In any type of industry, whether it is automotive, unmanned aerial vehicles, energy, logistics, agriculture, or manufacturing, automation brings increasing promises of great gains in terms of efficient utilization of resources, achieving accuracy, and safety. To achieve these gains it is necessary to identify the appropriate sensing technologies that will enhance the contextual knowledge of the equipment’s condition.
As the location or position of an equipment is a valuable input, precision inertial sensors provide accurate location information and help in maintaining accurate positioning. Where mobility is a factor, it is necessary to couple both the location and the contextual sensor information with the application. Operating in a harsh or complex environment often requires determination of position as a critical value. This is where inertial sensors help to make a difference.
Over the years, machinery has evolved from making simple passive movements, to functioning with embedded controls, and now it is moving towards fully autonomous operations, with sensors playing the enabling role. Earlier, for supporting offline analysis or process control, sensors working in isolation were adequate. However, obtaining real-time benefits requires increasingly sophisticated sensor types, while efficient processing requires important advances in sensor fusion. Therefore, increasingly intelligent sensor systems are coming up catering to complex systems on multiple platforms that require the knowledge of states the system has held in the past.
Inertial sensors used with smart machines serve two special functions, one for equipment stabilization or pointing, and other for equipment navigation or guidance. Most systems consider GPS as the most suitable for navigation. However, potential blockages cause significant concerns for many industrial systems. Some systems transition to inertial sensing when GPS is blocked, but this requires the inertial systems to be of sufficient quality to provide the same precision, as did the GPS.
Inertial sensors provide the feedback mechanism in case of servo loop or stabilization for maintaining a reliable positioning such as the antenna pointing angle, construction blade, crane platform, camera, UAV, or farming implement. For all these, the purpose is not only to provide a useful function, but also to deliver a safety mechanism or critical accuracy, even when the environment is incredibly difficult.
In reality, sensor quality matters when good performance is desired. Engineers use sensor fusion for making some correction, for instance, when correcting the temperature drift of sensors, or compensating an accelerometer when correcting for gravitational effects on a gyroscope. In such cases, this helps only in the calibration of the sensor to the environment, but does not improve the ability of the sensor to maintain performance between the calibration points. With a poor quality sensor, the accuracy falls off quickly, as the performance of the sensor rapidly drifts without expensive or extensive calibration points.
Even when using high quality sensors, some amount of calibration is desirable, especially when the aim is to extract the highest possible performance from the device. However, the most cost-effective method of calibration depends on the intricate details of the sensor, along with a deep knowledge of motion dynamics. This makes the compensation or calibration step an embedded necessity for the manufacturer of the sensor.