Precise underwater positioning remains a fundamental challenge for underwater robotics since global navigation satellite system (GNSS) signals cannot penetrate the sea surface. This paper presents Raspi$^2$USBL, an open-source, Raspberry Pi-based passive inverted ultra-short baseline (piUSBL) positioning system designed to provide a low-cost and accessible solution for underwater robotic research. The system comprises a passive acoustic receiver and an active beacon. The receiver adopts a modular hardware architecture that integrates a hydrophone array, a multichannel preamplifier, an oven-controlled crystal oscillator (OCXO), a Raspberry Pi 5, and an MCC-series data acquisition (DAQ) board. Apart from the Pi 5, OCXO, and MCC board, the beacon comprises an impedance-matching network, a power amplifier, and a transmitting transducer. An open-source C++ software framework provides high-precision clock synchronization and triggering for one-way travel-time (OWTT) messaging, while performing real-time signal processing, including matched filtering, array beamforming, and adaptive gain control, to estimate the time of flight (TOF) and direction of arrival (DOA) of received signals. The Raspi$^2$USBL system was experimentally validated in an anechoic tank, freshwater lake, and open-sea trials. Results demonstrate a slant-range accuracy better than 0.1%, a bearing accuracy within 0.1$^\circ$, and stable performance over operational distances up to 1.3 km. These findings confirm that low-cost, reproducible hardware can deliver research-grade underwater positioning accuracy. By releasing both the hardware and software as open-source, Raspi$^2$USBL provides a unified reference platform that lowers the entry barrier for underwater robotics laboratories, fosters reproducibility, and promotes collaborative innovation in underwater acoustic navigation and swarm robotics.

Muhammad Hafil Nugraha Research Centre for Smart Mechatronics National Research and Innovation Agency Bandung, Indonesia muha167@brin.go.id Estiko Rijanto Research Centre for Smart Mechatronics National Research and Innovation Agency Bandung, Indonesia estiko.rijanto@brin.go.id Oka Mahendra Research Centre for Smart Mechatronics National Research and Innovation Agency Bandung, Indonesia oka.mahendra@brin.go.id Abstract -- Accurate localization is crucial for effectively operating mobile robots in indoor environments. This paper presents a comprehensive approach to mobile robot localization by integrating an ultrasound - based indoor positioning system (IPS) with wheel odometry data via sensor fusion techniques. The Extended Kalman Filter (EKF) fusion method combines the data from the IPS sensors and the robot's wheel odometry, providing a robust and relia ble localization solution. Extensive experiments in a controlled indoor environment reveal that the fusion - based localization system significantly enhances accuracy and precision compared to standalone systems.

With the advancement of Internet of Things (IoT) technologies, high-precision indoor positioning has become essential for Location-Based Services (LBS) in complex indoor environments. Fingerprint-based localization is popular, but traditional algorithms and deep learning-based methods face challenges such as poor generalization, overfitting, and lack of interpretability. This paper applies conformal prediction (CP) to deep learning-based indoor positioning. CP transforms the uncertainty of the model into a non-conformity score, constructs prediction sets to ensure correctness coverage, and provides statistical guarantees. We also introduce conformal risk control for path navigation tasks to manage the false discovery rate (FDR) and the false negative rate (FNR).The model achieved an accuracy of approximately 100% on the training dataset and 85% on the testing dataset, effectively demonstrating its performance and generalization capability. Furthermore, we also develop a conformal p-value framework to control the proportion of position-error points. Experiments on the UJIIndoLoc dataset using lightweight models such as MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet show that the conformal prediction technique can effectively approximate the target coverage, and different models have different performance in terms of prediction set size and uncertainty quantification.

This paper presents an autonomous parking control system for an active-joint center-articulated mobile robot. We begin by proposing a kinematic model of the robot, then derive a control law designed to stabilize the vehicle's configuration within a small neighborhood of the target position. The control law is developed using Lyapunov techniques and is based on the robot's equations of motion in polar coordinates. Additionally, a beacon-based guidance system provides real-time feedback on the target's position and orientation. Simulation results demonstrate the robot's capability to start from arbitrary initial positions and orientations and successfully achieve parking.

