The Simulation Series, Part IV: Robotics
So far, this series has covered the complexity of simulation, the different types of simulation, and the characteristics of discrete event simulation.
- Part I – Navigating the Complexity of Simulation
- Part II – Manufacturing Simulation Types
- Part III – Discrete Event Simulation
In Part IV, we provide a brief history of robotics and explain its role in simulation.
The first and second industrial revolutions gave birth to various industries, resulting in the creation of different types of works and tasks. Many tasks were difficult and dangerous for humans. They were also more time consuming. The third industrial revolution brought forth the rise of electronics, telecommunications, and computers, along with nuclear energy.
Research and development in the field of electronics, telecommunications, and computers then gave birth to robots; their role was to handle difficult, dangerous, and repetitive tasks.
The term "robot" originates from a Czechoslovakian word meaning "work." The first industrial robot – The Unimate – was installed in 1962 on a General Motors production line to sequence and stack hot die-cast metal components. Unimate's 4000-pound (1,816 kg) arm was controlled by commands stored on a magnetic drum. By the 1970s, robots known as PUMA (Programmable Universal Machine for Assembly) were widely used in industries like automobile to assemble automobile subcomponents such as dash panels and lights. Simultaneously, another type of robot called AGVs (Automated Guided Vehicles) were being used in warehouses.
Robots are integral elements of Smart Manufacturing and Industry 4.0. In addition to addressing difficulties, dangers, and repetitive tasks, robots are also increasingly being used to improve speed-to-market in a dynamic, ever-competitive marketplace.
Robotic simulation is a digital way of engineering robotic work cells and robotics-based automated production systems, as well as generating off-line programing, without having physical robots in place, thus reducing a production system's shutdown time and a product’s lead time to market. It also provides engineers with the flexibility to try different ideas and formulate manufacturing scenarios in a digital environment, which in turn allows them to analyze each scenario’s pros and cons by collecting virtual response data that accurately represents the responses of physical systems. So, robotics simulation permits experimentations to optimize manufacturing process in less time with minimal capital cost.
Applications of Robots and Robotics Simulation
Robots are widely used in industries likes manufacturing, automotive, aerospace, fast-moving consumer goods (FMCG), warehouse, logistics and health care. Robotics simulation is being used to design and validate a variety of automated processes within these industries. Specifically, it helps detail robotic paths and ensures collision free operations with optimized cycle time for pick-and-place, cutting, spot welding, arc welding, drilling, riveting, painting and safety operations.
Benefits of Robotics Simulation for Industries
Benefits of robotics simulation can be categorized under three categories:
- Risk reduction in research, development, and experimentation.
- Time shortening of products to market.
- Cost saving in producing products.
On the Robotics Horizon
Research and development are being carried out to further enhance the capabilities of robots by combining them with technologies like machine learning and artificial intelligence. The accuracy of robots is being improved by precise machine learning processes. Artificial intelligence is being used to teach functions such as grasping objects, computer vision, and motion control in robots to make them understand and work on unseen data and situations. Hence, exciting discoveries are yet to be revealed!
In Part V of the Simulation Series, we’ll discuss the use of Emulation simulation to virtually commission industrial systems.
References: Computer based robot training in a virtual environment; A. Sett, K. Vollmann; IEEE International Conference; Autonomous path generation platform for robot simulation; Amit Kumar Bedaka, Chyi-Yeu Lin; IEEE International Conference