For many years, robots have been the subject of science fiction. From the groundbreaking films of early silent cinema to the thinking robots of American author Isaac Asimov, robots have been portrayed as machines in the form of humans. These fictional robots boasted intelligence far beyond our own, a remarkable idea considering that they were first imagined before the age of microelectronics, semiconductors, or even transistors.
The manufacturing industry was among the first to understand the robot’s usefulness in the real world. However, when robots finally started entering our lives, they were nothing like those of our imagination. Not only did they lack the sophistication of fictional robots, they also did not look human. This first generation of robots possessed no intelligence but was designed to quickly and precisely perform a limited number of functions. By employing machines to conduct painstaking and repetitive tasks, manufacturers could increase factory efficiencies and improve the quality of their output. For these tasks, a human form was neither needed nor advantageous.
This article examines the role of sensors in industrial robotics and how they help in collecting and analyzing data to enable smart decision-making. We’ll highlight some of the accuracy, power consumption, and cost challenges associated with sensors.
The field of robotics has evolved since the introduction of the first industrial machines. A range of technologies across multiple areas have all matured simultaneously to create a new generation of devices known as autonomous mobile robots (AMRs). At the heart of these AMRs is the processing power of modern microelectronics. Still, they are nothing without the advances in other technology areas, from wireless connectivity to batteries and motors.
AMRs are not self-aware and do not employ true artificial intelligence; however, they are designed to understand the environment around them. Gathering this information through sensors and other inputs, they respond and adapt their actions to accomplish their assigned task. From navigating a busy factory floor to handling sensitive cargo, these robots can act autonomously by employing a form of artificial intelligence called machine learning.
Just as science fiction imagined new uses for robots, the latest generation of AMRs has found applications in a wide range of industries in the real world. There is considerable interest in deploying robots into hazardous situations such as scientific research, disaster relief, and the vacuum of space to perform tasks that are too dangerous for a human worker to attempt. Despite the potential for autonomous robots in a vast array of applications, AMRs have already made a considerable impact on the industrial market.
Robots in the Smart Factory and Beyond
AMRs are enabling the lights-out, or dark, factory—that is, a factory where the need for human activity is so small that the facility can operate in the dark. In these dark factories, AMRs are an integral part of the production line, employed to deliver raw materials around the factory. The independent nature of AMRs allows them to respond to the latest requirements by calculating the optimum route through a complex and dynamic environment. Changes in the production schedule are automatically communicated to the fleet of delivery robots, ensuring that the correct parts are in the right place at the right time.
Beyond the factory, there has been considerable interest in the developments made by the giants of the courier and delivery industry. Many see AMRs as an ideal solution for making deliveries in the final stage of the shipping process, in which the package is delivered to the customer’s doorstep. In this application, AMRs would navigate the sidewalks of our streets to arrive at their destination. The challenge for designers is to create robots that can react safely to the unpredictable traffic found in urban areas.
Autonomous robots are even taking to the air. Unmanned aerial vehicles (UAVs), often known as drones, have been with us for some time but usually require a remote control. However, the latest generation of autonomous UAVs is being used to provide “eyes in the sky” in various applications. Their abilities to stay aloft for long periods, methodically search large areas, and identify anomalies have made these machines highly effective. For example, many precision farming operations employ autonomous UAVs to monitor the conditions of crops from above. Equipped with infrared and other specialist vision systems, these UAVs can identify areas of concern and even apply fertilizers or pesticides without human intervention.
An Array of Sensors
In all these applications, the robot’s interaction with the environment will be critical to its success. Sensors act as more than just the eyes and ears of the autonomous robot, performing several critical roles.
Before an AMR can navigate through its environment, it must be able to detect its own condition. These proprioceptive sensors include gyro sensors, tilt sensors, accelerometers, and thermometers that enable the robot to monitor its attitude, motion, and temperature. These are often combined with pressure and weight sensors that are especially critical for robots that are involved in logistics (for example, ensuring that cargo loads do not create unsafe conditions).
Most of these AMRs use battery packs as the power source for their electric motors, and the current technology of choice is the lithium-ion (Li-ion) battery. The energy capacity and recharging characteristics of Li-ion batteries mean that they are in widespread use across a huge range of industries, though with safety concerns. If a Li-ion battery is damaged or incorrectly charged, the result can be a spectacularly dangerous event called thermal runaway. Accurate temperature sensing is critical to battery management systems, which monitor the condition of these power packs to prevent thermal runaway risks.
These sensors also play an interesting role in the maintenance of the robot. The ability to monitor the robot’s performance and its components over long periods can provide valuable insights into its condition. For example, a steady rise in temperature could alert users to a worn-out motor, while changes in tilt angles during operation could result from a damaged chassis. Collecting and analyzing this data allows operators to conduct pre-planned maintenance, minimizing downtime and maintaining full service.
Passive and Active Sensors
When it comes to detecting the environment in which the robot must move or function, the designer can choose active or passive sensors. Passive sensors detect energy generated by the environment itself. This includes all the variations of electromagnetic energy, including visible light and infrared radiation, as well as physical conditions such as sound or atmospheric pressure. Passive sensors collect this information, which the robot will use to create a model of the conditions around it.
Passive sensor options for AMRs include temperature and pressure sensors, which prevent the robot from entering unsafe areas, and vision systems—usually a combination of cameras that capture information from the outside environment using visible or infrared light. When processed by the latest embedded computers, very accurate visual models of the environment can be created, allowing the robot to navigate safely around obstacles and hazards.
Active sensors work by creating energy which is then emitted into the environment. The sensor collects information about how this energy interacts with its surroundings. The most familiar application of the active sensor is radar, in which a pulse of radio frequency (RF) radiation is transmitted, and its reflection is measured. The time taken for the pulse to travel to the target and return provides an accurate measurement of distance.
AMRs use a range of active sensors to further enhance the model of their surroundings. Light detection and ranging (lidar) or lasers provide alternatives to radar, in which RF transmissions are replaced by light to detect objects. Although light-based sensors can be affected by atmospheric conditions such as dust or fog, they provide greater resolution than radar.
Making Sense of the World
Regardless of which sensor technology is employed, the robot will use a method called simultaneous localization and mapping, or SLAM. SLAM is a technique that autonomous robots and vehicles use to build an area map and simultaneously localize their position within it. Using the information generated by an array of sensors, SLAM technology provides robots with the information they need to carry out tasks such as path planning and obstacle avoidance.
Even more critical will be the role of sensors in public safety. As AMRs become familiar sights on our streets and in our factories, they will be entrusted with evermore critical tasks that they must perform without supervision. Both active and passive sensors will play a key role when deploying robots in public spaces or other areas where they might interact with people.
Sensors are more than the eyes and ears that allow robots to perceive and understand their surroundings. While AMRs are not self-aware, they are designed to respond to the environment around them, adapt their actions, and complete assigned tasks. Whether designers are creating robots to deliver pizza within 30 minutes or the most complex search-and-rescue machines, the array of sensors incorporated in AMRs will allow them to achieve their full potential. They are critical to the robot’s success as they perform critical roles such as detecting its own condition, monitoring battery health, and maintenance.