An official website of the United States government
Designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers seldom consider estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes in the environment or the robot’s dynamics can lead to catastrophic behaviors. Leveraging recent results in risk-sensitive optimal control, this paper presents a risk-sensitive Extended Kalman Filter that can adapt its estimation to the control objective, hence allowing safe output-feedback Model Predictive Control (MPC). By taking a pessimistic estimate of the value function resulting from the MPC controller, the filter provides increased robustness to the controller in phases of uncertainty as compared to a standard Extended Kalman Filter (EKF). The filter has the same computational complexity as an EKF and can be used for real-time control. The paper evaluates the risk-sensitive behavior of the proposed filter when used in a nonlinear MPC loop on a planar drone and industrial manipulator in simulation, as well as on an external force estimation task on a real quadruped robot. These experiments demonstrate the ability of the approach to significantly improve performance in face of uncertainties.
more »
« less
MCC-EKF for Autonomous Car Security
This work attempts to answer two problems. (1) Can we use the odometry information from two different Simultaneous Localization And Mapping (SLAM) algorithms to get a better estimate of the odometry? and (2) What if one of the SLAM algorithms gets affected by shot noise or by attack vectors, and can we resolve this situation? To answer the first question we focus on fusing odometries from Lidar-based SLAM and Visualbased SLAM using the Extended Kalman Filter (EKF) algorithm. The second question is answered by introducing the Maximum Correntropy Criterion - Extended Kalman Filter (MCC-EKF), which assists in removing/minimizing shot noise or attack vectors injected into the system. We manually simulate the shot noise and see how our system responds to the noise vectors. We also evaluate our approach on KITTI dataset for self-driving cars.
more »
« less
- PAR ID:
- 10231124
- Date Published:
- Journal Name:
- 2020 Fourth IEEE International Conference on Robotic Computing (IRC)
- Page Range / eLocation ID:
- 306 to 313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
To perform power system monitoring and control using synchrophasor measurements, various dynamic state estimators have been proposed in the literature, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). However, they are unable to handle system model parameter errors and any type of outliers, precluding them from being adopted for power system real-time applications. In this paper, we develop a robust iterated extended Kalman filter based on the generalized maximum likelihood approach (termed GM-IEKF) for dynamic state estimation. The proposed GM-IEKF can effectively suppress observation and innovation outliers, which may be induced by model parameter gross errors and cyber attacks. We assess its robustness by carrying out extensive simulations on the IEEE 39-bus test system. From the results, we find that the GM-IEKF is able to cope with at least 25% outliers, including in position of leverage.more » « less
-
Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). In this work, we develop a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design. This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points. We show that the error dynamics follows a log-linear autonomous differential equation with several important consequences: (a) the observable state variables can be rendered convergent with a domain of attraction that is independent of the system’s trajectory; (b) unlike the standard EKF, neither the linearized error dynamics nor the linearized observation model depend on the current state estimate, which (c) leads to improved convergence properties and (d) a local observability matrix that is consistent with the underlying nonlinear system. Furthermore, we demonstrate how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions. We compare the convergence of the proposed InEKF with the commonly used quaternion-based extended Kalman filter (EKF) through both simulations and experiments on a Cassie-series bipedal robot. Filter accuracy is analyzed using motion capture, while a LiDAR mapping experiment provides a practical use case. Overall, the developed contact-aided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in system.more » « less
-
This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in response to hemorrhage and resuscitation with fluids, which makes hemorrhage detection during resuscitation challenging. We developed two sequential inference algorithms for detection of internal hemorrhage based on the Luenberger observer and the extended Kalman filter. The sequential inference algorithms use fluid resuscitation dose and hematocrit measurement as inputs to generate signatures to enable detection of internal hemorrhage. In the case of the extended Kalman filter, the signature is nothing but inferred hemorrhage rate, which allows it to also estimate internal hemorrhage rate. We evaluated the proof-of-concept of these algorithms based on in silico evaluation in 100 virtual patients subject to diverse hemorrhage and resuscitation rates. The results showed that the sequential inference algorithms outperformed naïve internal hemorrhage detection based on the decrease in hematocrit when hematocrit noise level was 1% (average F1 score: Luenberger observer 0.80; extended Kalman filter 0.76; hematocrit 0.59). Relative to the Luenberger observer, the extended Kalman filter demonstrated comparable internal hemorrhage detection performance and superior accuracy in estimating the hemorrhage rate. The analysis of the dependence of the sequential inference algorithms on measurement noise and plant parametric uncertainty showed that small (≤1%) hematocrit noise level and personalization of sequential inference algorithms may enable continuous noninvasive detection of internal hemorrhage and estimation of its rate.more » « less
-
Unmanned aerial manipulators have been growing in popularity over the years, alongside the complexity of the tasks they undertake. Many of these tasks include physical interaction with the environment, where a force control or sensing component is desirable. In these types of applications, the forces and torques, or the wrench, acting on the robot by the environment must be known. This paper presents a wrench observer based on an Extended Kalman filter (EKF), and compares it against acceleration-based, momentum-based, and hybrid wrench observers. Simulations using each of these observers are conducted with an underactuated aerial manipulator composed of a hexarotor with coplanar propellers and a 2-DOF manipulator. Measurement noise on par with what is expected in real-world applications is added to the sensor signals, and results show that the EKF-based wrench observer is superior at noise reduction and wrench estimation in many cases compared to the other observers.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IRC.2020.00056
