Introduction

Autonomous mobile robots (AMRs) are essential for the smart factories and warehousing of the future, playing a pivotal role in shaping automated, sustainable, and cleaner factories of the future. AMRs enhance efficiency, reduce waste, and optimize utilization in industrial settings. While factories of the future may perhaps be purpose built and optimized for AMRs to operate in, adapting these robots to existing warehouses and factories presents challenges. The primary hurdle for AMRs involves two critical components: efficient path planning (determining the optimal path) and precise localization (continuously updating its position within its environment).

This article focuses on indoor navigation within a GPS denied closed environments. AMRs utilize an array of sensors and algorithms for localization and navigation. These include visual sensors like cameras, LIDAR, and radar, as well as odometry sensors such as wheel encoders and IMUs. Each sensor modality has its own unique advantages in terms of range, accuracy, and sensory information. The combination of these sensors ensures comprehensive data for effective robot localization in dynamic environments. While an array of sensors is a must for full autonomy, this article highlights the use cases and challenging environment in which AMRs operate and how IMUs aid in precise localization, which is crucial for navigation and autonomy.

What is an IMU?

IMUs are miniature devices made of microelectromechanical systems (MEMS) devices. They usually consist of the following elements:

• Triaxial accelerometer: Accelerometers measure acceleration with respect to the Earth’s gravitational field. In an IMU, triaxial accelerometers are used to measure the x, y, and z axes (see Figure 1).

Figure 1. Acceleration measurements on the x, y, and z axes.

• Triaxial gyroscope: Gyroscopes measure the rate of rotation providing angular velocity in each of the three axes. Triaxial gyroscope enables measurement of the robot’s angular velocity (ωx, ωy, ωz) along the x, y, and z axes (see Figure 2).

Figure 2. Gyroscope measurements on the x, y, and z axes.

• High performance magnetometer: It provides magnetic field measurements, essential for accurate orientation estimation in challenging environments. Although not popular, a magnetometer is available in some of the legacy IMUs.

• Other: A temperature sensor to compensate for temperature variations and a barometer to measure pressure.

Functional block diagram of IMU

• A typical IMU not only includes gyroscopes, accelerometers, and a temperature sensor, but also analog-to-digital conversion to extract the measurements and temperature compensation (see Figure 3).

Figure 3. Typical functional block of an IMU.

• An IMU features onboard preliminary filtering algorithms such as FIR (finite impulse response) onboard.

• Calibration and compensation correct any misalignment or sensor biases.

• The user has the option to rotate (dƟ) from the IMU module internal axis to match the robot’s frame of reference before transmitting the final data.

Why are IMUs beneficial for AMRs?

• Real-time localization with high update rates: Autonomy and real-time navigation are crucial elements in a robot’s operational environment. Perception sensors, however, typically operate with a restricted update rate, ranging from approximately 10 Hz to 30 Hz. In contrast, IMUs boast the capability to deliver high fidelity positional output, reaching up to 200 Hz. This higher update rate significantly enhances the system’s reliability in swiftly adapting to rapid changes in orientation within a dynamic environment, facilitating prompt responses. The accelerated update rate also empowers AMRs to provide an estimated pose during the brief intervals between other measurements. As a result, IMUs play a pivotal role in achieving real-time localization, surpassing perception sensors with update rates that are 10× faster.

• Dead reckoning: IMUs serve as the backbone for dead reckoning, a navigation technique to estimate the current position based on a previously known position. Constantly providing data on position, orientation, and speed over elapsed time, IMUs enable precise estimation, contributing to reliable navigation for AMRs.

• Compact size and weight: The compact size and lightweight design of IMUs make them ideal for integration into various mobile robot configurations. For instance, the Analog Devices ADIS16500, with a footprint of only 33.25 mm × 30.75 mm, ensures efficient placement without compromising the robot’s maneuverability.

• Robustness in diverse environments: IMUs are relatively resistant to electromagnetic interference and can operate in a variety of environments, including outdoor and indoor settings. This makes them suitable for a wide range of applications.

• Enhanced reliability through accelerated update rates: With perception sensors typically limited to update rates of ~10 Hz to 30 Hz, IMUs stand out by providing a high fidelity positional output of up to 4 kHz raw data. This increased update rate enhances reliability, especially in dynamic environments, enabling AMRs to respond quickly and help estimate the pose in the short time between these other measurements.

Why IMUs are essential for AMRs despite the availability of vision sensors

An AMR, as depicted in Figure 4, commonly features a variety of visual sensors, such as time of flight (ToF), camera, LIDAR, etc. Despite the rich dataset provided by visual odometry, the necessity for IMU persists. The following scenarios explore some of the reasons why:

Figure 4. Sensor stack of an AMR.

• AMR navigating a feature-sparse corridor: Simultaneous localization and mapping (SLAM) algorithms essentially operate by matching observed sensor data, which is stored in the map to localize within the map. When an AMR travels through a long corridor (see Figure 5), it is bound to lose its position quickly. Due to a lack of distinctive features such as straight walls with uniform color, texture, or reflectivity, SLAM struggles to localize precisely. In this case, IMUs act as a valuable guidance system by providing a heading and orientation information.

Figure 5. AMR losing visual odometry in a featureless corridor.

• Navigating across vast open environments: Range limitations: When operating in a large open space such as a huge warehouse (say 50 m × 50 m), AMRs have difficulty localizing as unique features extend beyond the sensor range (maximum reach of lidars is typically around 10 m to 15 m). As shown in Figure 6, the AMR’s odometry has already been lost due to the large space. Additionally, warehouses often exhibit uniform features, thus rendering it difficult for visual sensors. In such scenarios, IMUs and wheel encoders are the only reliable sources for precise local localization.

Figure 6. Limited field-of-view (FoV) of sensors, AMR unable to localize in a large open space.

• Navigating on a slope: While maneuvering on a slope, the traditional SLAM algorithm encounters a challenge when relying on LIDAR, as the 2D point data does not show gradient information. Consequently, slopes are misconstrued as walls or obstacles, leading to higher cost maps. As a result, conventional SLAM approaches with 2D systems become ineffective on slopes. IMUs help to solve this challenge by extracting gradient information (Figure 7) to effectively negotiate navigating on a slope.

Figure 7. AMR moving on a slope.

• Environmental factors while navigating: Sensitivity to environmental factors: LIDAR sensors can be sensitive to various environmental factors, such as ambient light, dust, fog, and rain. These factors can degrade the quality of the sensor data and, in turn, affect the performance of the SLAM algorithm. Similarly, other sensor modalities do get affected by reflective surfaces and dynamic moving objects (other AMRs or workers), thus further confusing SLAM. Table 1 summarizes how the environment affects different sensor modalities. IMUs can reliably operate in a variety of environments, making them a versatile choice for mobile robots.