Control systems are fundamental to today’s technology, shaping our interactions with the world. A control system is a mechanism or process that manages the behaviour of a system. When you adjust the thermostat to control the temperature in your home or tap the brakes in your car to avoid a collision, you’re interacting with a control system. In robotics, these systems allow machines to perform tasks, interact, adapt, and effectively respond to their environment.
Robotics, combining hardware and software, involves the design, construction, and operation of autonomous machines and is heavily dependent on control systems. From industrial robotic arms in manufacturing plants to Boston Dynamic’s humanoid robots, each robotic application uses some form of a control system. Here’s how:
Motion Control: Robot movement, whether it’s the simple rotation of a joint or the complex coordination required for bipedal walking, depends on control systems. These systems ensure the motors turn the right amount, at the right speed, and at the right time. Consider a robotic arm picking up an object. For the gripper to move towards the object, the motors of the robot need to know how to rotate, and each motor needs to know how much electrical voltage it takes to perform the desired rotation. Both relationships can be described and put into action using control systems.
Sensory Feedback: Robots are equipped with various sensors, from cameras to touch sensors, that feed information back to their control systems. This feedback allows robots to adapt to their environments. For instance, an autonomous car will incorporate sensory information into their control system to deaccelerate in the presence of obstacles.
Control systems are a pillar of robotics. They bridge the gap between a robot’s intention and its action, ensuring safe, efficient, and effective operation. As robotics technology continues to evolve, as will control systems, enabling more sophisticated interactions and capabilities. IEEE Control System’s Society’s “Control for Societal-scale Challenges: Road Map 2030” identifies three major challenges for the future of robot control:
Resilience: Robot control must quickly adapt to significant changes in the environment, like transitioning from sun to snow or from pavement to mud. This adaptability is essential in extreme environments, such as space or deep-sea, where autonomous robots perform dangerous tasks. Future robot controllers must be able to endure harsh conditions, limited communication, and system faults.
Performance Guarantees: Every robotic system carries uncertainties, such as its capabilities (like sensing) or its constraints (like power consumption). Ensuring robot controllers function safely and correctly despite these uncertainties is crucial. While classic control methods are often overly conservative, modern strategies should be able to flexibly achieve complex objectives under such uncertainties, while maintaining safety and efficiency.
Trust and Safety: As mentioned in some of my previous posts, robots are increasingly being introduced into our daily lives. Robots should be able to anticipate human actions and operate predictably for a mutual understanding. Adaptive, complaint control strategies should be developed to allow for safe, physical interactions between humans and robots.
The field of robotic control systems is an exciting area in which resilience, performance, and trust are at the core of research and development.
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