Technology was developed that not only allows wheels to “know” when and how to rotate, but also enables them to work together in interactive teams. The new technology can be used wherever there is a need for additional support when pulling, pushing, or driving a system that uses wheels. Potential applications range from mobility aids such as wheelchairs, to dollies, wheelbarrows, and shopping carts. Monitoring the data generated when the motors inside the wheels rotate allows the motors and wheels to be controlled without the need for additional sensors.
The wheels immediately recognize when there is increased or reduced loading on the right- or left-hand side of a wheeled system, and they are aware of any changes in their own position. The technology can be used in any application involving motor-driven wheels.
Smart motors generate operational data without the need for additional sensors; the motor itself is turned into a sensor, creating a new category of sensor. The data is used to monitor whether the motor is operating properly, or whether there are indications of faults or signs of wear. For example, researchers observed how the electromagnetic field is distributed at particular locations within the motor, and how this field changes while the motor is running.
Introducing the technology into multi-wheeled applications will enable the wheels to be controlled individually. This is all done automatically using a tiny microcontroller that gathers the data from the individual drive motors, and then calculates when and with what power a particular motor needs to switch on.
The researchers have carefully studied how the measured data correlates with specific motor states, and how a specific measured quantity changes when a wheel rotates. The more data collected on the motor that drives the wheel, the more precisely the motor and the wheel can be controlled.
The huge amount of motor data is analyzed to identify signal patterns that can be used to infer knowledge about the current state of the motor or to flag changes. Mathematical models are being developed that simulate the various motor states. If the signals coming from the wheels change, the control system can identify the underlying change in the motor's state, and can almost immediately respond with appropriately programmed commands. When a number of these “sentient” motors are connected via a databus system, the wheels can work together in one integrated network.