathematical equations that relate the angles and lengths of each joint and link to calculate where the end of the manipulator will be in Cartesian space. By understanding forward kinematics, engineers can predict the movement of robotic systems, ensuring precise control in automation tasks.
Fuzzy logic control: Fuzzy logic control is a form of control system that uses fuzzy set theory to handle the uncertainty and imprecision present in many real-world situations. This approach allows for reasoning and decision-making in systems where traditional binary logic is insufficient, making it particularly effective in complex environments like robotics, automation, and process control. By employing rules that mimic human reasoning, fuzzy logic control can improve performance and adaptability in these systems.
Hybrid position/force control: Hybrid position/force control is a control strategy used in robotics that allows a robot to exert force while also maintaining a specific position. This method combines the advantages of both position control, which focuses on the exact location of the robot's end effector, and force control, which emphasizes the application of force during interactions with the environment. This approach is particularly useful in tasks that require compliance, such as assembly or contact tasks, where maintaining contact while controlling forces is crucial.
Industrial automation: Industrial automation refers to the use of control systems such as computers or robots for handling different processes and machinery in an industry to replace human intervention. This technology enhances productivity and efficiency, allowing for improved consistency and safety in manufacturing processes. By integrating robotics and advanced control systems, industries can streamline operations, reduce labor costs, and minimize errors in production.
Industrial robots: Industrial robots are programmable machines designed to automate tasks in manufacturing and production processes. These robots are often used for repetitive, precise tasks such as welding, painting, assembly, and material handling, significantly enhancing efficiency and productivity in industrial settings.
Inverse Kinematics: Inverse kinematics is a mathematical process used to calculate the joint angles needed for a robotic arm or a character's limb to reach a specific position in space. This process is essential for achieving desired movements in robotics and animation, enabling the accurate control of systems with multiple joints and degrees of freedom. It contrasts with forward kinematics, where the end position is determined based on known joint angles.
Joint space control: Joint space control refers to a method of controlling robotic systems by managing the positions and movements of individual joints within the robot's structure. This approach allows for precise manipulation of the robot's end effector, enabling it to perform tasks with high accuracy while accommodating the specific configurations of its joints. By focusing on joint angles rather than Cartesian coordinates, joint space control simplifies the control algorithms and can be more intuitive when programming robotic movements.
Kinematics: Kinematics is the branch of mechanics that deals with the motion of objects without considering the forces that cause this motion. It focuses on parameters like position, velocity, and acceleration, providing a framework to describe and analyze movement in robotics and automation systems. Understanding kinematics is crucial for programming robots to perform tasks accurately and efficiently.
Lagrangian formulation: The Lagrangian formulation is a method in classical mechanics that reformulates Newton's laws of motion using the principle of least action. It focuses on the Lagrangian function, which is defined as the difference between kinetic and potential energy, and provides a powerful way to derive the equations of motion for a system. This approach is particularly useful in robotics and automation, where complex systems can be analyzed and controlled more effectively using generalized coordinates.
Localization and mapping: Localization and mapping is the process by which a robot or autonomous system determines its position within an environment while simultaneously creating a map of that environment. This involves using various sensors to gather data about surroundings and employing algorithms to interpret that data, allowing the system to navigate effectively. This concept is crucial for enabling robots to understand their position and the layout of the space they operate in, which enhances their functionality in various applications.
Machine learning applications: Machine learning applications refer to the use of algorithms and statistical models to enable machines to improve their performance on tasks through experience and data. These applications are particularly significant in robotics and automation, where they allow machines to learn from their environment, adapt to new situations, and perform complex tasks with minimal human intervention.
Manipulation: Manipulation refers to the ability of a robot or automated system to control or alter the position, orientation, or state of objects in its environment. This involves a series of precise movements and actions that allow robots to interact effectively with their surroundings, often using grippers or end effectors to perform tasks such as lifting, moving, and assembling. Successful manipulation is crucial for the automation of various processes in industries ranging from manufacturing to healthcare.
