How are industrial robots controlled?

Introduction

In the world of manufacturing, industrial robots have now become a crucial aspect in the revolutionization of production processes across several sectors. These industrial robots possess the ability to complete tasks with the utmost precision, speed, and consistency, hence, reducing the need for human labor and further enhancing efficiency.

Their functionality mainly lies in sophisticated control systems which control their every move with extreme precision. This also has a significant impact on shaping the landscape of modern manufacturing, from automotive assembly lines to pharmaceutical production facilities. Therefore, it is important to dive into the intricate mechanisms that control industrial robots and learn more about the various techniques and technologies that drive their operation. 

Basics of industrial robot control:

1. Teaching and offline programming

• In industrial robot control, common control methods include teaching and offline programming. The teaching method uses a teach pendant, and the operator records the path and movements by manually moving the robot, and then saves them as a program. The teaching method is simple and intuitive, suitable for quickly setting simple tasks. Another control method is offline programming, which uses computer software to program the robot without actually operating the robot. Offline programming has the advantage of improving programming efficiency and accuracy, and can perform path planning and simulation in a virtual environment, reducing production line downtime and costs. 

2. Trajectory planning

• Continuous path motion: This is mainly used in applications that demand smooth trajectory following, traversing already defined paths with constant velocity. Furthermore, this technique is most commonly used in applications in tasks like painting and laser cutting, where uniform coverage and maintenance of consistent cutting speed, are essential.

• Point to point motion: Robots execute point to point motion for discrete tasks which require precise positioning, moving directly from one predetermined position to another. This strategy reduces cycle time and enhances efficiency in applications such as palletizing and assembly. Their swift movement between pick up and placement locations streamlines the packaging process which ensures timely delivery to customers. 

Control techniques and algorithms

1. PID control

Proportional integral derivative control is a well established technique for regulating the robot’s motion. Through the alteration of proportional, integral, and derivative gains, PID controllers ensure the accurate tracking of desired trajectories while reducing errors. From robotic welding to material handling, PID control algorithms offer robust performance through continuous adjustments of control inputs based on error feedback. This nature allows robots to maintain stability and precision, even in dynamic environments or when encountering any unexpected disturbances. Its simplicity along with its effectiveness makes it highly suitable for real time implementation in embedded environments, where computational resources may be limited, confirming responsive and reliable motion control in resource constrained industrial settings. 

2. Model predictive control (MPC)

MPC algorithms optimize control inputs that are essentially based on a predictive model of the robot’s dynamics, taking into account constraints and objectives that help to achieve optimal performance. This technique enhances trajectory tracking accuracy and robustness in dynamic environments. It is especially beneficial in applications such as robotic machining where disturbances and uncertainties in material properties necessitate adaptive control strategies for the maintenance of machining precision and surface finish quality. Moreover, their ability to determine future system behavior allows proactive adjustments. This minimizes potential deviations from desired trajectories and ensures consistent performance even in challenging operating conditions.  

Advanced control strategies

1. Vision-based control

The integration of vision systems with industrial robots allows visual feedback for tasks such as localization, object detection, and tracking. Through analyzing images captured by cameras, robots are able to adapt their motion to handle variations in the environment, enhancing their flexibility and overall autonomy. They find extensive applications in industries like automotive manufacturing, where robots that are equipped with vision systems are able to precisely locate vehicle components for assembly, ensuring their accurate fit and alignment. Furthermore, in the field of logistics and warehousing, this control allows robots to navigate scattered environments, identify any objects of interest, and complete tasks such as inventory management along with order fulfillment. 

2. Force control

This allows robots to delicately interact with the environment while exerting precise forces to execute tasks like assembly, deburring, and polishing. Conformity with force sensors enables robots to adapt to different contact forces, ensuring efficiency and safety. Force control used in aerospace manufacturing is crucial for tasks like drilling and riveting where robots must have the consistent force to prevent any damage to delicate materials. Moreover, in medical robotics, this makes delicate procedures such as surgical interventions and tissue manipulation easier. Robots can be applied in this sector as they can apply controlled forces to ensure optimal outcomes while also reducing any patient trauma. 

Future plans and trends

1. Artificial intelligence (AI)

AI-powered control algorithms including reinforcement learning and neural networks, encourage robots to learn control techniques autonomously. Their self-learning capability has significant potential for improving adaptability and performance in dynamic environments. Using AI, these industrial robots can analyze vast datasets in real-time which allows predictive maintenance to deal with potential malfunctions and optimize operational efficiency, in turn reducing downtime and increasing productivity. Control system driven by AI allow robots to adapt to varying production demands through reconfiguring workflows and optimizing resource allocation to meet the evolving market needs with extreme precision. 

2. Collaborative Robotics

They offer safety and flexibility in shared workplaces as they are specifically designed to work alongside humans. Advanced control algorithms allow cobots to identify and respond to human presence which in turn facilitates seamless collaboration and productivity. Through the integration of sensors and vision systems, cobots can detect and respond to any changes in their environment, this prevents the risk of any potential collisions or accidents. They are also able to adapt to changing production demands, through dynamically reconfiguring workflows and optimizing resource allocation to meet the ever-evolving needs of the market with extreme precision.

Conclusion

All in all, these industrial robots represent the peak of automation by driving precision, innovation, and efficiency in manufacturing. As technology continues to make more advancements, the sector of industrial robot control holds countless possibilities, pushing manufacturing into an era of efficiency and productivity.