Robot Control Programming

Robots are becoming more prevalent across a wide range of sectors, from manufacturing to healthcare. The process of creating software that directs a robot’s movement and actions is known as robot control programming. Often, the software is made to provide the robot with a set of instructions or tasks to finish, including transferring an object from one place to another or carrying out a particular duty.

Robot control programming can be a difficult and complex undertaking, but it is necessary to make sure that robots work effectively and safely. Robot control programming can be done using a variety of tools and programming languages, such as C++, Python, MATLAB, and ROS (Robot Operating System).

Top Robotics Programming Languages

(Source: Wikipedia)

The creation of algorithms that allow the robot to traverse and interact with its surroundings is one of the most important areas of robot control programming. To do this, software must be developed that can analyze sensor data such as pictures and lidar scans to produce a map of the robot’s surroundings. On the basis of this map, the programmer must also be able to plan and carry out the robot’s travel routes.

Programming for robot control encompasses more than just the creation of software for specific robots. It may also entail creating software to coordinate the activities and motions of a fleet of robots to carry out challenging jobs. To do this, software that can allocate jobs to different robots, communicate with various robots, and track each robot’s development must be created.

Robot Programming

(Source: Wikipedia)

Providing for the safe operation of robots in their environs is one of the difficulties in programming robot control. For this, software that can recognize and react to possible safety concerns, such as barriers or people in the robot’s route, must be created. It also entails creating software that can keep track of the robot’s condition and turn it off if it notices a problem or anomaly.

Applications

1) Manufacturing : 

Robot control programming is widely used in manufacturing to automate production processes, increase efficiency, and reduce costs. Numerous jobs, including welding, painting, assembling, and material handling, can be programmed into robots. To form a completely automated manufacturing line, they can also be linked with other automated systems like conveyors, sensors, and vision systems.

Production line of electric drive housing 

(Source: BMW Group)

Few examples of industrial usage are, Siemens’ Sinumerik One used at BMW’s Steyr production plant in Austria to produce an electric drive housing. Up to ten times faster PLC cycle times and higher CNC performance in terms of machine operation, cutting speed, data gathering, and processing speed are purported capabilities of the integrated Simatic S7–1500F PLC. With the Simatic S7–1500F PLC, Sinumerik One is completely linked into the Engineering Framework TIA Portal, allowing operators of larger plants to standardize all engineering duties.

Since over a decade ago, the Robot Operating System (ROS) has been employed in space activities. At ROSCon 2012, NASA first demonstrated the Robonaut 2 (R2) humanoid robot, which used ROS. NASA converted R2's software to ROS and created a model of the robot and the International Space Station (ISS) using Gazebo, a 3D robotics simulator from Open Robotics.

Astrobee aboard the ISS 

(Source: NASA)

Astrobee, NASA's free-flying successor to SPHERES, employs ROS in other space robots. There are numerous Astrobees working within the ISS.

Currently, the VIPER programme is being developed by NASA and Open Robotics. In 2023, VIPER plans to launch a mobile robot to the Moon's South Pole. The rover will be under the control of ROS 2.

When you need to cut bespoke forms from huge workpieces, robot milling is the perfect answer. Because there are no size restrictions, milling robots can outperform conventional CNC machines. However, if you don't use the correct software, robot milling can be challenging. The Sunrob Robotics team realised they needed a highly adaptable, cost-effective solution that would enable them to quickly and easily load new hockey stick designs into the robot programme. They went with RoboDK, which includes a milling wizard.

Sunrob Robotics creates a 3D model of each stick using a handheld laser scanner, which they then precisely duplicated using robot milling. Following that, placed preform sticks into the production cell and used RoboDK to generate a milling program. The preform is next examined with a laser scanner to confirm that the milling is accurate. The preform is then precisely carved into the shape of the ice hockey stick utilizing robot milling as the last step.

2) Logistics :

Moreover, logistics uses robot control programming to automate order picking, transportation, and material handling. To transfer goods from one place to another, automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are frequently employed in warehouses, distribution centers, and e-commerce fulfillment centers.

Types of autonomous mobile robots 

(Source: Ubuntu)

Autonomous mobile robots architecture -

Products for autonomous mobile robots are sophisticated machines that incorporate hardware, software, and specialized parts. Devices are typically grouped according to their ability to localize, navigate, and plan motion. In the end, this establishes their autonomy. But, there is much more to AMRs. Navigation is merely a feature. So, understanding the architecture is crucial.

Autonomous mobile robots architecture

(Source: Ubuntu)

3) Healthcare :

Robot control programming is being utilized more frequently in the medical field to help with patient care, rehabilitation, and surgery. Robots are capable of being programmed to carry out exact manipulations and actions that are challenging or impossible for human hands. They can also be used to transport medications, monitor patients and provide companionship .

