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International Conference on control, Artificial Intelligence, robotics & Optimization (ICCAIRO)

作者： Aristeidis Geladaris Lampis Papakostas Athanasios Mastrogeorgiou Michael Sfakiotakis Panagiotis Polygerinos TWI-Hellas Athens Greece Mechanical Engineering Dept. Control Systems and Robotics Lab (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Crete Greece Control Systems Lab (CSL) School of Mechanical Engineering National Technical University of Athens Greece Electrical and Computer Engineering Dept. Control Systems and Robotics Lab (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Crete Greece

In this paper we propose a method for autonomous navigation in GPS-denied environments that can be deployed in all types of robotic vehicles requiring only a single RGB-D camera. Our algorithm is an integration of localization, mapping and trajectory planning software modules that can handle dynamic environments. dept. measurements and visual odometry are used to (a) create a robocentric Euclidean Signed Distance Fields (ESDF) map in real-time, (b) estimate the position of obstacles surrounding the robot, and (c) calculate the distance and gradient from them. Subsequently, we use an optimization algorithm to plan a collision-free trajectory. We show that a local map can be created faster than with the fixed-size array method used in current optimization methods. Our approach also allows for effective detection of dynamic obstacles by constantly updating the map. As a result, the robot deviates from its initial path only when necessary. We validate our results in simulation by deploying our algorithm in a skid steering rover and a hexacopter in an environment with static and dynamic obstacles.

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A New Robust control for Mobile Robots with Differential Wheels: Output- and State- Dependent Riccati Equation Approach

A New Robust Control for Mobile Robots with Differential Whe...

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International Conference on control, Instrumentation, and Automation

作者： Neda Nasiri Ahmad Fakharian Mohammad Bagher Menhaj Dept. of Electrical Engineering Qazvin Branch Islamic Azad University Qazvin Iran Dept. of Electrical Engineering The Center of Excellence on Control and Robotics Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

A new application of Output- and State- Dependent Riccati Equation (OSDRE) framework for robust control of over-actuated uncertain systems with constraints is addressed in this paper. Towards this aim, an equivalent optimal problem is introduced using OSDRE. The performance index for an auxiliary output- and state- dependent system is modified through inclusion uncertainty upper bounds and then minimized. The proposed method is employed for controller design of a mobile robot with differential wheels. Uncontrollability condition put on SDRE due to differential constraints of wheels. This problem leads to SDRE to be hardly solved. One could overcome this problem by a proper coordinate transformation. In this paper considering the capability of output feedback to remove outer layer of kinematic and direct control the output dynamics, this problem is handled. Simulation results for a mathematical model and a wheeled mobile robot easily demonstrate the efficiency and satisfactory performance of the proposed robust control scheme.

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A Framework Leveraging robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops

A Framework Leveraging Robotics and Machine Learning Technol...

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International Conference on control, Artificial Intelligence, robotics & Optimization (ICCAIRO)

作者： Nikolaos Kounalakis Emmanouil Kalykakis Dimitrios Kosmopoulos John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework.

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Design of Output and Impacts control Systems

Design of Output and Impacts Control Systems

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International Russian Automation Conference (RusAutoCon)

作者： Anatoly Gaiduk Nikolay Prokopenko Vladislav Chumakov Dept. of Automatic Control Systems Scientific Design Office Robotics and Control Systems Taganrog Southern Federal University Rostov-on-Don Russia Dept. of Scientific Research Dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia

To design linear control systems a new method is suggested on the basis of the principle of output and impacts control. The main difference of this principle consists in the use of only measurable variables and separate operators for each input signal of the control device. The design problem has a solution if the plant is completely controllable, and at least the controllable variable is measured. The suggested approach makes it possible to eliminate the well-known contradiction between the accuracy and stability of systems with control on deviation, and to provide the required astatism orders or selective invariance to the external actions. At the same time, the conditions of ensuring the direct indicators of the transient quality and the physical implementation of the control device are taken into account. The coefficients of the equation of the output and impacts control device are determined by solving a system of linear algebraic equations. The control device can be implemented on the basis of either operational amplifiers or sufficiently high-speed digital automation tools. The suggested method can be applied for designing high-quality linear control systems for various purposes.

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Neural Lyapunov control for Nonlinear Systems with Unstructured Uncertainties

arXiv

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arXiv 2023年

作者： Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Dept. of ECE NYU Tandon School of Engineering 5 Metrotech Center BrooklynNY11201 United States

Stabilizing controller design and region of attraction (RoA) estimation are essential in nonlinear control. Moreover, it is challenging to implement a control Lyapunov function (CLF) in practice when only partial knowledge of the system is available. We propose a learning framework that can synthesize state-feedback controllers and a CLF for control-affine nonlinear systems with unstructured uncertainties. Based on a regularity condition on these uncertainties, we model them as bounded disturbances and prove that a CLF for the nominal system (estimate of the true system) is an input-to-state stable control Lyapunov function (ISS-CLF) for the true system when the CLF’s gradient is bounded. We integrate the robust Lyapunov analysis with the learning of both the control law and CLF. We demonstrate the effectiveness of our learning framework on several examples, such as an inverted pendulum system, a strict-feedback system, and a cart-pole system. © 2023, CC BY.

