Control
Control algorithms form the backbone of intelligent robotic systems. They enable machines to interpret sensory input and translate it into precise and purposeful actions. In the field of robotics, control is not just about movement—it's about how robots interact with their environment, make decisions in real time, and adapt to both predictable and unpredictable changes. Whether a robot is navigating through a cluttered room, following a curved path on a road, or stabilizing itself against external forces, control systems make all of this possible.
One of the central challenges in robotics is accurately navigating and reacting to real-world conditions. This involves more than just moving from one point to another—it requires maintaining stability, compensating for errors, avoiding obstacles, and doing so efficiently. Real-world environments are often noisy and dynamic, meaning that sensors can produce imperfect data and conditions can change rapidly. Effective control algorithms must be resilient and responsive, allowing robots to function autonomously and safely.
On this page, we'll explore four foundational control concepts and algorithms used in robotics. These are widely applied across a variety of domains, from self-driving cars and autonomous drones to mobile robots and robotic arms:
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PID Controller (Proportional-Integral-Derivative):
One of the most widely used control mechanisms in robotics and engineering. The PID controller adjusts a system's output based on the error between a desired target and the actual position or speed. By combining proportional, integral, and derivative components, the PID controller enables precise and smooth control in systems such as motor regulation, balance correction, and even temperature control in manufacturing processes. -
Pure Pursuit Controller:
A geometric path-following algorithm that directs a robot to "pursue" a point a set distance ahead on a predefined path. It's particularly effective for smooth and continuous motion in mobile robots and autonomous vehicles, allowing them to follow curves and waypoints with minimal oscillation. Its simplicity and elegance make it a popular choice for real-time path tracking. -
Stanley Controller:
Developed for autonomous vehicle navigation, the Stanley controller is a more advanced path-following algorithm that minimizes both lateral error and heading error between a robot and the reference path. It is robust in handling sharp turns and irregularities in road curvature, making it ideal for real-world driving applications. Unlike pure pursuit, which follows a look-ahead point, Stanley corrects based on the vehicle's deviation from the path and its current orientation. -
Differential Drive Control:
A fundamental locomotion model used in many mobile robots, particularly those with two independently driven wheels. Differential drive systems steer by varying the speed of each wheel, allowing for precise control of direction and rotation. Control algorithms for differential drive robots often integrate with higher-level planners to convert motion commands into wheel velocities, factoring in constraints like wheel slip, turning radius, and surface traction.
Each of these control systems plays a unique and critical role in enabling autonomy. Some are designed for low-level motor control, while others are focused on high-level path tracking and behavior planning. Understanding their principles, strengths, and trade-offs provides the foundation for building responsive and intelligent robotic systems that can operate reliably in the real world.