Onboard Inertial Sensors Research Articles

RaDro: Indoor Drone Tracking Using Millimeter Wave Radar

Core to drone design is its ability to ascertain its location by utilizing onboard inertial sensors combined with GPS data. However, GPS is not always reachable, especially in challenging environments such as indoors. This paper proposes RaDro; a system that leverages millimeter-waves (mmWave) to precisely localize and track drones in indoor environments. Unlike commonly used alternative technologies, RaDro is cost-effective and can penetrate obstacles, a bonus in non-line-of-sight (NLoS) scenarios, which enhances its reliability for tracking objects in complex environments. It does this without the need for tags or anchors to be attached to the drone, achieving 3D tracking with just a single radar point, significantly streamlining the deployment process. Comprehensive experiments are conducted in different scenarios to evaluate RaDro's performance. These include employing different drone models with different sizes to execute a range of aerial manoeuvres across different flight arenas, each with its own settings and clutter, and encountering various LoS and NLoS scenarios in dynamic environments. The experiments aimed to assess the capabilities of the system to extract coarse-grained and fine-grained information for drone detection, motion recognition, and localization. The results showcase precise localization, achieving a 50% reduction in localization error compared to the conventional baseline. This localization accuracy remains resilient even when confronted with interference from other moving sources. The results also demonstrate the system's ability to accurately localize drones in NLoS scenarios where existing state-of-the-art optical technologies cannot work.

Multi-sensor Attitude Estimation using Quaternion Constrained GNSS Ambiguity Resolution and Dynamics-Based Observation Synchronization

Recently, high accuracy and low-cost navigation hardware is becoming increasingly available that can be efficiently used for the control of autonomous vehicles. We present a sensor fusion method providing tightly coupled integration of pseudorange, carrier phase, and Doppler satellite measurements taken at multiple vehicle-mounted GNSS antennas with onboard inertial sensor observations. The key of accurate GNSS position and orientation estimation is the successful integer ambiguity resolution. We propose a method that uses the quaternion states as constraints to improve ambiguity resolution and to increase the accuracy of the GNSS based attitude determination. Generally, the low-cost hardware neither allows a hardware-level time synchronization between the GNSS receivers due to a lack of a common external oscillator nor provides the clock steering function available in geodetic GNSS receivers. The lack of observation synchronization causes several degrees of error in attitude estimation. To eliminate this effect, a dynamics-based solution is presented that synchronizes the observations by taking the dynamics of the moving platform into account. Compared to common external oscillator based sensor setups, our solution allows to increase both the number of rover receivers on the platform and the baselines between them easily, thus it opens up new possibilities in the attitude determination of large vehicles. We validate our approach against a tactical grade inertial navigation system. The results show that our approach using low-cost sensors provides the ambiguity success rate of 100% for the moving baselines, and the positioning and attitude error reached the centimeter and half a degree level, respectively.

Autonomous control of a prototype solar‐powered offshore autonomous underwater vehicle servicing platform via a low‐cost embedded architecture

AbstractThis paper describes the design, implementation, and field‐testing of a low‐cost autonomous control system on the Platform for Expanding Autonomous Underwater Vehicles (AUV) exploRation to Longer ranges (PEARL). PEARL is a floating, solar‐powered platform designed as a servicing station for AUVs. This paper describes the field testing of a prototype system currently without the ability to dock and recharge AUVs. In this prototype, autonomous operations are realized through the open‐source MOOS‐IvP marine robotics software running on an embedded architecture including a Raspberry Pi microcomputer, Arduino microcontroller, and onboard inertial measurement unit and GPS sensors. A solar charge controller monitors energy system statistics in real‐time. Field tests included station‐keeping and waypoint missions in a freshwater pond in Orleans, MA. An RMS tracking error of 0.9–2.2 m was achieved during waypoint pattern executions, with an average battery usage of 13% per hour. Net power consumption rate will change due to variations in currents and winds, as well as variations in solar irradiance. The implementation of an autonomous sun‐tracking mode to maximize solar panel flux while station‐keeping is also evaluated. The field testing results will inform the design of a full‐scale PEARL capable of docking with AUVs. This research helps pave the way for autonomous solar‐powered service stations in the ocean that can connect the underwater world with space.

Seasonal Accumulated Workloads in Collegiate Women's Soccer: A Comparison of Starters and Reserves.

