This article presents a novel solution for the range-only simultaneous localization and mapping (RO-SLAM) that builds a map of the ultrawideband (UWB) anchors affixed to the unknown positions of the environment and localizes an autonomous mobile robot (AMR) at the same time. Delays in the initial positioning of the anchors are common in the RO-SLAM. To solve the delay problem, three UWB sensors named as the trilateration UWB tag (TUT) system on AMR are proposed. The TUT system makes it possible to estimate the range and bearing of each anchor without any delay at the first observation of three distance measurements from the UWB tags. However, the concentration of three UWB tags at a local point on AMR may degrade the performance of the range and bearing estimations. In order to improve the accuracy of the estimation, a constrained least-squares (CLS) algorithm is proposed in this article. The range and bearing estimations serve as the observational measurements for the proposed SLAM algorithm in this article, enabling accurate localization with computational efficiency. The performance of the proposed method is analyzed and demonstrated through simulations and experiments.

Accuracy of an indoor positioning system (IPS) under non-line-of-sight (NLOS) conditions is an open research topic. Performance of the IPS is significantly degraded due to a positive bias in range measurements caused by the NLOS condition between tags and anchors. This study proposes an anchor selection algorithm based on residual error, referred to as residual-based anchor selection (RAS), to mitigate the NLOS effect and improve the positioning accuracy of IPS. The proposed RAS algorithm does not require any prior information about the environment and utilizes only the measured distances between tags and anchors at each time instance to eliminate NLOS measurements, so its positioning performance is always guaranteed in both static and dynamic obstacles in environments. Ultrawideband (UWB) sensor is employed to validate the proposed algorithm because of its advantages for an IPS such as high accuracy ranging, mitigation of multipath effects, and extended measurement range. The effectiveness of the proposed method is addressed through simulation and experiment.

Autonomous mobile robots (AMRs) play a crucial role in smart factories and Internet of Things (IoT) applications. This paper presents a high-accuracy localization system called the global UWB system (GUS) for the AMR based on ultra-wideband (UWB) distance measurements. By using two UWB modules (tags) on AMR, it is possible to estimate both position and heading angle simultaneously, which can solve the problem of error accumulation over time in internal sensors. A new tightly coupled algorithm called baseline constraint extended Kalman filter (BC-EKF) is proposed. The baseline is the actual distance between the two tags that is used as a constraint for the measurement process. The relationship between the baseline and the heading angle error is also discussed to ensure reasonable deployment of the tags. The proposed localization method is persistent as value of the geometric dilution of precision (GDOP) increases. This helps to secure the performance of the localization method even in tight workspaces where the number of fixed UWBs (anchors) is limited. The simulation and experiment results demonstrate the localization performance of the proposed method.

The indoor positioning system (IPS) plays a crucial role in many applications of the Internet of Things (IoT). The trilateration is the most common and widely used method for the IPS because of its computational efficiency and ease of implementation. However, the accuracy of trilateration is affected by the geometric dilution of precision (GDOP). In this article, we propose a constrained least-squares trilateration (CLST) for the IPS that achieves high positioning performance with efficient computations even under severe GDOP conditions. The ultrawideband sensors are used to test the proposed positioning algorithm in this study. The positioning performance of the proposed method is demonstrated by simulations and experiments.

Autonomous mobile robots (AMRs) have revolutionized various aspects of our daily lives and manufacturing services. To enhance their efficiency, productivity, and safety, AMRs are equipped with advanced capacities such as object detection and tracking, localization, collision-free navigation, and decision-making. Among these technologies, 2D LiDAR commonly stands out as the prevailing choice, showcasing remarkable accomplishments in practices. Obviously, the precision of mentioned modules is affected by the accuracy of 2D LiDAR observed data. Typically, 2D LiDAR intrinsic parameters are adequately calibrated during the manufacturing process, while the extrinsic parameters should be intervened by user at the application level. Previous research has predominantly emphasized extrinsic calibration for sensor fusion, given its perceived appeal over individual 2D LiDAR extrinsic calibration. However, it is important to note that a multi-sensor system usually includes more favorable geometric constraints between different sensor datasets. In contrast, a 2D LiDAR sensor only provides position information in a 2D horizontal plane, resulting in fewer features or constraints when used alone. Besides, in the realm of multi-sensor calibration, the direct incorporation of observed data within the robot base coordinates is often overlooked, despite it is necessary for AMR applications. This paper presents an extrinsic calibration for coordinates of a single 2D LiDAR in AMRs’ base coordinates directly, which ensures accuracy as well as easy tool installation, fast and simple observation for data samples without supports from other sensors. The proposed method has been verified through both simulation and real experiments.

This study presents a three-dimensional inverted pendulum system using three symmetrically arranged vertical reaction wheels for balance control and an additional horizontal reaction wheel for direction control. The dynamics of the inverted pendulum system is modeled using the Euler–Lagrange equation, and the model is linearized for controller design. To verify the control performance of the system, a linear quadratic regulator controller is designed for the balance control and a proportional and derivative controller for the direction control. The performance of the proposed control system is verified through simulations. Based on the simulation results, an actual inverted pendulum is constructed, and the control for the system is experimentally validated.

