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Inverse Kinematics is the process of determining the joint parameters needed to place a robot's end effector at a desired position and orientation. It is often more complex than forward kinematics.

ROS is an open-source framework for robot software development, providing services such as hardware abstraction, low-level device control, message-passing between processes, and package management.

State estimation is the process of estimating the state of a system (position, velocity, orientation) from uncertain measurements, often used in robot localization and mapping.

Localization is the process by which a robot determines its position within an environment, typically using sensors and algorithms such as Simultaneous Localization and Mapping (SLAM).

ANNs are computational models inspired by the human brain that are used in machine learning. They are composed of interconnected processing elements called neurons and are used in robotics for tasks like perception and decision-making.

Swarm robotics is a field of robotics that studies how large numbers of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between agents and the environment.

An IMU is a device that measures and reports a body's specific force, angular rate, and sometimes the magnetic field surrounding the body, using a combination of accelerometers and gyroscopes, and occasionally magnetometers.

Obstacle avoidance is a critical feature in robotics that enables robots to navigate through their environment by detecting and avoiding collisions with obstacles using various sensors and algorithms.

The Kalman Filter is an algorithm that uses a series of measurements over time, containing noise and other inaccuracies, to produce estimates that tend to be more accurate than those based on a single measurement alone.

Forward Kinematics involves computing the position and orientation of a robot's end effector given its joint parameters (e.g., angles for a robotic arm). It is the basis for robot motion planning.

Machine learning in robotics applies algorithms that allow robots to learn from and adapt to new situations without explicit programming, improving their performance with experience.

An end effector is the device at the end of a robotic arm, designed to interact with the environment. Examples include grippers, tools, or sensors.

A PID Controller is a type of feedback controller that uses three terms: Proportional (P), Integral (I), and Derivative (D) to adjust the control variable for minimal error. It is widely used in industrial control systems.

Adaptive control is a type of control that can adjust its parameters in real-time to cope with changes in the system dynamics or the environment, which is crucial for dealing with uncertainties during robot operation.

Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems with inputs. It seeks to develop a systematic approach to controlling these systems using feedback.

Actuators are devices that convert energy into motion in a control system. They are essential components that drive the movement of a mechanical system, such as motors in a robotic arm.

Fuzzy Logic is a form of logic where the truth values of variables may be any real number between 0 and 1, representing the concept of partial truth. It is used in systems that require a more flexible approach than binary logic.

Haptic feedback is the use of touch sensation to communicate with the user of a system. In robotics, it enables operators to feel the forces that a robot interacts with, enhancing remote control.

Compliance in robotics refers to the ability of a robot to yield under force, allowing it to adapt to external forces, minimize damage, and work safely alongside humans.