Research quantifying the unique workload demands of starters and reserves in training and match settings throughout a season in collegiate soccer is limited. Purpose: The purpose of the current study is to compare accumulated workloads between starters and reserves in collegiate soccer. Methods: Twenty-two NCAA Division III female soccer athletes (height: 1.67 ± 0.05 m; body mass: 65.42 ± 6.33 kg; fat-free mass: 48.99 ± 3.81 kg; body fat %: 25.22 ± 4.78%) were equipped with wearable global positioning systems with on-board inertial sensors, which assessed a proprietary training load metric and distance covered for each practice and 22 matches throughout an entire season. Nine players were classified as starters (S), defined as those playing >50% of playing time throughout the entire season. The remaining 17 were reserves (R). Goalkeepers were excluded. A one-way ANOVA was used to determine the extent of differences in accumulated training load throughout the season by player status. Results: Accumulated training load and total distance covered for starters were greater than reserves ((S: 9431 ± 1471 vs. R: 6310 ± 2263 AU; p < 0.001) and (S: 401.7 ± 31.9 vs. R: 272.9 ± 51.4 km; p < 0.001), respectively) throughout the season. Conclusions: Starters covered a much greater distance throughout the season, resulting in almost double the training load compared to reserves. It is unknown if the high workloads experienced by starters or the low workloads of the reserves is more problematic. Managing player workloads in soccer may require attention to address potential imbalances that emerge between starters and reserves throughout a season.

Cooperative Transport of a Suspended Payload via Two Aerial Robots With Inertial Sensing

In this article, we address the problem of aerial transport of a suspended payload by two vehicles with minimal sensing and computational requirements. Herein, the leader-follower paradigm is adopted, allowing the follower robot to estimate and stabilize the formation using only feedback from its onboard inertial sensor. This is achieved through an analysis of the underlying dynamics of the coupled system. The gained insights lead to the introduction of a two-stage Kalman-based estimation strategy and the linear control laws that are capable of robustly dealing with the underactuated leader-payload-follower system. The vision-less methods have been verified through a series of flight experiments. The efficiency and the robustness of the schemes enable the cooperative payload transport to be accomplished by two sub-100-g robots with highly limited computational power in both indoor and outdoor environments.

Railway track location estimation using onboard inertial sensors

In railways, using a track- and ride-quality monitoring system on in-service train has become desirable for coordination and security. Identification of the track- or train-related rough rides via train crew can be estimated to the nearest kilometre. However, if the train is equipped with a monitoring system a better location and track quality evaluation can be provided. These systems commonly use information such as GNSS and/or an odometer to provide location information. This work proposes a practical method for track alignment estimation using real data from an in-cab inertial measurement system and using also a novel method based on crosslevel variations. The speed estimation is done through speed-related harmonics detected on inertial sensors, which depend on speed and track characteristics; and distance correction is provided by comparing crosslevel derived from inertial sensors and a reference track geometry. The effectiveness and accuracy of the method is demonstrated with data collected between London and Ashford.

Identification of vehicle directional parameters using the sigma-point Kalman filters

The article discusses the problem of identifying the parameters of the vehicle's directional movement, which are necessary for the operation of active safety systems (SAB). The inability to determine some of the parameters necessary for the functioning of the SAB by direct measurements with on-board sensors (due to the absence of corresponding sensors in production vehicles) makes it relevant to use indirect computational methods for identifying these parameters, which are based on mathematical structures called observers.&#x0D; The purpose of this work is to create a system for identifying vehicle motion parameters, which, using the measurements available on board the vehicle and the mathematical apparatus of the theory of observers and optimal filters, indirectly determines unmeasured parameters that are important for the operation of active safety systems.&#x0D; Based on the analysis of existing methods and tools, a diagram of the observer of the parameters of the vehicle's directional movement using the sigma-point Kalman filter is proposed. The observer identifies the lateral component of the vehicle speed vector, the coefficients of the lateral adhesion of the tires to the supporting surface and the wheel slip angles using the vehicle dynamics model and on-board inertial sensors that measure the linear acceleration and yaw rate of the vehicle.&#x0D; The observer's performance and adequacy was confirmed by comparing the parameters he identifies with direct measurements made during road tests of the vehicle. There was used a root-mean-square error of identification as a measure for assessing the accuracy with respect to direct measurements of the parameters of course movement. An additional assessment of the adequacy is made by comparing the identified grip characteristic (the dependence of the coefficient of adhesion on the slip angle) with the characteristic obtained by approximation using a mathematical model of the tire. The assessment showed a good quality of identification of course movement parameters provided by the developed observer, which gives grounds to consider it a useful tool for research and development of active safety systems.