Connected and Automated Vehicle (CAV) research has gained traction in the last decade due to significant advancements in perception, navigation, communication, and control functions. Accurate and reliable position information is needed to meet the requirements of CAV applications, especially when safety is concerned. With the advent of various perception sensors (e.g. camera, LiDAR, etc.), the vehicular positioning system has improved both in accuracy and robustness. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based cooperative positioning can improve the accuracy of the position estimates, but the integrity risks involved in multi-sensor fusion in a cooperative environment have not yet been fully explored. This paper reviews existing research in the field of positioning Integrity Monitoring (IM) and identifies various research gaps. Particular attention has been placed on identifying research that highlights cooperative IM methods. This analysis helps pave the way for the development of new IM frameworks for cooperative positioning solutions in the future.

Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based positioning is becoming a key technology to overcome the limitations of traditional methods. This paper begins with an introduction to the fundamentals of AI and wireless positioning, covering AI models, algorithms, positioning applications, emerging wireless technologies, and the basics of positioning techniques. Subsequently, focusing on standardization progress, we provide a comprehensive review of the evolution of 3GPP positioning standards, with an emphasis on the integration of AI/ML technologies in recent and upcoming releases. Based on the AI/ML-assisted positioning and direct AI/ML positioning schemes outlined in the standards, we conduct an in-depth investigation of related research. we focus on state-of-the-art (SOTA) research in AI-based line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle estimation techniques. For Direct AI/ML Positioning, we explore SOTA advancements in fingerprint-based positioning, knowledge-assisted AI positioning, and channel charting-based positioning. Furthermore, we introduce publicly available datasets for wireless positioning and conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.

Abstract--This paper describes the development of a costeffective yet precise indoor robot navigation system composed of a custom robot controller board and an indoor positioning system. First, the proposed robot controller board has been specially designed for emerging IoT-based robot applications and is capable of driving two 6-Amp motor channels. Then, working together with the robot controller board, the proposed positioning system detects the robot's location using a down-looking webcam and uses the robot's position on the webcam images to estimate the real-world position of the robot in the environment. The positioning system can then send commands via WIFI to the robot in order to steer it to any arbitrary location in the environment. Our experiments show that the proposed system reaches a navigation error smaller or equal to 0.125 meters while being more than two orders of magnitude more cost-effective compared to off-the-shelve motion capture (MOCAP) positioning systems.

One of the motivations for the development of wirelessly guided untethered magnetic devices (UMDs), such as microrobots and nanorobots, is the continuous demand to manipulate, sort, and assemble micro-objects with high level of accuracy and dexterity. UMDs can function as microgrippers or manipulators and move micro-objects with or without direct contact. In this case, the UMDs can be directly teleoperated by an operator using haptic tele-manipulation systems. The aim of this chapter is threefold: first, to provide a mathematical framework to design a scaled bilateral tele-manipulation system to achieve wireless actuation of micro-objects using magnetically-guided UMDs; second, to demonstrate closed-loop stability based on absolute stability theory; third, to provide experimental case studies performed on haptic devices to manipulate microrobots and assemble micro-objects. In this chapter, we are concerned with some fundamental concepts of electromagnetics and low-Reynolds number hydrodynamics to understand the stability and performance of haptic devices in micro- and nano-manipulation applications.

We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions.

Visible Light Positioning (VLP) has emerged as a promising technology capable of delivering indoor localization with high accuracy. In VLP systems that use Photodiodes (PDs) as light receivers, the Received Signal Strength (RSS) is affected by the incidence angle of light, making the inclination of PDs a critical parameter in the positioning model. Currently, most studies assume the inclination to be constant, limiting the applications and positioning accuracy. Additionally, light blockages may severely interfere with the RSS measurements but the literature has not explored blockage detection in real-world experiments. To address these problems, we propose a tightly coupled VLP/INS (Inertial Navigation System) integrated navigation system that uses graph optimization to account for varying PD inclinations and VLP blockages. We also discussed the possibility of simultaneously estimating the robot's pose and the locations of some unknown LEDs. Simulations and two groups of real-world experiments demonstrate the efficiency of our approach, achieving an average positioning accuracy of 10 cm during movement and inclination accuracy within 1 degree despite inclination changes and blockages.