Robots and IoT devices are similar in that they both rely on sensors to understand their environment, rapidly process large streams of data and decide how to respond.
That’s where the similarities end. Most IoT applications handle well-defined tasks, whereas robots autonomously handle anticipated situations. Let’s consider both from six different vectors:
- IoT – Binary output from stationary sensor. “Is the door open or closed?”
- Robots – Complex output from multiple sensors. “What is in front of me? How do I navigate around it?”
- IoT – Simple data stream of signals handled with well-known programming methods.
- Robots – Large complex data streams handled by neural network computing.
- IoT – Sensors are stationary and signal processing is done in the cloud.
- Robots – The sensor laden robot is mobile and signal processing is done locally and autonomously.
- IoT – The action to take in response to a situation is well defined.
- Robots – Multiple actions could be taken in response to a situation.
- IoT – The application typically does not ‘evolve’ on its own and develop new features.
- Robots – Machine learning and other techniques are used to let the robots ‘learn’ and increase their capacity to deal with new situations. E.g. self-driving cars collectively get smarter as more situations are deal with.
- IoT – Stationary sensors. Processing done centrally where power is readily available. Need for communication channels between sensor and the cloud.
- Robots – Weight, size and power demand are important design considerations. Communication capability is less important.
IoT applications are centralized with edge devices with little intelligence of their own. Low cost sensors transmit signals to a control center in the cloud which analyzes the data stream and decides the action to take. The cost of the central hub can be amortized over thousands of sensor-based applications making IoT applications more affordable. Network connectivity and latency limit the range of applications that IoT can meet.