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FORCE-controlLED FUZZY-LOGIC-BASED ROBOTIC DEBURRING

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control ENGINEERING PRACTICE 1995年第2期3卷 189-201页

作者： LIU, MH Department of Control Engineering Control System Theory and Robotics Group Technical University of Darmstadt Landgraf-Georg-Str. 4 64283 Darmstadt Germany

In this paper a three-step active deburring strategy is proposed based on fuzzy logic and force feedback control. A strategy for automatic contour following is developed to identify unknown workpiece contours, to automatically generate desired robot motions, and to detect unknown burr sizes and positional inaccuracies. Under the deburring strategy the robot motion and force control are planned actively according to the detected burr sizes and positional inaccuracies. A fuzzy man-machine interface is built to make use of the knowledge of human operators to simplify the burr detection. A fuzzy model of the deburring process is developed to support the planning of deburring procedures. Implementation of and experiments with the proposed strategies are described.

关键词： DEBURRING FORCE control FUZZY systemS FUZZY MODELING GRINDING FUZZY MAN-MACHINE INTERFACE MACHINING POSITION control ROBOTS

A PLANNING APPROACH FOR ROBOT-ASSISTED MULTIPLE-BENT PROFILE HANDLING

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robotics AND COMPUTER-INTEGRATED MANUFACTURING 1994年第1期11卷 35-40页

作者： ZUSSMAN, E HORSCH, T Institute of Machine Tools and Manufacturing Technology Technical University of Berlin 1000 Berlin 10 Pascalstr. 8-9 Germany Control System Theory and Robotics Group Institute of Control Engineering Darmstadt Institute of Technology 6100 Darmstadt Landgraf-Georgstr. 4 Germany

This paper describes a solution for the general problem of handling a multiple-bent profile of sheet metal in the constrained environment of a bending machine. The handling device is a robot feeding the machine and handling the sheet metal in between the bending operations. A new planning approach for a multiple-bent profile handling which is based on the configuration space and a potential function is presented. Special heuristics for the multiple-bent profile are applied and accelerate the motion planning process by recognition of the critical part of the profile and its orientation and position relative to the bending machine. This approach assists the sheet metal part planner to plan in advance the handling operations and to confirm if the robot can perform the handling or not. Two examples of handling multiple-bent profiles inside a bending machine are described and demonstrate the overall approach by a simulation.

关键词： Industrial robots

Force-controlled Robotic Deburring

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IFAC Proceedings Volumes 1993年第2期26卷 855-858页

作者： M.-H. Liu H. Tolle Control System Theory and Robotics Group Department of Control Engineering Technical University of Darmstadt Schlossgraben 1 Darmstadl Germany

In this paper a three-step active deburring strategy is proposed based on force feedback control. Strategies for automatic contour following are developed to identify unknown workpiece contours, to automatically generate desired robot motions, and to detect unknown burr sizes. Under the active deburring strategy burr size variations are identified through contour following and different sizes of burrs are removed with a specification of a variable desired deburring force. Implementation of and experiments with the proposed strategies are described.

关键词： Deburring force control grinding machining position control robots teaching

Equivariant filter design for inertial navigation systems with input measurement biases

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arXiv 2022年

作者： Fornasier, Alessandro Ng, Yonhon Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

Inertial Navigation systems (INS) are a key technology for autonomous vehicles applications. Recent advances in estimation and filter design for the INS problem have exploited geometry and symmetry to overcome limitations of the classical Extended Kalman Filter (EKF) approach that formed the mainstay of INS systems since the mid-twentieth century. The industry standard INS filter, the Multiplicative Extended Kalman Filter (MEKF), uses a geometric construction for attitude estimation coupled with classical Euclidean construction for position, velocity and bias estimation. The recent Invariant Extended Kalman Filter (IEKF) provides a geometric framework for the full navigation states, integrating attitude, position and velocity, but still uses the classical Euclidean construction to model the bias states. In this paper, we use the recently proposed Equivariant Filter (EqF) framework to derive a novel observer for biased inertial-based navigation in a fully geometric framework. The introduction of virtual velocity inputs with associated virtual bias leads to a full equivariant symmetry on the augmented system. The resulting filter performance is evaluated with both simulated and realworld data, and demonstrates increased robustness to a wide range of erroneous initial conditions, and improved accuracy when compared with the industry standard Multiplicative EKF (MEKF) approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Inertial navigation systems

Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation with Online Calibration

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arXiv 2022年

作者： Fornasier, Alessandro Ng, Yonhon Brommer, Christian Böhm, Christoph Mahony, Robert Weiss, Stephan The Control of Networked Systems Group University of Klagenfurt Austria The System Theory and Robotics Lab Australian National University Australia

Stochastic filters for on-line state estimation are a core technology for autonomous systems. The performance of such filters is one of the key limiting factors to a system’s capability. Both asymptotic behavior (e.g., for regular operation) and transient response (e.g., for fast initialization and reset) of such filters are of crucial importance in guaranteeing robust operation of autonomous systems. This paper introduces a new generic formulation for a gyroscope aided attitude estimator using N direction measurements including both body-frame and reference-frame direction type measurements. The approach is based on an integrated state formulation that incorporates navigation, extrinsic calibration for all direction sensors, and gyroscope bias states in a single equivariant geometric structure. This newly proposed symmetry allows modular addition of different direction measurements and their extrinsic calibration while maintaining the ability to include bias states in the same symmetry. The subsequently proposed filter-based estimator using this symmetry noticeably improves the transient response, and the asymptotic bias and extrinsic calibration estimation compared to state-of-the-art approaches. The estimator is verified in statistically representative simulations and is tested in real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

An Equivariant Approach to Robust State Estimation for the ArduPilot Autopilot system

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An Equivariant Approach to Robust State Estimation for the A...