Programming the Perfect Grip for Robotic Surgery -

Cambridge Research & Development of Cambridge, Massachusetts has gained the lead in creating the optimum surgical robot grip with its Neo interface. Due to the insertion of incredibly sensitive, paper-thin FlexiForce sensors into the gripping sections, Neo's principal purpose is that of a haptic man-machine. The method uses wearable technology that may be worn anywhere on a surgeon's body to transmit force input and imitate the sensation of force. The haptic interface reads the information from the force sensor and converts it into pressure that the device applies. The force sensor can measure applied force using a mechanism that rises and falls in reaction to the force.

4) Agriculture :

Robot control programming is also used in agriculture to automate crop harvesting, planting, and maintenance. Autonomous tractors, drones, and robots equipped with sensors and cameras can be used to monitor crops, detect pests and diseases, and optimize irrigation and fertilization.

 

One specific example of how robot control programming is used in agriculture is in the development of autonomous tractors.

Autonomous tractors are being developed to improve the efficiency and accuracy of planting and harvesting crops. These tractors use GPS technology and other sensors to navigate through the fields and plant or harvest crops with precision. Robot control programming for autonomous tractors involves developing software algorithms that can interpret data from sensors and make decisions based on that data to guide the tractor's movements and tasks.

The programming for autonomous tractors is typically done in a language such as C/C++ or Python. The code is written to control the tractor's movement and to perform the tractor's movements, such as turning, accelerating, and decelerating and planting seeds. The code must also incorporate the sensor data and make decisions based on that data, such as adjusting the tractor's path to avoid obstacles or planting in the correct location.

General scheme of the hardware architecture for the autonomous tractor

(Source: Research Article, “New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots”, Hindawi Publishing Corporation The Scientific World Journal Volume 2014, Article ID 404059)

For example, if the tractor's lidar sensor detects an obstacle in its path, the programming must instruct the tractor to stop or change direction to avoid the obstacle. Similarly, if the tractor's GPS sensor detects that it has reached the end of a row, the programming must instruct the tractor to turn around and begin a new row.

The programming also includes algorithms for performing specific tasks such as planting or harvesting. For example, the programming might include an algorithm for determining the optimal planting depth based on soil conditions or an algorithm for identifying ripe crops to be harvested.

The programming must also take into account the tractor's surroundings and adjust its movements and tasks accordingly. For example, if the tractor is operating in a hilly terrain, the programming must take into account the slope of the land and adjust the tractor's movements accordingly to prevent it from tipping over.

Software architecture for the autonomous tractor to process image

(Source: Research Article, “New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots”, Hindawi Publishing Corporation The Scientific World Journal Volume 2014, Article ID 404059)

One example of a company that develops autonomous tractors is CNH Industrial. CNH Industrial uses a combination of software tools and development frameworks that allow their engineers to rapidly prototype and test new algorithms and features. These tools may include machine learning libraries, simulation environments, and programming frameworks that are tailored specifically to the needs of agricultural automation. They use a combination of C/C++ and Python to program the tractors and incorporate the sensor data into their decision-making processes.

Overall, the use of robot control programming in agriculture is an exciting development that has the potential to improve crop yields and reduce the need for manual labor. By automating tasks such as planting and harvesting, farmers can save time and improve the efficiency of their operations.

5) Energy :

Robot control programming is also used in the energy industry to automate inspection, maintenance, and repair of power plants, pipelines, and offshore platforms. Robots equipped with cameras, sensors, and manipulators can be used to inspect equipment and structures, detect defects and anomalies, and perform repairs and maintenance tasks.

The inspection of aeroplane fuselages is being automated and streamlined by a team at NASA's Langley Research Centre employing a number of robots and RoboDK. The group now employs two robots, an external axis, and a more intricate form of infrared examination. The team's inspection work is an instance of "non-destructive evaluation." This phrase describes a sizable range of testing procedures intended to find manufacturing faults without actually harming the product. For instance, the Langley team specialises in infrared detection, which entails heating the fuselage and subsequently using an infrared camera to find defects in the heated material.

The two robots, which are both UR10s, carry out the following tasks:

1) The first robot moves a heating element down the fuselage in a straight line while holding it.

2) The second robot is equipped with an infrared FLIR camera. This slides behind the heating element down the fuselage. The team creates a scan from the image that was obtained and uses the scan to look for material flaws.

Two robot arrangement in RoboDK for inspection of aircrafts by NASA

(Source: https://robodk.com/blog/multi-robot-inspection/)

A crucial component of NASA's inspection application is RoboDK. With RoboDK, combining numerous robots and including external axes in the programming is simple.

The method they employ for the inspection is:

1) The engineers initially map out the fuselage's surface using a Creaform optical scanner. This process is done manually.

2) They can precisely locate the fuselage in space and in relation to the robots using the scanned data.

3) They design the path in RoboDK, which produces robot code automatically.

4) The inspection work is carried out by the robots, who also create an infrared scan of the fuselage.

5) The engineers then examine the scan for flaws using Matlab.

Recent Developments 

1) Reinforcement Learning :

Reinforcement learning is a type of machine learning that enables robots to learn from their environment and improve their performance over time. This method has been used for a variety of robot control tasks, including grasping, manipulating, and movement.

