Inertial Navigation and Position Uncertainty during a Blind Safe Stop of an Autonomous Vehicle

This work considers the problem of position and position-uncertainty estimation for atonomous vehicles during power black-out, where it cannot be assumed that any position data is accessible. To tackle this problem, the position estimation will instead be performed using power separated and independent measurement devices, including one inertial 6 Degrees of Freedom (DOF) measurement unit, four angular wheel speed sensors and one pinion angle sensor. The measurement unit\u27s sensors are initially characterized in order to understand conceptual limitations of the inertial navigation and also to be used in a filtering process. Measurement models are then fused together with vehicle dynamics process models using the architecture of an Extended Kalman Filter (EKF). Two different EKF filter concepts are developed to estimate the vehicle position during a safe stop; one simpler filter for smooth manoeuvres and a complex filter for aggressive manoeuvres. Both filter designs are tested and evaluated with data gathered from an experimental vehicle for selected manoeuvres of developed safe-stop scenarios. The experimental results from a set of use-case manoeuvres show a trend where the size of the position estimation errors significantly grows above an initial vehicle speed of 70 km/h. This paper contributes to develop vehicle dynamics models for the purpose of a blind safe stop

Event based localization in Ackermann steering limited resource mobile robots

“© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”This paper presents a local sensor fusion technique with an event-based global position correction to improve the localization of a mobile robot with limited computational resources. The proposed algorithms use a modified Kalman filter and a new local dynamic model of an Ackermann steering mobile robot. It has a similar performance but faster execution when compared to more complex fusion schemes, allowing its implementation inside the robot. As a global sensor, an event-based position correction is implemented using the Kalman filter error covariance and the position measurement obtained from a zenithal camera. The solution is tested during a long walk with different trajectories using a LEGO Mindstorm NXT robot.This work was supported by FEDER-CICYT projects with references DPI2011-28507-C02-01 and DPI2010-20814-C02-02, financed by the Ministerio de Ciencia e Innovacion (Spain). This work was also supported by the University of Costa Rica.Marín, L.; Vallés Miquel, M.; Soriano Vigueras, Á.; Valera Fernández, Á.; Albertos Pérez, P. (2014). Event based localization in Ackermann steering limited resource mobile robots. IEEE/ASME Transactions on Mechatronics. 19(4):1171-1182. doi:10.1109/TMECH.2013.2277271S1171118219

Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position

Tyre curve estimation in slip-controlled braking

Progress in reducing actuator delays in pneumatic brake systems creates an opportunity for advanced anti-lock braking algorithms to be used on heavy goods vehicles. However, these algorithms require knowledge of variables that are impractical to measure directly. This paper introduces a braking force observer and road surface identification algorithms to support a sliding-mode slip controller for air-braked heavy vehicles. Both the force observer and the slip controller are shown to operate robustly under a variety of conditions in quarter-car simulations. A non-linear least-squares algorithm was found to be capable of performing regressions on all the parameters of the tyre model from the University of Michigan Transportation Research Institute when used ‘in the loop’ with the controller and the observer. A recursive least-squares algorithm that is less computationally expensive than the non-linear algorithm was also investigated but gave only reasonable estimates of the tyre model parameters on high-friction smooth roads. The authors would like to thank the members of the Cambridge Vehicle Dynamics Consortium (CVDC), and the Gates Cambridge Trust for their parts in funding this work.This is the author accepted manuscript. The final version is available from Sage via http://dx.doi.org/10.1177/095440701558593

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter

Correlated-Data Fusion and Cooperative Aiding in GNSS-Stressed or Denied Environments