Mobile robotics: Mobile robotics refers to the branch of robotics that focuses on designing and developing robots capable of moving throughout their environment. These robots can navigate autonomously or be remotely controlled, employing various sensors and algorithms to interact with their surroundings. Mobile robotics encompasses a wide range of applications, from industrial automation to personal assistance, emphasizing the importance of mobility in performing tasks effectively.
Newton-Euler formulation: The Newton-Euler formulation is a method used in robotics and dynamics to describe the motion of rigid bodies by combining Newton's laws of motion and Euler's rotational equations. This approach allows for the analysis of forces and torques acting on a body, making it essential for understanding the dynamics of robotic systems and automation processes. It provides a systematic way to derive equations of motion for complex mechanical systems, taking into account both translational and rotational dynamics.
Norbert Wiener: Norbert Wiener was an American mathematician and philosopher, best known as the founder of cybernetics, the study of control and communication in animals and machines. His work laid the foundation for modern control theory, influencing fields such as robotics, automation, and feedback systems, which are essential in understanding how mechanical systems function and are managed.
Operational Space Control: Operational space control refers to a method of managing the motions and interactions of robotic systems in a defined operational space. It emphasizes the ability to specify tasks in terms of end-effector positions and trajectories, allowing for effective manipulation of objects in various environments. This approach is crucial in robotics and automation, as it directly impacts the precision and efficiency of robotic movements while adapting to changing conditions and objectives.
Optimization-based methods: Optimization-based methods are mathematical strategies used to find the best solution from a set of possible solutions by maximizing or minimizing an objective function. These methods are crucial in robotics and automation, where they help in tasks like path planning, motion control, and resource allocation, ensuring that systems operate efficiently and effectively under given constraints.
Path planning: Path planning is the process of determining a sequence of movements or actions for a robot or automated system to reach a desired destination while avoiding obstacles. This concept is crucial in robotics and automation as it involves optimizing the route taken, ensuring efficiency and safety in navigation.
PID Control: PID control, or Proportional-Integral-Derivative control, is a widely used control loop feedback mechanism that adjusts an output based on the difference between a desired setpoint and a measured process variable. By combining three control actions—proportional, integral, and derivative—this method effectively minimizes steady-state error, enhances disturbance rejection, and optimizes performance in various applications, including robotics and process control.
Programmable logic controllers (PLCs): Programmable Logic Controllers (PLCs) are industrial digital computers designed to control manufacturing processes or machinery automatically. They are robust devices that can be programmed to perform various tasks, including monitoring inputs from sensors and controlling outputs to actuators, making them essential in robotics and automation systems.
Proprioceptive sensors: Proprioceptive sensors are specialized sensory receptors that provide information about body position, movement, and spatial orientation. They play a crucial role in enabling robots and automated systems to understand their own state and adjust their actions accordingly, enhancing their ability to perform tasks autonomously and effectively.
Robot control: Robot control refers to the methods and techniques used to manage and direct the behavior of robots in order to achieve specific tasks. This encompasses the algorithms and systems that enable robots to interpret sensory data, make decisions, and execute actions effectively within their operational environment. Successful robot control is crucial for automation, allowing machines to work autonomously or in collaboration with humans across various industries.
Robot dynamics: Robot dynamics refers to the study of the forces and torques that affect the motion of a robot, taking into account its mass, inertia, and the external forces acting on it. Understanding robot dynamics is crucial for designing and controlling robotic systems, as it allows engineers to predict how robots will move and respond to inputs. This knowledge is essential for tasks such as path planning, control system design, and stability analysis.
Robot kinematics: Robot kinematics is the study of the motion of robots without considering the forces that cause this motion. It focuses on how a robot's joints and links move to achieve a desired position or orientation, which is critical for effective robotics and automation. Understanding robot kinematics allows engineers to design robots that can perform complex tasks accurately and efficiently, ensuring they can operate in various environments.
Robot Operating System (ROS): Robot Operating System (ROS) is an open-source framework that provides a collection of software libraries and tools to help developers create robotic applications. It enables hardware abstraction, device drivers, communication between processes, and development of software modules for various robotic systems. By offering standard interfaces and an extensive ecosystem, ROS significantly simplifies the complexity involved in robotics and automation.