Pick-and-place of color-coded square blocks with the Franka Emika Panda robot 

(Source: research article by Andrew Lobbezoo, article belongs to the Special Issue 10th Anniversary of Robotics—Feature Papers in Intelligent Robots and Mechatronics)

As an illustration, consider using a fundamental robotics environment to programme the motion of a simulated 2D robotic arm. using the reinforcement learning method DDPG, which produces continuous values. Pyglet and a training and assessment pipeline are used to create an Arm environment that keeps track of its state and can render itself.

2) Collaborative Robotics :

Collaborative robotics, or robots, are robots designed to work alongside humans in a shared workspace. Recent developments in robot control programming have focused on enabling robots to work safely and effectively with humans, including techniques such as motion planning, force sensing, and human-robot interaction.

FANUC has introduced a line of collaborative robots that can lift up to 35 kg and operate safely alongside people, enabling them to do more in a congested setting. Additionally, they include a range of hollow wrist alternatives, ethernet functionality, hand guidance, and many other features. At their Ventra Ionia, Michigan facility, Flex-N-Gate, a provider of bumpers, exterior trim, lighting, chassis assembly, and other automotive products, uses inspection technologies to guarantee product quality. Flex-N-Gate turned to FANUC for a collaborative robot solution to assist in enhancing these procedures, cutting costs, and conserving floor space.

AI is used in Intelligent Robotics to improve interoperability between people and machines. AI enables robots to adapt to changing circumstances and interact with humans naturally. AI is used in Intelligent Robotics to improve interoperability between people and machines. AI enables robots to adapt to changing circumstances and interact with humans naturally.

Intelligent robotics architecture of Sawyer robot

(Source: http://img-prod-cms-rt-microsoft-com.akamaized.net/cms/api/am/imageFileData/RE3Jukc )

The main controller in the diagram consists of a Robot Operation System, a REST interface, a task planner, a vision system, and a motion planner (ROS). ROS and an embedded controller are both used by the Sawyer Robot Controller. Together, these three stages enable intelligent robotics.

3) Cloud Robotics :

In 2020, the COVID-19 pandemic revealed the weaknesses in the global supply chain for products. As autonomous driving technology develops swiftly, wheeled robots are being used in an increasing number of application scenarios. Several companies, including Amazon and logistics and supply chain management companies, have experimented with and developed autonomous delivery robots. The complexity of application situations and the computational requirements of delivery robots are both increasing.Professor James Kuffner of Carnegie Mellon University first proposed the concept of cloud robotics in response to this inquiry in 2010. A cloud robot, which combines robotics and cloud computing, offloads complex computer operations, such as data processing, planning, decision-making, and robot communication, to the cloud. 

As long as the robot has fundamental sensors and a network connection, it can perform complex tasks. Robot cloud platform is a software service platform that integrates Internet and robot technology to provide consumers with professional services such network-based robot access, monitoring, management, data analysis, and control optimisation. Each robot might receive the full processing capacity of the cloud, which would enhance cognition and judgement. All a robot needs to perform complex tasks are basic sensors and network connectivity. 

Robot cloud platform is a software platform that integrates robot and Internet technologies to provide users with professional services such network-based robot access, monitoring, management, data analysis, and control optimisation. Each robot may receive the full processing capacity of the cloud, which can also be used to reinvent the robot development process, deploy the development and test environment, programme the robot as a cloud service, and enhance the robot's memory and degree of judgement.

Delivery robot cloud platform based on microservice 

(Source: https://doi.org/10.1155/2021/6656912)

Turtlebot3

(Source: https://doi.org/10.1155/2021/6656912)

4) Simulation & Virtual Reality :

Recent advancements in simulation and virtual reality (VR) of robot control programming have enabled engineers and developers to create more accurate, efficient, and realistic models of robotic systems, which can be used for a variety of purposes, such as testing and prototyping new robots, training operators, and optimizing performance.

One of the major advancements in simulation and VR of robot control programming is the use of physics-based simulation, which enables engineers to create highly accurate models of robot behavior in real-world environments. This allows them to test and refine their control algorithms and programming in a safe and controlled environment, without risking damage to expensive hardware or endangering human operators.

Robots with Virtual Reality

(Source: https://www.wired.com/story/embodied-intelligence-want-to-really-teach-a-robot-command-it-with-vr/)

Another important development is the use of VR and augmented reality (AR) technologies to provide operators with more intuitive and immersive interfaces for controlling robots. VR and AR can be used to create interactive 3D environments that enable operators to visualize and manipulate robots in real time, which can help to improve their situational awareness and decision-making.

In addition, recent advancements in machine learning and AI are enabling robots to learn from simulations and improve their performance over time. By training robots in virtual environments, developers can quickly test and iterate on new control algorithms and optimize robot performance without the need for expensive physical testing.

Overall, the advancements in simulation and VR of robot control programming are helping to accelerate the development and deployment of robotic systems across a wide range of industries, including manufacturing, healthcare, and logistics, by providing engineers and operators with powerful tools for testing, training, and optimizing robotic systems.

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