Glossary

3.3 Force control and impedance control

2 min read•july 25, 2024

Force and are crucial for robots interacting with their environment. These techniques allow robots to apply precise forces and adapt to different surfaces, making them safer and more versatile in tasks like assembly and human collaboration.

Control laws for force and impedance use feedback to achieve desired behavior. aims for specific forces, while impedance control regulates the relationship between motion and force. Implementation requires sensors, real-time processing, and careful tuning to ensure stability and performance.

Force Control and Impedance Control in Robotics

Concepts of force and impedance control

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  • Force control manipulates forces and torques applied by robot enabling safe and precise (assembly tasks)
  • Impedance control regulates dynamic relationship between robot's motion and interaction forces allowing desired mechanical impedance behavior (human-robot collaboration)
  • Force control achieves desired force/torque while impedance control aims for desired dynamic behavior
  • Applications include assembly tasks, surface finishing, and human-robot collaboration

Derivation of control laws

  • Force control law: u=Kp(Fd−Fa)+Ki∫(Fd−Fa)dtu = K_p(F_d - F_a) + K_i \int (F_d - F_a) dt
    • FdF_d: desired force
    • FaF_a: actual force
    • KpK_p: proportional gain adjusts response speed
    • KiK_i: integral gain eliminates steady-state error
  • Impedance control law: Mdx¨+Bdx˙+Kdx=FM_d \ddot{x} + B_d \dot{x} + K_d x = F
    • MdM_d: desired inertia affects acceleration response
    • BdB_d: desired influences velocity behavior
    • KdK_d: desired determines position compliance
    • xx: position error
    • FF: interaction force
  • Robotic manipulator dynamics: M(q)q¨+C(q,q˙)q˙+G(q)=τ+JT(q)FM(q)\ddot{q} + C(q,\dot{q})\dot{q} + G(q) = \tau + J^T(q)F
    • qq: joint positions
    • M(q)M(q): inertia matrix represents mass distribution
    • C(q,q˙)C(q,\dot{q}): Coriolis and centrifugal terms account for velocity-dependent forces
    • G(q)G(q): gravity vector compensates for gravitational effects
    • τ\tau: joint torques
    • J(q)J(q): Jacobian matrix maps joint to Cartesian velocities

Implementation of control algorithms

  • Force control implementation:
    1. Measure actual force using force/torque sensor
    2. Calculate force error
    3. Apply control law to generate joint torques
    4. Use inverse dynamics to compensate for robot's natural dynamics
  • Impedance control implementation:
    1. Measure position and force
    2. Calculate desired acceleration based on impedance model
    3. Use inverse dynamics to generate joint torques
  • Sensor integration incorporates force/torque sensors, joint encoders, and tactile sensors for accurate feedback
  • Control loop requires high-frequency updates (1 kHz or higher) and real-time operating system for precise control
  • Stability considerations involve gain tuning and robustness to uncertainties to prevent instability

Robot-environment interaction analysis

  • Contact modeling considers rigid contact, compliant contact, and friction models (Coulomb, viscous) for accurate interaction simulation
  • Performance metrics evaluate force tracking error, position tracking error, settling time, and overshoot to assess control quality
  • Stability analysis employs Lyapunov stability theory and small-gain theorem to ensure system stability
  • Environmental uncertainty addressed through stiffness estimation and adaptive control techniques for robust performance
  • Task-specific analysis examines peg-in-hole assembly, surface following, and cooperative manipulation to optimize control strategies

Key Terms to Review (18)