Ultra-wideband (UWB) positioning systems have been extensively studied and implemented in dense urban and indoor environments. Assuming that the positions of UWB anchors are given, the position of a UWB tag on a moving platform can be estimated by measuring its distances to the anchors. However, precisely determining anchor positions in advance is labor-intensive and may be impractical in emergency situations, such as firefighting or search and rescue missions. To address this challenge, this paper presents a method for simultaneously localizing both the tag and fixed anchors in real-time without any prior knowledge of the anchor positions. Multiple UWB tags (MUT) are placed on an autonomous mobile robot (AMR), which allows the anchor initialization at the first observation without delay. In addition, a novel protocol for the MUT measurement is proposed to minimize measurement time. The geometric structure of the MUT placement is incorporated as a constraint in the estimation algorithm. In this paper, a tightly coupled approach is proposed for the estimation, which enhances the convergence and accuracy of localization. The proposed method is rigorously evaluated through simulations and real-world experiments, with results demonstrating its superior performance in the UWB localization, confirming its effectiveness in practical applications. Specifically, the proposed method achieves root mean square errors of 0.076 m in AMR's position, 0.017 rad in AMR's heading angle, and 0.131 m in anchor position, representing reductions of 24.05%, 45.4%, and 56.36%, respectively, compared with a state-of-the-art algorithm.

Brushless direct current (BLDC) motors are widely used in many industrial applications because they have several advantages compared to conventional DC motors, including high speed, high torque, and low maintenance. General BLDC motors have built-in hall sensors to detect the rotor position. However, because the output signal from the hall sensors is vulnerable to environmental noise, sensorless BLDC motors are adopted in critical environments. To control the sensorless BLDC motor, it is necessary to estimate the rotor position based on back-electro magnetic force (EMF) signal measurements. Sensorless BLDC motors have the benefits of low cost and compact size and are often used in gimbal systems, which is a type of motion stabilizer that has two or more axes of rotation and requires nonlinear dynamic control. In this paper, a nonlinear dynamic control simulation is implemented using MATLAB/Simulink for a two-axis gimbal with a sensorless BLDC motor.

This article presents a novel solution for the range-only simultaneous localization and mapping (RO-SLAM) that builds a map of the ultrawideband (UWB) anchors affixed to the unknown positions of the environment and localizes an autonomous mobile robot (AMR) at the same time. Delays in the initial positioning of the anchors are common in the RO-SLAM. To solve the delay problem, three UWB sensors named as the trilateration UWB tag (TUT) system on AMR are proposed. The TUT system makes it possible to estimate the range and bearing of each anchor without any delay at the first observation of three distance measurements from the UWB tags. However, the concentration of three UWB tags at a local point on AMR may degrade the performance of the range and bearing estimations. In order to improve the accuracy of the estimation, a constrained least-squares (CLS) algorithm is proposed in this article. The range and bearing estimations serve as the observational measurements for the proposed SLAM algorithm in this article, enabling accurate localization with computational efficiency. The performance of the proposed method is analyzed and demonstrated through simulations and experiments.

Accuracy of an indoor positioning system (IPS) under non-line-of-sight (NLOS) conditions is an open research topic. Performance of the IPS is significantly degraded due to a positive bias in range measurements caused by the NLOS condition between tags and anchors. This study proposes an anchor selection algorithm based on residual error, referred to as residual-based anchor selection (RAS), to mitigate the NLOS effect and improve the positioning accuracy of IPS. The proposed RAS algorithm does not require any prior information about the environment and utilizes only the measured distances between tags and anchors at each time instance to eliminate NLOS measurements, so its positioning performance is always guaranteed in both static and dynamic obstacles in environments. Ultrawideband (UWB) sensor is employed to validate the proposed algorithm because of its advantages for an IPS such as high accuracy ranging, mitigation of multipath effects, and extended measurement range. The effectiveness of the proposed method is addressed through simulation and experiment.

Autonomous mobile robots (AMRs) play a crucial role in smart factories and Internet of Things (IoT) applications. This paper presents a high-accuracy localization system called the global UWB system (GUS) for the AMR based on ultra-wideband (UWB) distance measurements. By using two UWB modules (tags) on AMR, it is possible to estimate both position and heading angle simultaneously, which can solve the problem of error accumulation over time in internal sensors. A new tightly coupled algorithm called baseline constraint extended Kalman filter (BC-EKF) is proposed. The baseline is the actual distance between the two tags that is used as a constraint for the measurement process. The relationship between the baseline and the heading angle error is also discussed to ensure reasonable deployment of the tags. The proposed localization method is persistent as value of the geometric dilution of precision (GDOP) increases. This helps to secure the performance of the localization method even in tight workspaces where the number of fixed UWBs (anchors) is limited. The simulation and experiment results demonstrate the localization performance of the proposed method.

The indoor positioning system (IPS) plays a crucial role in many applications of the Internet of Things (IoT). The trilateration is the most common and widely used method for the IPS because of its computational efficiency and ease of implementation. However, the accuracy of trilateration is affected by the geometric dilution of precision (GDOP). In this article, we propose a constrained least-squares trilateration (CLST) for the IPS that achieves high positioning performance with efficient computations even under severe GDOP conditions. The ultrawideband sensors are used to test the proposed positioning algorithm in this study. The positioning performance of the proposed method is demonstrated by simulations and experiments.