Article
Engineering, Electrical & Electronic
Yangtianze Tao, Stephen Shing-Toung Yau
Summary: In this paper, we propose a novel outlier-robust iterative extended Kalman filtering (OR-IEKF) framework based on nonlinear regression formulation of update step. The OR-IEKF framework introduces a new Kalman-type update step with reweighted prediction covariance and reweighted observation noise covariance, which can eliminate large outliers caused by unknown outlier noises. By employing robust cost functions, three algorithms are derived to solve the special nonlinear regression problems. The performances of these new filters are evaluated in a simulation study of a nonlinear system.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Energy & Fuels
Mojtaba Ahmadieh Khanesar, David Branson
Summary: This paper presents a sliding mode fuzzy control approach for controlling industrial robots at static and low-speed situations. The approach utilizes an extended Kalman filter to translate coordinates and a sliding mode fuzzy controller for controlling the robot dynamics. Simulation results show that the proposed approach outperforms the sliding mode control method in the presence of uncertainties.
Article
Environmental Sciences
Zhihui Yin, Jichao Yang, Yue Ma, Shengli Wang, Dashuai Chai, Haonan Cui
Summary: This paper proposes a Robust Adaptive Extended Kalman Filter (RAKF) method for GNSS/INS integrated navigation systems. By constructing an optimal measurement noise covariance matrix based on the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, the method improves positioning accuracies and heading angle accuracy in complex urban environments. Experimental results show significant improvements compared to classical EKF, AKF, and RKF algorithms.
Article
Engineering, Aerospace
Cory T. Fraser, Steve Ulrich
Summary: Two unique adaptive extended Kalman filter algorithms are proposed to address spacecraft formation flying missions in near-Earth orbit, capable of real-time updating of internal noise characteristics. Numerical simulations show that these algorithms are significantly more robust to filter initialization errors, dynamics modeling deficiencies, and measurement noises compared to the standard Kalman filter.
Article
Engineering, Multidisciplinary
Andrew S. Lee, Waleed Hilal, S. Andrew Gadsden, M. Al-Shabi
Summary: This paper proposes a novel adaptive estimation strategy, which combines the optimality of EKF and UKF with the robustness of ESIF, for a nonlinear system with modeling uncertainties. It introduces the EKF-ESIF and UKF-ESIF methods and uses a sliding innovation filter and a time-varying sliding boundary layer to detect faults or uncertainties.
Article
Robotics
Seyed Fakoorian, Angel Santamaria-Navarro, Brett T. Lopez, Dan Simon, Ali-akbar Agha-mohammadi
Summary: This work presents a resilient and adaptive state estimation framework, AMCCKF, for robots operating in perceptually-degraded environments, which is able to robustly handle corrupted measurements and adjust filter parameters online for improved performance. Two methods are developed, modifying noise models and kernel bandwidth based on measurement quality, with differences in computational complexity and overall performance. The framework is validated through real experiments on aerial and ground robots, forming part of the solution used in the DARPA Subterranean Challenge by the COSTAR team.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Engineering, Electrical & Electronic
Boxuan Zhang, Xianghao Hou, Yixin Yang, Long Yang, Yong Wang
Summary: This study addresses the issue of robust underwater direction-of-arrival (DOA) tracking using hydrophone array in passive sonar signal processing. To handle the unknown measurement noise, a computationally efficient version of the variational Bayesian adaptive extended Kalman filter (FVB-AEKF) is proposed. The FVB-AEKF reduces the computational complexity while ensuring accuracy in DOA tracking.
IEEE SENSORS JOURNAL
(2023)
Article
Energy & Fuels
Yuanmao Ye, Zhenpeng Li, Jingxiong Lin, Xiaolin Wang
Summary: This paper proposes a new model-based SOC estimation method for lithium-ion batteries, which integrates parameter identification and state estimation into one closed-loop algorithm. The algorithm utilizes extended stochastic gradient algorithm and adaptive extended Kalman filter for parameter identification and state estimation respectively. Experimental results demonstrate the good performance of the proposed method in terms of estimation accuracy and robustness under different test conditions, making it more suitable for online SOC estimation of lithium-ion batteries.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Automation & Control Systems
Huazhen Fang, Mulugeta A. Haile, Yebin Wang
Summary: This paper introduces an innovative saturation mechanism to make the Extended Kalman Filter robust against outliers, leading to the development of robust EKF approaches for both continuous- and discrete-time systems. The proposed approaches demonstrate the capability to reject outliers of various magnitudes, types, and durations at significant computational efficiency without requiring additional measurement redundancy.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Hoang Ngoc Tran, Jae Wook Jeon
Summary: This paper proposes a robust mechanical parameter estimation and adaptive speed control algorithm for permanent magnet synchronous motor (PMSM) drive systems based on the dual adaptive sliding-mode method. The methods include a robust adaptive sliding mode mechanical observer (RASM) and mechanical parameter identification (MPI) to eliminate system parameter errors, as well as an adaptive sliding-mode speed control (ASMSC) to reduce control signal chattering. The experimental results verify the accuracy and stability of the proposed scheme.
Article
Engineering, Marine
Seongpil Cho, Hyungwon Shim, Young-Shik Kim
Summary: The conventional PD control algorithm with gain scheduling is commonly used in dynamic positioning systems, but it makes the system more complex. The sliding-mode control algorithm can control any point on a floating production storage and offloading vessel, taking into account the uncertainty of vessel dynamics, environmental disturbances, and transient performance.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Yuming Chen, Wei Li, Yuqiao Wang
Summary: A novel fast indirect in-motion coarse alignment method is proposed, utilizing the output of GPS and SINS to construct the measurement model and using the rotation vector as the attitude parameterization. An adaptive Student's t-based Kalman filter is introduced to handle challenges of measurement noise distribution deviation and inaccurate noise covariance matrix.