University of Minnesota Ph.D. dissertation. September 2014. Major: Aerospace Engineering and Mechanics. Advisor: Demoz Gebre-Egziabher. 1 computer file (PDF); xiii, 172 pages.A growing number of applications require continuous and reliable estimates of position, velocity, and orientation. Price requirements alone disqualify most traditional navigation or tactical-grade sensors and thus navigation systems based on automotive or consumer-grade sensors aided by Global Navigation Satellite Systems (GNSS), like the Global Positioning System (GPS), have gained popularity. The heavy reliance on GPS in these navigation systems is a point of concern and has created interest in alternative or back-up navigation systems to enable robust navigation through GPS-denied or stressed environments. This work takes advantage of current trends for increased sensing capabilities coupled with multilayer connectivity to propose a cooperative navigation-based aiding system as a means to limit dead reckoning error growth in the absence of absolute measurements like GPS. Each vehicle carries a dead reckoning navigation system which is aided by relative measurements, like range, to neighboring vehicles together with information sharing. Detailed architectures and concepts of operation are described for three specific applications: commercial aviation, Unmanned Aerial Vehicles (UAVs), and automotive applications. Both centralized and decentralized implementations of cooperative navigation-based aiding systems are described. The centralized system is based on a single Extended Kalman Filter (EKF). A decentralized implementation suited for applications with very limited communication bandwidth is discussed in detail. The presence of unknown correlation between the a priori state and measurement errors makes the standard Kalman filter unsuitable. Two existing estimators for handling this unknown correlation are Covariance Intersection (CI) and Bounded Covariance Inflation (BCInf) filters. A CI-based decentralized estimator suitable for decentralized cooperative navigation implementation is proposed. A unified derivation is presented for the Kalman filter, CI filter, and BCInf filter measurement update equations. Furthermore, characteristics important to the proper implementation of CI and BCInf in practice are discussed. A new covariance normalization step is proposed as necessary to properly apply CI or BCInf. Lastly, both centralized and decentralized implementations of cooperative aiding are analyzed and evaluated using experimental data in the three applications. In the commercial aviation study aircraft are simulated to use their Automatic Dependent Surveillance - Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) systems to cooperatively aid their on board INS during a 60 min GPS outage in the national airspace. An availability study of cooperative navigation as proposed in this work around representative United States airports is performed. Availabilities between 70-100% were common at major airports like LGA and MSP in a 30 nmi radius around the airport during morning to evening hours. A GPS-denied navigation system for small UAVs based on cooperative information sharing is described. Experimentally collected flight data from 7 small UAV flights are played-back to evaluate the performance of the navigation system. The results show that the most effective of the architectures can lead to 5+ minutes of navigation without GPS maintaining position errors less than 200 m (1-σ). The automotive case study considers 15 minutes of automotive traffic (2,000 + vehicles) driving through a half-mile stretch of highway without access to GPS. Automotive radar coupled with Dedicated Short Range Communication (DSRC) protocol are used to implement cooperative aiding to a low-cost 2-D INS on board each vehicle. The centralized system achieves an order of magnitude reduction in uncertainty by aggressively aiding the INS on board each vehicle. The proposed CI-based decentralized estimator is demonstrated to be conservative and maintain consistency. A quantitative analysis of bandwidth requirements shows that the proposed decentralized estimator falls comfortably within modern connectivity capabilities. A naive implementation of the high-performance centralized estimator is also achievable, but it was demonstrated to be burdensome, nearing the bandwidth limits

Cascaded Filtering Using the Sigma Point Transformation (Extended Version)

It is often convenient to separate a state estimation task into smaller "local" tasks, where each local estimator estimates a subset of the overall system state. However, neglecting cross-covariance terms between state estimates can result in overconfident estimates, which can ultimately degrade the accuracy of the estimator. Common cascaded filtering techniques focus on the problem of modelling cross-covariances when the local estimators share a common state vector. This letter introduces a novel cascaded and decentralized filtering approach that approximates the cross-covariances when the local estimators consider distinct state vectors. The proposed estimator is validated in simulations and in experiments on a three-dimensional attitude and position estimation problem. The proposed approach is compared to a naive cascaded filtering approach that neglects cross-covariance terms, a sigma point-based Covariance Intersection filter, and a full-state filter. In both simulations and experiments, the proposed filter outperforms the naive and the Covariance Intersection filters, while performing comparatively to the full-state filter.Comment: This is an extended version of the original letter to be published in the IEEE Robotics and Automation Letter

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed