Sensores para robótica 2023-2043: tecnologías, mercados y pronósticos

Robots are, in essence, machines with autonomy. To enable their autonomy, a suite of sensors is needed to achieve the requirements of different tasks such as autonomous navigation, object detection, proximity sensing, and many others. Sensors have been widely used in a number of industries, and thanks to the increasing technology readiness, the costs of various sensors have gradually decreased over the past few years, enabling greater adoption within robotics. Robots, as highly integrated machines, contain many sensors ranging from optical encoders, and current sensors, to inertial measurement units, cameras, LiDAR, and many others.

 

Depending on the data collected, sensors can be segmented into two primary categories: proprioceptive and exteroceptive sensors. Proprioceptive sensors collect internal data such as speed, torque, and position. These sensors are usually used for robotic control. On the contrary, exteroceptive sensors collect external data (surroundings) and sense environmental parameters, such as the distance of an obstacle, external force exerted on the robot, and many other inputs. Tactile sensors, vision sensors (cameras), and proximity sensors (e.g. LiDAR, radar, ultrasonic sensors, stereo cameras, etc) are several typical examples of exteroceptive sensors. Driven by the increasing adoption of robots and the increasing demand for 'intelligent' robots, IDTechEx concludes that sensors for robotics will experience a rapid growth over the upcoming two decades. IDTechEx's latest report 'Sensors for Robotics: Technologies, Markets, and Forecasts 2023-2043' takes a deep dive into nine types of common sensors, nine types of robots, and 29 applications with an in-depth analysis of the key enabling technologies, players, and markets with granular forecasts showing the market size and sales volume trend for the next 20 years. The chart below shows an overview of the robots, sensors, and tasks covered in the report.

 

Autonomous mobility has gained significant momentum over the past decade thanks to the development of autonomous driving technologies. As one of the most important factors for robot autonomy, autonomous mobility enables robots to move independently with minimum human supervision and fulfill many tasks such as logistics, delivery, weeding and seeding, mapping, and exploration. Autonomous mobility consists of two steps: mapping and navigation. A robot initially needs to map the environment to construct a model made of point clouds and plan a moving trajectory/path, then follow the proposed trajectory and use navigation sensors for localizing. Both steps require sensors for object detection, navigation, and collecting data from the ambient environment. In practice, depending on the working environments, different navigation and mapping sensors are often used together, and sensor fusion algorithms are implemented to process data from different sensors (sensory modalities). Typical navigation and mapping sensors include LiDAR, radar, cameras, GPS/GNSS, and ultrasonic sensors (1D and 3D). The table below compares the advantages and disadvantages of some of these sensors, a more in-depth technology analysis can be found in IDTechEx's report - 'Sensors for Robotics: Technologies, Markets, and Forecasts 2023-2043'.

 

Aside from autonomous mobility, safety always comes as the overarching priority for any robot, especially with increasing human-robot interaction (HRI) and the complexity of tasks. IDTechEx believes that the regulations are expected to get increasingly strict to ensure a high safety level of HRI. In order to make robots comply with the safety requirements, robots need to be able to sense collisions and distances between robots and human operators. When the human operators/objects are in proximity, the robot needs to slow down or stop. To enable this, collision detection and proximity sensors are usually used.

 

With the advancement of sensor technology, the boundary between collision detection and proximity detection has been blurred. The fundamental difference between collision detection and proximity detection is the distance between the objects and sensors/robots. From the technology point of view, proximity sensors are usually based on one or more of five detection principles that are light reflection, time of flight, triangulation, capacitive, and ultrasonic waves. The chart below compares the different detection principles of several commercial sensors, outlining the response time, and maximum sensing range of each detection method. The general trend here is that robot end-users would want a sensor with a fast response, a large maximum sensing range, and a small footprint. However, depending on certain applications, some factors can be compromised. For instance, indoor autonomous mobile robots for logistics or material handling might not need a sensing range as large as outdoor mobile robots would do.

 

 

Based on the analysis of sensors in 29 robotic applications, IDTechEx concludes that the market will grow very rapidly. Given the large market size of robots and automated machines, IDTechEx believes that the market size of sensors will have a 20-fold increase and exceed $US80 billion within 20 years. Each category of robot and sensor has different needs and market growth. The massive market size and fast growth represent a significant number of opportunities, which are analyzed in detail in the report 'Sensors for Robotics: Technologies, Markets, and Forecasts 2023-2043'.

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