An autonomous mobile robot needs more than motors and a navigation program to move safely. It needs reliable information about where it is, what is around it and whether the path ahead is clear.
That information comes from sensors.
A warehouse AMR may need to detect pallets, people and other robots while keeping track of its position. A delivery robot operating outdoors has different requirements, including changing light, weather and longer detection distances.
There is no single sensor that can handle all of these tasks well. In most AMR designs, several sensors work together.
The first question is not which sensor has the highest resolution. It is where the robot will operate.
An indoor warehouse usually provides relatively controlled lighting and a defined floor surface. An outdoor mobile robot may encounter sunlight, rain, uneven ground and objects at much greater distances.
The environment affects the choice of sensing technology.
For example, a sensor that works well for short-range obstacle detection inside a factory may not provide enough range for an outdoor autonomous vehicle.
The robot's speed matters too. A faster robot needs enough sensing distance and processing time to detect an obstacle before it becomes a problem.
LiDAR is widely used in AMR navigation because it can provide distance information about the surrounding environment.
A LiDAR sensor can detect walls, equipment, racks and other objects and use this information to build or update a map.
For an indoor warehouse robot, 2D LiDAR may be sufficient for navigation at floor level. More complex environments may benefit from 3D LiDAR, particularly when objects exist at different heights.
LiDAR is useful, but it should not automatically be treated as the only sensor. Objects with unusual surfaces, limited visibility or specific positioning requirements may require additional sensing technologies.
Cameras give the robot information that distance-only sensors cannot provide.
An RGB camera can help identify objects, signs, people and visual markers. With suitable computer vision software, the robot can classify what it sees rather than simply determine that something is present.
This can be useful when an AMR needs to distinguish between a person, a pallet and another piece of equipment.
The downside is that camera performance depends heavily on lighting and image processing. A dark warehouse, direct sunlight or glare can make visual recognition more difficult.
A 3D camera provides depth information along with visual data.
This can be particularly useful when an AMR needs to understand the distance and position of nearby objects.
ToF cameras, stereo cameras and structured-light systems are some of the technologies used for 3D sensing.
For example, a compact 3D camera can help detect an object directly in front of a mobile robot, estimate its distance and provide additional information to the navigation system.
For short- and medium-range perception, this can complement LiDAR rather than replace it.
Ultrasonic sensors are relatively simple compared with LiDAR and 3D vision systems.
They measure distance using sound waves and are often used for short-range detection.
An AMR can use them to monitor areas that may be difficult to cover with other sensors.
They generally do not provide a detailed representation of the environment, so they are better viewed as an additional sensing layer rather than the main navigation sensor.
Their low complexity and cost can make them useful in applications where close-range detection is sufficient.
An IMU measures motion-related information, typically using accelerometers and gyroscopes.
It can tell the control system how the robot is moving and changing orientation.
This information becomes particularly useful when combined with other positioning systems.
For example, the robot can combine IMU data with wheel encoder information and LiDAR or camera measurements to improve its estimate of movement.
An IMU by itself does not provide a complete solution for long-term positioning because measurement errors can accumulate over time.
Wheel encoders measure the rotation of the robot's wheels.
From this information, the control system can estimate how far the robot has moved.
This approach is known as odometry.
Encoders are relatively straightforward and are commonly used in mobile robots, but wheel slip can introduce errors. A wheel may rotate even though the robot has not moved the expected distance.
For that reason, encoder data is often combined with other sensors.
GPS or GNSS becomes more useful when the robot operates outdoors.
An outdoor delivery robot, agricultural robot or inspection vehicle may use satellite positioning as part of its navigation system.
However, GPS is not reliable everywhere. Buildings, trees and other structures can interfere with satellite signals.
Indoor AMRs therefore normally rely on other positioning methods.
Outdoor systems may combine GNSS with cameras, LiDAR, IMU and other sensors to achieve more reliable positioning.
The most important part of an AMR sensing system is often not an individual sensor but how the information from different sensors is combined.
Consider a robot using:
LiDAR for environmental mapping
Cameras for visual recognition
3D sensing for nearby depth information
IMU for motion
Encoders for wheel movement
GNSS for outdoor positioning
Each sensor provides a different piece of information.
The navigation system can combine these inputs to create a more reliable estimate of the robot's position and surroundings.
This is known as sensor fusion.
Sensor placement matters almost as much as sensor selection.
A technically excellent sensor cannot detect something that is outside its field of view.
Sensors should be positioned to cover the areas most relevant to the robot's movement. Low obstacles, objects close to the floor and objects at different heights may require particular attention.
The mounting position should also consider vibration, dirt, accidental impacts and the possibility of people or equipment blocking the sensor.
A common mistake is choosing a sensor based only on its maximum advertised range.
The more useful question is whether the sensor provides enough information for the robot to react safely at its actual speed.
A slow-moving indoor AMR may have plenty of time to respond to an obstacle detected a short distance ahead.
A faster robot needs greater detection distance.
Processing time also matters. The sensor may detect an object quickly, but the robot still needs time to process the data and adjust its movement.
Different sensors react differently to the environment.
Cameras depend strongly on visible light.
ToF cameras use infrared light and can be affected by strong ambient infrared radiation.
LiDAR can encounter difficulties with certain reflective, transparent or highly absorbent surfaces.
Ultrasonic sensors can behave differently depending on object shape and material.
Understanding these limitations helps prevent a sensor combination from having the same weakness across the entire system.
AMRs are often designed around limited battery capacity and compact mechanical structures.
A large sensor with high power consumption may not be practical for a small robot.
Power requirements become especially important for robots that operate for many hours without charging.
The sensor should provide enough performance for the application without adding unnecessary weight, power consumption or processing requirements.
Before selecting a sensor, look at the complete system rather than one specification.
Important factors include:
Detection range
Measurement accuracy
Field of view
Frame rate
Resolution
Ambient-light performance
Operating temperature
Vibration resistance
Power consumption
Communication interface
Software and SDK support
Physical size
For production equipment, long-term availability and supplier support should also be considered.
There is no standard number.
A simple indoor robot may work with wheel encoders, an IMU and a LiDAR or camera system.
A more advanced AMR operating around people may need several sensing technologies to cover navigation, obstacle detection and safety requirements.
Adding sensors is not always the answer, either. More sensors create more data and can increase software complexity.
The goal is to use enough complementary sensing to cover the robot's actual risks and navigation requirements.
The best AMR sensor system starts with the operating environment and the robot's job.
For indoor navigation, LiDAR, cameras, IMUs and wheel encoders are common building blocks. 3D cameras can add useful short-range depth information, while ultrasonic sensors can provide another layer of close-range detection.
Outdoor robots may add GNSS and sensors designed to handle changing environmental conditions.
Rather than choosing the most advanced sensor available, select a combination that provides the required range, accuracy and reliability while fitting the robot's size, power budget and processing capability.
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