Statically Fused Converted Measurement Kalman Filters

This chapter presents a state estimation method without using of nonlinear recursive filters when Doppler measurement is incorporated into the tracking system. The commonly used motions, such as the constant velocity (CV), constant acceleration (CA), and constant turn (CT), are represented in a pseudo-state space, defined from the product of target true range and range rate, by linear pseudo-state equations. Then the linear converted Doppler measurement Kalman filter (CDMKF) is presented to extract pseudo-state from the converted Doppler measurement, constructed by the product of the range and Doppler measurements. The output of the CDMKF is fused statically with that of the converted position measurement (range and one or two angles) Kalman filter (CPMKF) to produce target Cartesian state estimates. The accuracy and consistence of the estimator can be both guaranteed, since linear Kalman filters are both used to extract information from position and Doppler measurements

Low cost underwater acoustic localization

Over the course of the last decade, the cost of marine robotic platforms has significantly decreased. In part this has lowered the barriers to entry of exploring and monitoring larger areas of the earth's oceans. However, these advances have been mostly focused on autonomous surface vehicles (ASVs) or shallow water autonomous underwater vehicles (AUVs). One of the main drivers for high cost in the deep water domain is the challenge of localizing such vehicles using acoustics. A low cost one-way travel time underwater ranging system is proposed to assist in localizing deep water submersibles. The system consists of location aware anchor buoys at the surface and underwater nodes. This paper presents a comparison of methods together with details on the physical implementation to allow its integration into a deep sea micro AUV currently in development. Additional simulation results show error reductions by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ

An Acoustic Network Navigation System

This work describes a system for acoustic‐based navigation that relies on the addition of localization services to underwater networks. The localization capability has been added on top of an existing network, without imposing constraints on its structure/operation. The approach is based on the inclusion of timing information within acoustic messages through which it is possible to know the time of an acoustic transmission in relation to its reception. Exploiting such information at the network application level makes it possible to create an interrogation scheme similar to that of a long baseline. The advantage is that the nodes/autonomous underwater vehicles (AUVs) themselves become the transponders of a network baseline, and hence there is no need for dedicated instrumentation. The paper reports at sea results obtained from the COLLAB–NGAS14 experimental campaign. During the sea trial, the approach was implemented within an operational network in different configurations to support the navigation of the two Centre for Maritime Research and Experimentation Ocean Explorer (CMRE OEX) vehicles. The obtained results demonstrate that it is possible to support AUV navigation without constraining the network design and with a minimum communication overhead. Alternative solutions (e.g., synchronized clocks or two‐way‐travel‐time interrogations) might provide higher precision or accuracy, but they come at the cost of impacting on the network design and/or on the interrogation strategies. Results are discussed, and the performance achieved at sea demonstrates the viability to use the system in real, large‐scale operations involving multiple AUVs. These results represent a step toward location‐aware underwater networks that are able to provide node localization as a service

Clustering for filtering: multi-object detection and estimation using multiple/massive sensors

Advanced multi-sensor systems are expected to combat the challenges that arise in object recognition and state estimation in harsh environments with poor or even no prior information, while bringing new challenges mainly related to data fusion and computational burden. Unlike the prevailing Markov-Bayes framework that is the basis of a large variety of stochastic filters and the approximate, we propose a clustering-based methodology for multi-sensor multi-object detection and estimation (MODE), named clustering for filtering (C4F), which abandons unrealistic assumptions with respect to the objects, background and sensors. Rather, based on cluster analysis of the input multi-sensor data, the C4F approach needs no prior knowledge about the latent objects (whether quantity or dynamics), can handle time-varying uncertainties regarding the background and sensors such as noises, clutter and misdetection, and does so computationally fast. This offers an inherently robust and computationally efficient alternative to conventional Markov–Bayes filters for dealing with the scenario with little prior knowledge but rich observation data. Simulations based on representative scenarios of both complete and little prior information have demonstrated the superiority of our C4F approach

Quantitative Performance Assessment of LiDAR-based Vehicle Contour Estimation Algorithms for Integrated Vehicle Safety Applications

Many nations and organizations are committing to achieving the goal of `Vision Zero\u27 and eliminate road traffic related deaths around the world. Industry continues to develop integrated safety systems to make vehicles safer, smarter and more capable in safety critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for shape estimation of crash partners lack the fidelity required for edge case collision detection and advanced crash modeling. This research presents a novel algorithm for robust and accurate contour estimation of opposing vehicles. The presented method is evaluated via a developed framework for key performance metrics and compared to alternative algorithms found in literature

Fusion of low-cost and light-weight sensor system for mobile flexible manipulator

