April 13, 2016      

As the revolution in robot development continues over the next decade, the increased connectivity of robots, and related network sensors, to the Internet of Things (IoT) will become commonplace. The advantages of doing so are many, including cloud computing and data storage, distributed sensor data processing and analysis, reduced power consumption, lower robot unit costs, and better communication, awareness, and collaboration between other autonomous robots and users of the network sensor data.

By connecting the autonomous robot to the cloud, and achieving the Internet of Robotic Things (IoRT), the industrial and consumer applications for robots become infinite. And moreover, with the integration of artificial intelligence (AI) in future robot generations the dawn of self-aware robots, that can independently think, learn, reason, predict, and effectively mimic human life, will be upon us.

With this said, there are major challenges still ahead in the field of robot development. First and foremost, there are serious security and safety concerns connecting autonomous robots to the cloud. But secondly, and in many respects just as important, there are necessary technology improvements required in how robots move, see and sense their surroundings. Specifically, there is still needed improvements in sensor technology, wired and wireless communication internal and external to robots, and autonomous robotic system algorithm development.

To achieve human-like qualities, and in particular fluid limb movement, robot design engineers need to develop robots with fine motor control of limbs and end effectors (hands). More specifically, they need to develop robust solutions for providing energy efficient motor control for each joint in the robot.

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To meet these goals, robot engineers need high resolution, high accuracy, motor position feedback information for ensuring robot limbs move to the precise positions they are commanded to do so by the robot’s central processing unit (CPU), or via a cloud network processing unit. In addition, the position feedback sensors need to be fast, small and co-located with robots’ joint motors.

To meet these stringent fine motor joint control requirements, sensor suppliers like ams are developing contact-less, low power, magnetic position sensor integrated circuits (ICs) for providing the fine joint position feedback information and for aiding in joint motor commutation. The same magnetic sensors also support very high bandwidths and rotational shaft rates, and have very low latency, thus enabling robots to support precise and fluid limb movement.

With each magnetic position sensor IC, a small target magnet is paired with it. The target magnet is affixed to the end of each joint motor shaft (for joint motor commutation feedback) and/or gear (for joint position measurement). As the motor shaft or gear turns, the magnetic position sensor IC measures the angular change in magnetic field associated with the target magnet. The result, highly accurate and repeatable absolute rotary position information of robot joint motor shaft and gear position is measured and transmitted back to the robot’s CPU, or to a cloud network processing unit.

Magnetic position sensor ICs are an attractive alternative to optical encoders, resolvers and other rotational sensor technologies historically used in industrial robots for a number of reasons. First, they provide precise and repeatable measurements in even the harshest of conditions, such as in extreme temperatures and humidity levels, dirty or moist environments, and in high vibration applications. Second, they are packaged in ultra-small surface mount and thru-hole packages, thus enabling smaller robot joints and reducing overall robot weight. Third, today’s newest magnetic position sensor ICs offer in-situ programmability, and high speed digital interfaces that connect seamlessly to common microprocessors used in robots. Fourth, they are contact-less so they don’t wear out. Lastly, they cost a fraction of alternative rotational sensor technologies.

Besides the advantages already mentioned, new state-of-the-art magnetic position sensors being developed, such as at ams AG, also support new and unique features in magnetic position sensors ICs that enable them to provide further enhanced performance and improved autonomous robot safety. For example, ams AG’s latest magnetic position sensors ICs (e.g. the AS5047P) provides nearly latency-free position feedback information via its unique DAECTM feature, a feature and benefit that is highly important in ensuring highly responsive joint and limb movement relative to on-board robot CPUs commands, or commands issued from an IoRT cloud environment.

ams’ magnetic position sensors ICs also uniquely provide stray field immunity, a feature that is inherent in the design and architecture of the sensors ICs. This feature provides the benefit for ensuring that the magnetic position sensor measurements cannot be influenced by stray magnetic fields. For example, when an autonomous robot may possibly walk by or stand near a high current carrying cable, or in the vicinity of a large magnetic field environment. Other magnetic sensor ICs may have their measurements corrupted due to the stray magnetic fields, and provide invalid or skewed joint position feedback measurements back to the robot’s CPU or IoRT cloud environment, thus causing an unsafe robot operating condition. With ams AG magnetic position sensor ICs, this dangerous safety situation is not possible, as the sensor ICs reject any stray magnetic fields due to their unique differential measurement techniques of the target magnetic field.

To conclude, magnetic position sensor ICs are the ideal solution for providing precision angle measurements in robot joints. Consequently, they will play a major role in enabling the next generation of autonomous robots to have fine joint motor control and nearly human-like limb movement. They are the low power and low cost alternative to traditional rotational measurement technologies. They also provide the high speed refresh rates, near zero latency, and high speed digital interfaces that alternative sensor solutions do not have, to support autonomous robots in both power conservation and real time position feedback to the cloud in an IoRT environment.