Distributed control systems simplify the 3C design of robots (Part 1)

By Warren Miller, Mouser Electronics

You may be familiar with Asimov’s Three Principles of Robotics, which are famous for being mentioned in many of his science fiction novels. However, you may not be aware that there are three additional organizing principles (or perhaps not strictly speaking principles)—the 3Cs of Robotics—that are important in modern robot design.

Communication, command and control (sometimes referred to as 3C) used in the military are also three key principles for collecting, processing and disseminating information through distributed elements. The technology used to realize robots today can be considered as the result of combining distributed force elements - although it mainly relies on mechanical force, these "C" elements can also be applied to the design of robotic distributed systems.

Figure 2: Using “3C” makes it easier to control decentralized robotic systems. (Source: IStockPhoto.com)

1. Communication

Communication is probably the most easily understood element of a distributed system design. Multiple elements for imaging, positioning, environmental monitoring, power, and motor control (to name just a few) need to "talk" to each other and to a centralized controller that manages and coordinates the detailed activities to accomplish a task. Standard communication interfaces, wired or wireless, are used to transmit information from the edge of the system to the central controller. When the central controller needs to send instructions to an edge element, perhaps to send a sensor update or add a stepper motor, the same interface is used. Microcontrollers (MCUs) are often the brains inside the end nodes, and they support a variety of communication interfaces to simplify data transfer.

It is often convenient to minimize the amount of data flowing from the edge to the central controller, so additional processing power is often moved to these end nodes. The end nodes do things independently, eliminating the need for intermediate data flows. As edge devices become more autonomous, only critical updates or requests require involvement from the main controller. For example, sensor data often needs to be processed to determine if it is within the allowed range. If every measurement was sent to the central controller, this would create a huge data flow and require additional processing power from the controller. If the sensors can complete the processing autonomously and only report to the controller when the text is out of the knowledge range (or heading in that direction), huge amounts of central controller data bandwidth and processing power can be saved.

For complex sensing algorithms, multiple data streams may need to be combined and processed to see if the central processor should take some action. For example, image information along with speed and distance measurements may show that an object is moving along a certain motion path. If these contexts can be combined, perhaps using a decentralized local controller that controls several key edge sensors, a warning can be sent to the central controller and a response can be taken.

Often these complex functions require advanced signal processing capabilities, which are available even on inexpensive MCUs. For example, in the Texas Instruments MSP430 MCU family, even many of the inexpensive devices have parallelization and acceleration capabilities. This capability enables simple digital signal processing (DSP) algorithms, which are often used when combining multiple sensor contexts, called sensor fusion, for intelligent and independent computing. Many MCUs offer ultra-high-performance DSP capabilities, which are often used for more complex tasks such as imaging systems. Simple MACs are sufficient for a wide range of low-level tasks and often significantly improve execution efficiency using more complex devices.

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