There is a need for non-industrial robots such as in homecare and eldercare. Light-weight mobile robots preferred as compared to conventional fixed based robots as the former is safe, portable, convenient and economical to implement. Sensor system for light-weight mobile flexible manipulator is studied in this research. A mobile flexible link manipulator (MFLM) contributes to high amount of vibrations at the tip, giving rise to inaccurate position estimations. In a control system, there inevitably exists a lag between the sensor feedback and the controller. Consequently, it contributed to instable control of the MFLM. Hence, there it is a need to predict the tip trajectory of the MFLM. Fusion of low cost sensors is studied to enhance prediction accuracy at the MFLM’s tip. A digital camera and an accelerometer are used predict tip of the MFLM. The main disadvantage of camera is the delayed feedback due to the slow data rate and long processing time, while accelerometer composes cumulative errors. Wheel encoder and webcam are used for position estimation of the mobile platform. The strengths and limitations of each sensor were compared. To solve the above problem, model based predictive sensor systems have been investigated for used on the mobile flexible link manipulator using the selected sensors. Mathematical models were being developed for modeling the reaction of the mobile platform and flexible manipulator when subjected to a series of input voltages and loads. The model-based Kalman filter fusion prediction algorithm was developed, which gave reasonability good predictions of the vibrations of the tip of flexible manipulator on the mobile platform. To facilitate evaluation of the novel predictive system, a mobile platform was fabricated, where the flexible manipulator and the sensors are mounted onto the platform. Straight path motions were performed for the experimental tests. The results showed that predictive algorithm with modelled input to the Extended Kalman filter have best prediction to the tip vibration of the MFLM

Overcoming the challenges of low-cost inertial navigation

Inertial navigation is always available as a base for multisensor navigation systems on, because it requires no external signals. However, measurement errors persist and grow with time so accurate calibration is crucial. Large systematic errors are present in the micro-electro-mechanical sensors (MEMS) whose low cost brings inertial navigation to many new applications. Using factory-calibrated MEMS another navigation technology can calibrate these errors with in-run estimation using a Kalman filter (KF). However, the raw systematic errors of low-cost MEMS are often too large for stable performance. This thesis contributes to knowledge in three areas. First, it takes a simple GNSS-inertial KF and examines the levels of the various systematic errors which cause the integration to fail. This allows the user to know how well calibrated the sensors need to be to use in-run calibration. Second, the thesis examines how the end-user could conduct a calibration: it analyses one method in detail showing how imperfections in the procedure affect the results and comparing calculation methods. This is important as frequently calibration methods are only validated by demonstrating consistent results for one particular sensor. These two are primarily accomplished using statistical Monte Carlo simulations. Third, techniques are examined by which an array of inertial sensors could be used to produce an output which is better than the simple array average. This includes methods that reduce the array’s sensitivity to environmental conditions, this is important because the sensors’ calibration typically depends strongly on temperature. Also included in the thesis are descriptions of experimental hardware and experiments which have been carried to support and unify the other parts of the thesis. Overall, this thesis’ contributions will help make low-cost inertial navigation systems more accurate and will allow system designers to concentrate effort where it will make the most difference

Practical investigations in robot localization using ultra-wideband sensors

Robot navigation is rudimentary compared to the capabilities of humans and animals to move about their environments. One of the core processes of navigation is localization, the problem of answering where one is at the present time. Robot localization is the science of using various sensors to inform a robot of where it is within its environment. Ultra-wideband (UWB) radio is one such sensor technology that can return absolute position information. The algorithm to accomplish this is known as multilateration, which uses a collection of distance measurements between multiple robot tag and environment anchor pairs to calculate the tag’s position. UWB is especially suited to the task of returning precise distance measurements due to its capabilities of short duration, high amplitude pulse generation and detection. Decawave Ltd. has created an UWB integrated circuit to perform ranging and a suite of products to support this technology. Claimed and verified accuracies using this implementation are on the order of 10cm. This thesis describes various experiments carried out using Decawave technology for robot localization. The progression of the chapters starts with commercial product verification before moving into development and testing in various environments of an open-source driver package for the Robot Operating System (ROS), then the development of a novel phase difference of arrival (PDoA) sensor for three-dimensional robot localization without an UWB anchor mesh, before concluding with future research directions and commercialization potential of UWB. This thesis is designed as a compilation of all that the author has learned through primary and secondary research over the past three years of investigation. The primary contributions are: 1. A modular ROS UWB driver framework and series of ROS bags for offline experimentation with multilateration algorithms. 2. A robust ROS framework for comparing motion capture system (MoCap) ground truth vs sensor data for rigorous statistical analysis and characterization of multiple sensors. 3. Development of a novel UWB PDoA sensor array and data model to allow 3D localization of a target from a single point without the deployment of an antenna mesh