Article
Environmental Sciences
Andreu Salcedo-Bosch, Francesc Rocadenbosch, Joaquim Sospedra
Summary: A new method for correcting motion-induced error in Doppler Wind-LiDAR data was presented using a Robust Adaptive Unscented Kalman Filter. After correction, motion-induced turbulence was reduced, and statistical indicators showed overall improvement.
Article
Chemistry, Multidisciplinary
Tadeo Espinoza-Fraire, Armando Saenz, Francisco Salas, Raymundo Juarez, Wojciech Giernacki
Summary: This study proposes three robust mechanisms based on the MIT rule and sliding-mode techniques, which tune the gains of an adaptive Proportional-Derivative controller to achieve trajectory tracking for a quadrotor.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Electrical & Electronic
Baojian Yang, Binhan Du, Ning Li, Siyu Li, Zhiyong Shi
Summary: This paper proposes a centered error entropy based variational Bayesian adaptive and robust Kalman filter (CEEVBKF) to suppress outlier noise and estimate the unknown noise covariance adaptively. It improves the iterative efficiency and reduces the parameter sensitivity by jointly estimating the centered error entropy and variational Bayesian.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Robotics
Alexander H. Chango, Patricio A. Vela
Article
Automation & Control Systems
Alexander H. Chang, Patricio A. Vela
ROBOTICS AND AUTONOMOUS SYSTEMS
(2020)
Article
Robotics
Yipu Zhao, Patricio A. Vela
IEEE TRANSACTIONS ON ROBOTICS
(2020)
Article
Robotics
Ruinian Xu, Fu-Jen Chu, Chao Tang, Weiyu Liu, Patricio A. Vela
Summary: This study explores the integration of keypoint detections into a deep network affordance segmentation pipeline to better interpret the functionality of object parts. By creating a new dataset and conducting joint training, the trained network AffKp shows promising performance in both affordance segmentation and keypoint detection.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Automation & Control Systems
Alexander H. Chang, Christian M. Hubicki, Jeffrey J. Aguilar, Daniel Goldman, Aaron D. Ames, Patricio A. Vela
Summary: Dynamic terrain poses challenges for legged robots in real-world scenarios, but through tasks like vertical robotic jumping, robots can learn to adapt and achieve control objectives. This study demonstrates a capability to rapidly estimate and adapt to unknown terrain dynamics within a few iterations, using Gaussian process-based regression and optimization tools.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2021)
Article
Robotics
Ruoyang Xu, Shiyu Feng, Patricio A. Vela
Summary: This study proposes a local planning module called "potential gap" that integrates gap-based local navigation methods with artificial potential field (APF) methods for hierarchical navigation systems. By using sensory-derived local free-space models to detect gaps and synthesize the APF, collision-free trajectories can be achieved. Algorithm modifications are introduced to correct errors and enhance robustness for non-ideal models, particularly nonholonomic robot models. Integration of the potential gap local planner into hierarchical navigation systems provides local goals and trajectories for collision-free navigation through unknown environments, as confirmed by Monte Carlo experiments in benchmark worlds.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Ruinian Xu, Fu-Jen Chu, Patricio A. Vela
Summary: Contemporary grasp detection approaches often rely on deep learning to handle uncertainties in sensors and object models. This paper presents a novel approach to grasp detection by treating it as keypoint detection in image-space. The proposed method detects grasp candidates as pairs of keypoints, and incorporates a non-local module to capture dependencies between keypoints. Experimental results show that the approach achieves a good balance between accuracy and speed, and demonstrates robustness to various nuisance factors in different types of grasping experiments.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
(2022)
Article
Automation & Control Systems
Shiyu Feng, Zixuan Wu, Yipu Zhao, Patricio A. Vela
Summary: This article presents a stereo image-based visual servoing system for trajectory tracking of a nonholonomic robot. The system does not require externally derived pose information or a known visual map of the environment. It utilizes a feature-based, indirect SLAM method to provide a pool of available features with estimated depth, which are then propagated forward in time to generate image feature trajectories for visual servoing. Experimental results demonstrate the benefits of the system for navigating unknown areas without absolute positioning.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Robotics
Ruinian Xu, Hongyi Chen, Yunzhi Lin, Patricio A. Vela
Summary: This paper investigates human instruction following for robotic manipulation via a hybrid, modular system with symbolic and connectionist elements. Symbolic methods build modular systems with semantic parsing and task planning modules for producing sequences of actions from natural language requests, while modern connectionist methods employ deep neural networks for mapping inputs to a sequence of low-level actions, in an end-to-end fashion. The hybrid, modular system blends these two approaches to create a modular framework that formulates instruction following as symbolic goal learning via deep neural networks followed by task planning via symbolic planners.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Proceedings Paper
Automation & Control Systems
Yipu Zhao, Justin S. Smith, Sambhu H. Karumanchi, Patricio A. Vela
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Yi-Pu Zhao, Haotian Wu, Patricio A. Vela
COMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCE
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Yi-Pu Zhao, Patricio A. Vela
COMPUTING IN CIVIL ENGINEERING 2019: VISUALIZATION, INFORMATION MODELING, AND SIMULATION
(2019)
Article
Robotics
Fu-Jen Chu, Ruinian Xu, Landan Seguin, Patricio A. Vela
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)
Article
Robotics
Fu-Jen Chu, Ruinian Xu, Patricio A. Vela
IEEE ROBOTICS AND AUTOMATION LETTERS
(2019)