关键词： State feedback

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A Multivariate Functional Analysis of Inter-Hands Spatiotemporal Coordination in Human Bimanual Tasks and its Implications for robotics

A Multivariate Functional Analysis of Inter-Hands Spatiotemp...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Marco Baracca Andre Meixner Giuseppe Averta Paolo Salaris Tamim Asfour Matteo Bianchi Dept. of Information Engineering Research Center “E. Piaggio” University of Pisa Pisa Italy Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Dep. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Dual-arm manipulation is a key enabler for significantly enhancing the interaction between humans and robots, and their capabilities to purposefully shape the surrounding environment. However, the spatiotemporal coordination between the motion of the hands required for this type of actions makes their planning not trivial. A proper definition of these coordination patterns moving from the human example could simplify their translation on the robot side, fostering the generation of effective bimanual tasks. In this work, we propose Multivariate functional Principal Component Analysis (MfPCA) as a mathematical tool to encode inter-hands temporal kinematic covariations in terms of principal spatiotemporal coordination patterns in the Cartesian domain. We compared these patterns extracted from a dataset of human bimanual tasks with those resulting from the usage of classical fPCA, applied independently to each hand (univariate fPCA). We found that MfPCA allows for a better classification of the tasks, with respect to a state of the art taxonomy. For what concerns motion planning, MfPCA and fPCA yield similar accuracy in the reconstruction of the motion, but with a smaller number of principal components needed in the MfPCA case. These results, although preliminary, can open interesting perspectives for the usage of MfPCA for human-like bimanual motion planning and control of robotic manipulators, as well as for action recognition, to enable a more effective human-robot interaction.

关键词： Hands Translation Shape Robot kinematics Taxonomy Human-robot interaction Functional analysis Planning Spatiotemporal phenomena Principal component analysis

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Non-collocated Vibration Suppression using Delay-Based controllers with Multiple Fixed Delays*

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IFAC-PapersOnLine 2023年第2期56卷 180-185页

作者： Adrian Saldanha Wim Michiels Tomáš Vyhlídal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

The problem of non-collocated vibration absorption is targeted utilizing a multiparameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target location, which is different from the position of the absorber. This problem can be translated into an optimization problem of shaping the poles of a closed-loop system with delays subject to zero-location constraints. The solution approach involves remodelling the closed-loop system as a system of delay-differential algebraic equations (DDAEs) which allows to systematically account for delays in the control law, thereby enabling a straightforward technique for computation of controller parameters. Finally, the proposed design method is validated by simulation on a lumped parameter model of a mass-spring-damper system.

关键词： vibration suppression time-delay systems DDAE optimization

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Data-Driven Deep Learning Based Feedback Linearization of Systems with Unknown Dynamics

Data-Driven Deep Learning Based Feedback Linearization of Sy...

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American control Conference (ACC)

作者： Raktim Gautam Goswami Prashanth Krishnamurthy Farshad Khorrami Elec. & Comp. Engg. Dept. Control/Robotics Research Laboratory Tandon School of Engineering (Polytechnic Institute) New York University Brooklyn NY

A methodology is developed to learn a feedback linearization (i.e., nonlinear change of coordinates and input transformation) using a data-driven approach for a single input control-affine nonlinear system with unknown dynamics. We employ deep neural networks to learn the feedback law (input transformation) in conjunction with an extension of invertible neural networks to learn the nonlinear change of coordinates (state transformation). We also learn the matrices A and B of the transformed linear system and define loss terms to ensure controllability of the pair (A, B). The efficacy of our approach is demonstrated by simulations on a nonlinear system. Furthermore, we show that state feedback controllers designed using the feedback linearized system yield expected closed-loop behavior when applied to the original nonlinear system, further demonstrating validity of the learned feedback linearization.

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Ensuring Safety for UAVs Through Event-Triggered Predictive control

Ensuring Safety for UAVs Through Event-Triggered Predictive ...

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Mediterranean Conference on control and Automation (MED)

作者： Sotirios N. Aspragkathos George C. Karras Kostas J. Kyriakopoulos Control Systems Lab School of Mechanical Engineering National Technical University of Athens Athens Greece Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Engineering Division and Center for AI & Robotics (CAIR) New York University Abu Dhabi UAE

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

This paper presents a control system for un-manned aerial vehicles (UAVs) that track deformable objects. Using Event-Triggered (ET) principles and Image Moments-Based Nonlinear Model Predictive control (NMPC), the suggested approach improves flexibility by triggering control changes in response to specific occurrences. The incorporation of Barrier Functions (BFs) enforces safety requirements, which are critical for negotiating uncertain terrains. The framework's implementation and testing on a UAV tracking deformable coastlines are intended to demonstrate its effectiveness in adapting to dynamic situations while adhering to safety limitations.

关键词： Coastlines Target tracking Streaming media Aerodynamics Visual servoing Safety Vehicle dynamics

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An NMPC Framework for Tracking and Releasing a Cable-suspended Load to a Ground Target Using a Multirotor UAV

An NMPC Framework for Tracking and Releasing a Cable-suspend...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Fotis Panetsos George C. Karras Kostas J. Kyriakopoulos Control Systems Lab School of Mechanical Engineering National Technical University of Athens Athens Greece Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this work, we present a nonlinear Model Predictive control (NMPC) scheme for tracking a ground target using a multirotor with a cable-suspended load. The NMPC framework relies on the dynamic model of the UAV with the suspended load and, hence, an estimate of the load state is obtained by fusing the measurements of a downward-facing camera and a load cell with an Unscented Kalman Filter (UKF). Additionally, since the NMPC relies on the future behavior of the system, the trajectory of the ground target throughout the predicted time horizon of the NMPC, is required. Towards this direction, Bézier curves are employed in order to predict the future trajectory of the target, which moves in an arbitrary way. The ultimate goal of the proposed framework is to release the suspended load to the ground target and, consequently, a condition is checked at each time instant that triggers the opening of a gripper, located at the lower edge of the cable. The performance of the proposed control scheme is experimentally validated using an octorotor.

关键词： Target tracking Autonomous aerial vehicles Cameras Robot sensing systems Trajectory Grippers Vehicle dynamics

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