Geometric and constrained control for a string of tethered drones

In this study, we present a novel concept of a multi tethered drone system. The system includes an arbitrary number of drones connected serially to an active ground station. The considered drones are of quadrotor type. Utilizing a unique pulley–gimbal mechanism, each drone can freely move along the tether, and its state is measured with respect to the ground station without the use of standard onboard inertial sensors or GPS. The proposed system can be thought of as a robotic arm where each tether section acts as a variable-length link and each drone is a joint actuator. We model the coupled behavior of the ground station and the string, taking into account an arbitrary number of drones. Then, a controller that combines tools from geometric-control and Model Predictive Control is suggested. The developed model and control approach are also applicable for other swarm applications where the position of agents is to be controlled to a string-like form. Finally, the concept is demonstrated using numerical simulations and an initial experiment, which illustrate its potential effectiveness.

Entropy analysis of data collected from inertial measurement unit of cyber-physical system under non-disturbed conditions

Nowadays cyber-physical systems are widely used for many purposes. We consider the provision of information security of data channels in such systems. Cryptographic data security approach based on random sequences is commonly used to solve this task. Its reliability depends on quality of random data being used, thus truly random sequences are preferable for application. Truly random data generation is a time-consuming process and it requires entropy sources of physical nature. The goal of the paper presented is to research methods and approaches of collecting random numbers using inertial measurement unit as a part of cyber-physical system. Method. Quality assessment of a binary sequence was carried out during the research by determination of random sequence statistical characteristics.Main Results. Research results have shown up that raw data collected from onboard inertial sensors possess lack of entropy under non-disturbed conditions, therefore an additional post-processing is required. Practical Relevance. The results of the research can be used to obtain random sequences for on board cyber-physical systems equipped with inertial measurement units without the use of additional devices. It is planned to collect data from a flying unmanned aerial system in future to apply extractors and to utilize other methods in order to improve quality of a binary sequence.

Prediction of Railway Vehicles’ Dynamic Behavior with Machine Learning Algorithms

The dynamic performance of railway vehicles needs to be carefully monitored to ensure their safe operation. Presently a number of systems such as the Vehicle Track Interaction Monitor and the Instrumented Revenue Vehicles, utilize a number of on-board inertial sensors to obtain near-real time information on the dynamic performance of railway vehicles. These systems provide rich data sets that give an indication of the underlying track condition and the corresponding dynamic response. This paper outlines the use of Machine learning to develop dynamic behavior predictive models for railway vehicles from measured data. This study worked on the development of 2 types of predictive models, viz. regression and classification model. The regression model predicted the time series dynamic response amplitude and the classification model classified the track sections based on the response distribution over it. Train speed and parameters estimated from the unsprung mass were used as predictors in the model. After the trial of a number of predictive models the Ensemble Tree Bagger method was found to have highest overall prediction accuracy. These predictive models can be utilized as a decision making tool to determine safe operational limits and prioritize maintenance interventions.

Flexible decoupled camera and projector fringe projection system using inertial sensors

Measurement of objects with complex geometry and many self occlusions is increasingly important in many fields, including additive manufacturing. In a fringe projection system, the camera and the projector cannot move independently with respect to each other, which limits the ability of the system to overcome object self-occlusions. We demonstrate a fringe projection setup where the camera can move independently with respect to the projector, thus minimizing the effects of self-occlusion. The angular motion of the camera is tracked and recalibrated using an on-board inertial angular sensor, which can additionally perform automated point cloud registration.

Robust L∞ convex optimisation for UAVs cooperative motion estimation

In this paper, a system for real-time cooperative monocular visual motion estimation with multiple unmanned aerial vehicles is proposed. Distributing the system across a network of vehicles allows for efficient processing in terms of both computational time and estimation accuracy. The resulting global cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms are developed within a convex optimisation framework on-board the monocular vision systems of each vehicle. In the presented novel distributed cooperative strategy a visual loop-closure module is deployed to detect any simultaneously overlapping fields of view of two or more of the vehicles. A positive feedback from the latter module triggers the collaborative motion estimation algorithm between any vehicles involved in this loop-closure. This scenario creates a flexible stereo set-up which jointly optimises the motion estimates of all vehicles in the cooperative scheme. Prior to that, vehicle-to-vehicle relative pose estimates are recovered with a novel robust registration solution in a global optimisation framework. Furthermore, as a complementary solution, a robust non-linear H∞filter is designed to fuse measurements from the vehicles’ on-board inertial sensors with the visual estimates. The proposed cooperative navigation solution has been validated on real-world data, using two unmanned aerial vehicles equipped with monocular vision systems.