Active Compliance: Active compliance refers to a control strategy used in robotics that allows a robot to adapt its behavior in response to external forces or interactions with the environment. This technique enables the robot to maintain a desired trajectory while accommodating disturbances, enhancing safety and effectiveness during physical interactions. Active compliance is closely linked to force control and impedance control, both of which focus on regulating the interaction forces between the robot and its surroundings.
Compliant Control: Compliant control is a strategy in robotics that allows a system to adaptively respond to external forces while maintaining desired performance. This approach is essential for applications where interaction with the environment is necessary, as it allows robots to adjust their motion and behavior according to external disturbances or contacts. By balancing between stiffness and flexibility, compliant control ensures safe and efficient operations in force and impedance control scenarios.
Contact Dynamics: Contact dynamics refers to the study of forces and motions that occur when two or more bodies interact through physical contact. This includes understanding how the contact forces change in response to movements and deformations of the interacting bodies, which is essential for achieving stable and controlled interactions in robotic systems. This concept plays a crucial role in force control and impedance control, as it helps determine how robots should respond to external forces while maintaining desired motion and stability.
Damping: Damping refers to the process of reducing oscillations or vibrations in a mechanical system, typically through energy dissipation. This concept is crucial in controlling how a system responds to external forces, allowing for smoother interactions and better stability. In applications like force control and impedance control, damping helps manage the dynamic behavior of robotic systems, ensuring they can adapt effectively to varying conditions without excessive overshoot or oscillation.
Environmental Interaction: Environmental interaction refers to the way robots engage and respond to physical stimuli and forces within their surroundings. This concept is crucial for understanding how robots can manipulate objects, navigate through spaces, and adapt their behavior based on real-time feedback from the environment. It encompasses the principles of force control and impedance control, which are essential for achieving precise movement and stability when interacting with various external forces.
Feedback Loop: A feedback loop is a process in which the output of a system is circled back and used as input, allowing for continuous monitoring and adjustment. This dynamic interaction helps systems respond to changes in their environment, making them more adaptive and efficient. Feedback loops are crucial in robotic systems as they facilitate real-time adjustments, ensuring that robots can maintain desired performance despite variations in external conditions.
Force Control: Force control is a technique used in robotics to manage and regulate the force exerted by a robot during interactions with its environment. This method is essential for tasks that require delicate handling or interaction with human operators, as it ensures safety and precision. By adjusting the force output based on feedback from sensors, robots can adapt their actions to accommodate varying resistance and compliance in their tasks.
Force Sensor: A force sensor is a device that detects and measures the magnitude of force applied to it, converting mechanical pressure into an electrical signal. This capability allows robots to interact with their environment in a controlled manner, making it essential for applications such as force control and impedance control, where precise feedback about external forces is crucial for safe and effective operation.
Giorgio Metta: Giorgio Metta is a prominent researcher in the field of robotics, particularly known for his work on force control and impedance control techniques. His contributions focus on developing robots that can interact safely and effectively with humans and their environments, which is crucial for applications in collaborative robotics. Metta's research emphasizes the importance of dynamic interaction in robotic systems, leading to advancements in how robots respond to external forces and adapt their behavior accordingly.
Humanoid Robots: Humanoid robots are robots designed to resemble the human body in shape, appearance, and behavior. They are typically equipped with advanced sensors and actuators that allow them to interact with humans and their environment in a more intuitive manner. This design not only facilitates better human-robot interaction but also allows humanoid robots to perform tasks that require human-like mobility and dexterity, making them suitable for various applications including service, healthcare, and education.
Impedance Control: Impedance control is a control strategy used in robotics to manage the interaction between a robot and its environment by dynamically adjusting its mechanical impedance. This means that the robot can alter its stiffness, damping, and inertia to respond appropriately to external forces, allowing for better force and motion control during tasks like manipulation and contact with objects. By effectively managing impedance, robots can achieve safer and more compliant interactions with their surroundings, making it essential in areas like force control and end-effector design.
ISO 10218: ISO 10218 is an international standard that focuses on the safety requirements for industrial robots and robot systems. It aims to ensure that robotic systems are designed and operated in a way that minimizes risk to human operators and other personnel. This standard outlines critical safety principles applicable to various aspects of robotics, making it essential for understanding safe robot operation, force control, impedance control, and collaborative interactions between robots and humans.
Marc Raibert: Marc Raibert is a prominent roboticist known for his pioneering work in dynamic control of bipedal robots and the development of legged robotics. His research has significantly influenced the fields of force control and impedance control, where understanding the interaction between robots and their environment is crucial for achieving stable and agile movement.
Robotic surgery: Robotic surgery is a minimally invasive surgical technique that utilizes robotic systems to assist surgeons in performing complex procedures with enhanced precision, flexibility, and control. This method allows for smaller incisions, reduced blood loss, and quicker recovery times compared to traditional open surgery. The integration of advanced technology in robotic systems connects closely with various aspects of robotics, including the manipulation of force and impedance, the structural architecture of robotic arms, and the use of visual tracking systems for improved surgical outcomes.
ROS: ROS, or Robot Operating System, is an open-source framework designed to simplify the development of robotic applications. It provides a collection of software libraries and tools that facilitate the creation, simulation, and control of robots, making it easier for developers to implement complex behaviors and functionalities. By offering a standardized environment, ROS enhances collaboration and integration between different robotics components and systems.
Stiffness: Stiffness is a measure of a system's resistance to deformation when subjected to an applied force. In robotics, it plays a crucial role in determining how a manipulator or system reacts to external loads, influencing control strategies and the performance of tasks. Understanding stiffness helps engineers design systems that can effectively manage forces while maintaining desired positions and movements.
Tactile Sensor: A tactile sensor is a device that detects and measures physical touch or pressure through direct contact with an object or surface. These sensors provide valuable feedback about force, texture, and shape, enabling robots and systems to interact more effectively with their environments. Tactile sensors play a crucial role in force control and impedance control by allowing robotic systems to adjust their movements based on real-time feedback, ensuring precision and safety during interactions.
Transfer Function: A transfer function is a mathematical representation that describes the relationship between the input and output of a linear time-invariant (LTI) system in the frequency domain. It provides insight into how a system responds to different inputs, characterizing its dynamics and stability. The transfer function is typically expressed as a ratio of polynomials in the Laplace transform variable, which helps in analyzing the system's behavior under various conditions, including feedback control and interaction with external forces.
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