IEEE International Conference on robotics and Automation (ICRA)

作者： Alessandro Fornasier Yixiao Ge Pieter van Goor Martin Scheiber Andrew Tridgell Robert Mahony Stephan Weiss Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The majority of commercial and open-source autopilot software for uncrewed aerial vehicles rely on the tried and tested extended Kalman filter (EKF) to provide the state estimation solution for the inertial navigation system (INS). While modern implementations achieve remarkable robustness, it is often due to the careful implementation of exception code for a multitude of corner cases along with significant skilled tuning effort. In this paper, we use the data wealth of the ArduPilot community to identify and highlight the most common real-world challenges in INS state estimation, including sensor self-calibration, robustness in static conditions, global navigation satellite system (GNSS) outliers and shifts, and robustness to faulty inertial measurement units (IMUs). We propose a novel equivariant filter (EqF) formulation for the INS solution that exploits a Semi-Direct-Bias symmetry group for multi-sensor fusion with self-calibration capabilities and incorporates equivariant velocity-type measurements. We augment the filter with a simple innovation-covariance inflation strategy that seamlessly handles GNSS outliers and shifts without requiring coding of a whole set of exception cases. We use real-world data from the Ardupilot community to demonstrate the performance of the proposed filter on known cases where existing filters fail without careful exception handling or case-specific tuning and benchmark against the ArduPilot’s EKF3, the most sophisticated EKF implementation currently available.

关键词： Global navigation satellite system Low-pass filters Inertial navigation Robot sensing systems Autonomous aerial vehicles Robustness Software

MSCEqF: A Multi State Constraint Equivariant Filter for Vision-aided Inertial Navigation

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arXiv 2023年

作者： Fornasier, Alessandro van Goor, Pieter Allak, Eren Mahony, Robert Weiss, Stephan The Control of Networked Systems Group University of Klagenfurt Austria The System Theory and Robotics Lab Australian National University Australia

This letter re-visits the problem of visual-inertial navigation system (VINS) and presents a novel filter design we dub the multi state constraint equivariant filter (MSCEqF, in analogy to the well known MSCKF). We define a symmetry group and corresponding group action that allow specifically the design of an equivariant filter for the problem of visual-inertial odometry (VIO) including IMU bias, and camera intrinsic and extrinsic calibration states. In contrast to state-of-the-art invariant extended Kalman filter (IEKF) approaches that simply tack IMU bias and other states onto the SE2(3) group, our filter builds upon a symmetry that properly includes all the states in the group structure. Thus, we achieve improved behavior, particularly when linearization points largely deviate from the truth (i.e., on transients upon state disturbances). Our approach is inherently consistent even during convergence phases from significant errors without the need for error uncertainty adaptation, observability constraint, or other consistency enforcing techniques. This leads to greatly improved estimator behavior for significant error and unexpected state changes during, e.g., long-duration missions. We evaluate our approach with a multitude of different experiments using three different prominent real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

Equivariant IMU Preintegration with Biases: a Galilean group Approach

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arXiv 2024年

作者： Delama, Giulio Fornasier, Alessandro Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation system (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discrete-time formulation of the IMU preintegration on Gal(3) ⋈ gal(3), the left-trivialization of the tangent group of the Galilean group Gal(3). We define a novel preintegration error that geometrically couples the navigation states and the bias leading to lower linearization error. Our method improves in consistency compared to existing preintegration approaches which treat IMU biases as a separate state-space. Extensive validation against state-of-the-art methods, both in simulation and with real-world IMU data, implementation in the Lie++ library, and open-source code are provided. Copyright © 2024, The Authors. All rights reserved.

关键词： Inertial navigation systems

Equivariant Symmetries for Inertial Navigation systems

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arXiv 2023年

作者： Fornasier, Alessandro Ge, Yixiao van Goor, Pieter Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian Centre for Robotic Vision Australian National University Australia

This paper investigates the problem of inertial navigation system (INS) filter design through the lens of symmetry. The extended Kalman filter (EKF) and its variants, have been the staple of INS filtering for 50 years;however, recent advances in inertial navigation systems have exploited matrix Lie group structure to design stochastic filters and state observers that have been shown to display superior performance compared to classical solutions. In this work, we consider the case where a vehicle has an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver. We show that all the modern variants of the EKF for these sensors can be interpreted as the recently proposed Equivariant Filter (EqF) design methodology applied to different choices of symmetry group for the INS problem. This leads us to propose two new symmetries for the INS problem that have not been considered in the prior literature, and provide a discussion of the relative strengths and weaknesses of all the different algorithms. We believe the collection of symmetries that we present here capture all the sensible choices of symmetry for this problem and sensor suite, and that the analysis provided is indicative of the relative real-world performance potential of the different algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Inertial navigation systems