Accurate calibration of systematic errors for car-like mobile robots using experimental orientation errors

One of the essential technologies for autonomous navigation is localization. Localization is important because accurate pose estimation is required for path planning and motion control. In order to improve localization accuracy, a relative positioning method on the basis of accurate odometry is necessary. Odometry calibration methods for two wheel differential drive robots have been researched for many years. However, it is difficult to find odometry calibration methods for car-like mobile robots. In this paper, an accurate calibration method for car-like mobile robots is proposed. Experimentally measured orientation errors were used to improve the accuracy of the calibration method. There are two contributions in this paper. The first is the significant reduction of calibration errors by the use of accurate calibration equation. In the previous research, calibration equation required approximations. However, there is no approximation error in the proposed equation owing to the use of orientation errors, not positional errors. The second is the experimental convenience. The orientations can be easily measured by onboard inertial sensors. Therefore, calibration experiments can be easily carried out in both indoor and outdoor environments. The presented experimental results show that resultant performance of the proposed scheme is superior to the results of the previous research.

GNSS/Onboard Inertial Sensor Integration With the Aid of 3-D Building Map for Lane-Level Vehicle Self-Localization in Urban Canyon

Lane-level vehicle self-localization is a challenging and significant issue arising in autonomous driving and driver-assistance systems. The Global Navigation Satellite System (GNSS) and onboard inertial sensor integration are among the solutions for vehicle self-localization. However, as the main source in the integration, GNSS positioning performance is severely degraded in urban canyons because of the effects of multipath and non-line-of-sight (NLOS) propagations. These GNSS positioning errors also decrease the performance of the integration. To reduce the negative effects caused by GNSS positioning error, this paper proposes to employ an innovative GNSS positioning technique with the aid of a 3-D building map in the integration. The GNSS positioning result is used as an observation, and this is integrated with the information from the onboard inertial sensor and vehicle speedometer in a Kalman filter framework. To achieve stable performance, this paper proposes to evaluate and consider the accuracy of the employed GNSS positioning method in dynamic integration. A series of experiments in different scenarios is conducted in an urban canyon, which can demonstrate the effectiveness of the proposed method using various evaluation and comparison processes.

UAV energy extraction with incomplete atmospheric data using MPC

A nonlinear model predictive control strategy is presented for unmanned aerial vehicle (UAV) trajectory determination. The objective is to find optimal paths in the atmosphere by maximizing the UAV's energy (kinetic and potential) over a finite but receding horizon. The main assumption is that the updraft distribution is unknown, creating a realistic situation. The updrafts are only estimated online using standard on-board inertial sensors. Real-time implementation of the algorithm is shown to be possible in principle.

An iterative method for attitude determination based on misaligned GNSS baselines

Multiple Global Navigation Satellite System (GNSS) antennae systems have been among the well-known approaches to attitude determination of moving platforms in recent years. However, the constraints on the onboard GNSS antennae configuration, that is, installing the antennae baselines along the main axes of the platform, can lead to practical difficulties and inevitable uncertainty. In this contribution we present an iterative method to obtain the accurate attitudes by means of the onboard misaligned baselines. In order to remove the effects of the horizontal and vertical misalignments from GNSS observations of the baselines, attitude-induced corrections have been developed through the relative lever-arm coordinates of the onboard antennae. This approach provides us with more freedom from spatial distribution of the onboard antennae. The performance of the proposed method has been analyzed by simulated data and an actual experiment in low- and high-dynamic situations. The simulation has been designed to evaluate the capability and reliability of the iterative method under the presence of small and large misalignments. The field experiment was carried out in the offshore waters of Kish harbor using three dual-frequency GNSS receivers with choke-ring antennae onboard a survey vessel, which was also equipped with an inertial measurement unit (IMU). The maximum allowable misalignments, the convergence of the iteration, and the advantage of the proposed method over the trigonometric approach in spite of applying the traditional calibration are presented. The long-term stability of GNSS attitude determination as well as IMU accuracy degradation, due largely to the increase in the time-varying biases/noises, have demonstrated the potential of the method to estimate the accuracies and biases of the onboard inertial sensor. The overall results affirm that the presented method can effectively provide the platform attitudes using the misaligned GNSS baselines with as much flexibility as possible for the onboard antennae configuration.

