As Robots and IoT devices are alike, both of them need sensors to know their environment, rapidly process large flows of data and fix on how to respond.

While most IoT applications handle well-defined chores, robots individually handle probable situations. Let’s judge both from six different vectors:

  1. Sensor

  • IoT – Double output from a stationary sensor. “Is the door open or closed?”
  • Robots – Multifaceted output from many sensors. “What is in front of me? How do I navigate around it?”

  1. Handling

  • IoT – The signals of the simple data stream are handled with widely used programming methods.
  • Robots – Large complex data streams are controlled by neural network computing.

  1. Mobility

  • IoT – Sensors are still and signal processing is carried out in the cloud.
  • Robots – The sensor loaded robot is movable and signal processing is carried out locally and autonomously.

  1. Response

  • IoT – The action to take in response to a circumstance is well defined.
  • Robots – Several actions can be taken in response to a circumstance.

  1. Learning

  • IoT – The application usually does not come out autonomously and give rise to new features.
  • Robots – Robots need a machine learning and other techniques ‘learn’ and boost their ability to deal with new circumstances. E.g. self-driving cars collectively get smarter as they have to deal with more situations.

  1. Design

  • IoT – Stationary sensors. Processing is carried out centrally where power is readily available. The necessity of communication channels between the sensor and the cloud.
  • Robots – Weight, size and power demand are major design concerns. Communication capability is less significant.

Topology

IoT applications are organized with edge devices with little insights of their own. Low-cost sensors help pass on signals to a control center in the cloud that analyzes the data stream and chooses the action to take. The expense of the central hub can be amortized more than thousands of sensor-based applications making IoT applications increasingly affordable. Network connectivity and inactivity check the variety of applications that IoT can meet.

Both robot and drones function in a decentralized model. They can show a high degree of decision-making capacity of their own and are able to operate on their own even if after getting disconnected.

Thinking is difficult

Whenever you want to pick up a particular object, your eyes try to scan your surroundings and your brain recognizes that object. Signals are transmitted through nerves to your arm muscles instructing them to move to that object. At the same time, visual signals from your eyes give constant feedback on your hand’s position to move it exactly to the object. Tangible feedback from your hand proves when the object has been picked up. Here one can think a great deal of signal dispensation and constant control for such a simple task!

To do a similar task, programming a robot needs visual sensors (cameras) to provide constant visual input, a graphical processing unit (GPU) to processes the stream of visual signals and a central processing unit (CPU) to control the functions of the motor.

Robots need various high-resolution sensors which produce complex data streams. For this, there is a requirement of a lot more processing power and several neural networks to process them in parallel. “Neural networks are loosely modeled on the human brain with thousands of small processing units (neurons) arranged in layers. They identify patterns based on a learning rule.”

Learning as you go

IoT devices handle specific tasks successfully. Its work can be simple like sensor detecting whether a door is open or not and the central hub sending an alert to inform the owner that the door is open. Robots should respond to unpredicted conditions that their developer may not have expected. This could be like how to navigate to an obstacle in their way. To handle these situations, Artificial Intelligence (AI) platforms and machine learning help robots.

Systems design

Hardware charges for designing robots are reducing as their processing power increases. The Jetson Nano Developer Kit charges $99 to build robots and runs many neural networks in parallel for applications like image classification, object recognition, segmentation, and speech processing. It includes an NVIDIA CUDA-X™ AI computer which sends 472 GFLOPS of total performance for AI workloads on 5 watts power. This facilitates a robot to work longer before requiring a recharge.

There is a requirement of specialized software for programming a robot. Developers change complex robotic tasks into smaller, simpler assignments. This is carried out with computational graphs and an entity component system, for example, the Isaac Robot Engine. The robotic application is made with smaller modules (Gems) for sensing, planning, and actuation. These allow robots to deal with obstacle detection, stereo depth estimation, and human speech recognition.

Train your robots properly

Like humans, robots advance their motor skills with practice. These require a test bed where their training can be tested and repaired. Virtual test beds are better than physical ones as it is not possible to create a physical demonstration of every environment where the robot might function. Isaac Sim is a virtual robotics laboratory and a high-fidelity 3D world simulator. Developers train and check their robots in a detailed, realistic simulation reducing the costs and expansion time.

Robots advance as their decision models is modified to cover new circumstances that they come across. Robots function according to models they were programmed with, however, they also show details of unanticipated situations back to the cloud for review. This allows developers to advance the robot’s decision-making model to deal with the new conditions. The total feedback increases as more robots are installed, raising the speed at which all the robots collectively get “smarter.”