Robot programming languages: Robot programming languages are specialized coding languages designed to control and program the behavior of robots. These languages enable developers to create algorithms that instruct robots on how to perform tasks, interact with their environment, and make decisions based on sensor inputs and other stimuli, ultimately enhancing automation capabilities.
Robotic process automation: Robotic process automation (RPA) refers to the technology that allows software robots to automate repetitive and rule-based tasks typically performed by human workers. RPA uses bots to mimic human actions, such as data entry, transaction processing, and system integration, effectively increasing efficiency and reducing errors in business processes. This technology not only streamlines operations but also enables organizations to allocate human resources to more complex and strategic tasks.
Robotic safety: Robotic safety refers to the set of guidelines, protocols, and technologies designed to ensure that robots operate safely and do not pose a risk to humans, the environment, or property. This concept is crucial as robotics and automation become increasingly integrated into various industries, emphasizing the importance of designing robots that can operate effectively while minimizing hazards and accidents.
Sampling-based methods: Sampling-based methods are techniques used to create solutions in robotics and automation by generating and evaluating potential configurations or paths through the use of randomly selected samples. These methods rely on exploring the space of possible actions, often employing probabilistic algorithms to find feasible solutions that meet specific criteria. They are particularly useful in high-dimensional spaces where traditional optimization techniques may struggle, allowing robots to navigate complex environments efficiently.
Sensing and Perception: Sensing and perception refer to the processes through which systems, including robots, collect data about their environment and interpret that data to make informed decisions. These processes are crucial for enabling automation and robotics, as they help machines understand their surroundings, recognize patterns, and respond to stimuli, ultimately facilitating effective interaction with the world.
Sensor Fusion: Sensor fusion is the process of integrating data from multiple sensors to produce more accurate and reliable information than could be obtained from any individual sensor alone. This technique enhances the performance of robotic systems and automation by combining diverse inputs, such as visual, auditory, and tactile data, to improve decision-making and environmental perception.
Sensors: Sensors are devices that detect and measure physical properties from the environment and convert them into signals that can be read and interpreted. They play a critical role in various systems, providing data that enables machines to respond to their surroundings, ensuring accurate monitoring, control, and automation.
Shigeo Shingo: Shigeo Shingo was a Japanese industrial engineer and a key figure in the development of manufacturing practices, particularly in the context of the Toyota Production System. He is renowned for his work on improving production efficiency through methods like the Single-Minute Exchange of Die (SMED) and the concept of poka-yoke, or error-proofing. His contributions have had a lasting impact on robotics and automation, particularly in optimizing processes and enhancing quality control.
Simulation: Simulation is the process of creating a virtual model of a real-world system or process to analyze its behavior under various conditions. This technique is essential for understanding complex systems, enabling experimentation without the risks or costs associated with real-life implementation. By replicating the dynamics of a system, simulation allows for insights into performance, optimization, and decision-making.
Simulation environments: Simulation environments are virtual platforms that mimic real-world scenarios for testing and analysis, allowing users to observe and interact with complex systems without the risks associated with real-life experimentation. These environments are essential in robotics and automation, as they facilitate the design, development, and evaluation of robotic systems under various conditions. By providing a controlled space for experimentation, simulation environments help engineers understand system behaviors, optimize designs, and validate control strategies before deployment in real-world applications.
Supervisory Control and Data Acquisition (SCADA): SCADA is a system used for remote monitoring and control of industrial processes, gathering real-time data from various sensors and devices to facilitate centralized management. This technology is vital in managing operations across multiple locations, providing operators with the tools to analyze, control, and optimize systems such as water treatment, electrical grids, and manufacturing lines.
System Integration: System integration refers to the process of bringing together various subsystems and components into a unified whole to function cohesively. This is crucial in fields such as robotics and automation, where multiple technologies, software, and hardware must work seamlessly to achieve a common goal, like improving efficiency or enhancing performance. Effective system integration ensures that all parts communicate effectively, leading to optimized operations and increased productivity.
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Practice QuizGlossary
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