Reliable orientation estimation for mobile motion capturing in medical rehabilitation sessions based on inertial measurement units

Fully mobile and wireless motion capturing is a mandatory requirement for undisturbed and non-reactive analysis of human movements. Inertial sensor platforms are used in applications like training session analysis in sports or rehabilitation, and allow non-restricted motion capturing. The computation of the required reliable orientation estimation based on the inertial sensor RAW data is a demanding computational task. Therefore, an analysis of the computational costs and achievable accuracy of a Kalman filter and a complementary filter algorithm is provided. Highly customized and thus low-power, wearable computation platforms require low-level, platform independent communication protocols and connectivity. State-of-the-art small sized commercial inertial sensors either lack the availability of an open, platform independent protocol, wireless connectivity or extension interfaces for additional sensors. Therefore, an extensible, wireless inertial sensor called Institute of Microelectronic Systems Inertial Measurement Unit (IM)2SU, featuring onboard inertial sensor fusion, for use in home based stroke rehabilitation is presented. Furthermore, a Quaternion based, singularity free orientation estimation accuracy error measure is proposed and applied. To evaluate orientation estimation accuracy an optical system is used as golden reference. Orientation estimation based on a Kalman filter and a complementary filter algorithm is evaluated. The proposed IMU provides high orientation estimation accuracy, is platform independent, offers wireless connection and extensibility and is low cost.

Quadrocopter Hovering Using Position-estimation Information from Inertial Sensors and a High-delay Video System

The requirement that mobile robots become independent of external sensors, such as GPS, and are able to navigate in an environment by themselves, means that designers have few alternative techniques available. An increasingly popular approach is to use computer vision as a source of information about the surroundings. This paper presents an implementation of computer vision to hold a quadrocopter aircraft in a stable hovering position using a low-cost, consumer-grade, video system. However, such a system is not able to stabilize the aircraft on its own and must rely on a data-fusion algorithm that uses additional measurements from on-board inertial sensors. Special techniques had to be implemented to compensate for the increased delay in the closed-loop system with the computer vision system, i.e., video timestamping to determine the exact delay of the vision system and a slight modification of the Kalman filter to account for this delay. At the end, the validation results of the proposed filtering technique are presented along with the results of an autonomous flight as a proof of the proposed concept.

Particle Filter SLAM with High Dimensional Vehicle Model

This work presents a particle filter method closely related to Fastslam for solving the simultaneous localization and mapping (slam) problem. Using the standard Fastslam algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work, an extra factorization of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental data from an unmanned aerial vehicle (helicopter) are presented. The proposed algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro), barometer, and vision in order to solve the slam problem.

Feedforward ins aiding: an investigation of maneuvers for in-flight alignment

Navigation in autonomous vehicles involves integrating measurements from on-board inertial sensors and external data collected by various sensors. In this paper, the computer-frame velocity error model is augmented with a random constant model of accelerometer bias and rate-gyro drift for use in a Kalman filter-based fusion of a low-cost rotating inertial navigation system (INS) with external position and velocity measurements. The impact of model mismatch and maneuvers on the estimation of misalignment and inertial measurement unit (IMU) error is investigated. Previously, the literature focused on analyzing the stripped observability matrix that results from applying piece-wise constant acceleration segments to a stabilized, gimbaled INS to determine the accuracy of misalignment, accelerometer bias, and rate-gyro drift estimation. However, its validation via covariance analysis neglected model mismatch. Here, a vertically undamped, three channel INS with a rotating IMU with respect to the host vehicle is simulated. Such IMU rotation does not require the accurate mechanism of a gimbaled INS (GINS) and obviates the need to maneuver away from the desired trajectory during in-flight alignment (IFA) with a strapdown IMU. In comparison with a stationary GINS at a known location, IMU rotation enhances estimation of accelerometer bias, and partially improves estimation of rate-gyro drift and misalignment. Finally, combining IMU rotation with distinct acceleration segments yields full observability, thus significantly enhancing estimation of rate-gyro drift and misalignment.