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arXiv 2024年

作者： Kollarčík, Adam Hanzálek, Zdeněk Dept. of Control Engineering Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic

This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for planning under uncertainty. The method utilizes the Adaptive Belief Tree (ABT) algorithm as an approximate solver for the POMDPs. We outline the POMDP formulation, beginning with discretizing the intersection’s topology. Additionally, we present a dynamics model for the prediction of the evolving states of vehicles, such as their position and velocity. Using an observation model, we also describe the connection of those states with the imperfect (noisy) available measurements. Our results confirmed that the method is able to plan collision-free trajectories in a series of simulations utilizing real-world traffic data from aerial footage of two distinct intersections. Furthermore, we studied the impact of parameter adjustments of the ABT algorithm on the method’s performance. This provides guidance in determining reasonable parameter settings, which is valuable for future method applications. © 2024, CC BY-NC-ND.

关键词： Markov processes

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APO-Optimized ARX-Based System Identification of a Ship Maneuvering System

APO-Optimized ARX-Based System Identification of a Ship Mane...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Farooq Olanrewaju Md Shafiullah Mujahed Al-Dhaifallah Muslim Alaran Control & Instrumentation Eng. Dept. King Fahd Univ. of Petroleum & Minerals Dhahran Saudi Arabia IRC for Sustainable Energy Systems King Fahd Univ. of Petroleum & Minerals Dhahran Saudi Arabia Dept. of Robotics Nazarbayev University Astana Kazakhstan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The identification of dynamic models for ships is essential in marine and maritime engineering, as it facilitates accurate prediction and control of vessel behavior. Traditional ship system identification methods involve identifying a few parameters from the dynamic model. Only simplified linearized models have utilized metaheuristic techniques for ship identification recently. This paper describes a new way to use the Artificial Protozoa Optimization (APO) algorithm to make an Autoregression with Exogenous Input (ARX) model for the full ship dynamics work better. The adaptive foraging, dormancy, and reproduction behaviors of protozoa gave rise to the APO algorithm, which is a strong metaheuristic framework for parameter optimization. By leveraging the APO, we aim to enhance the accuracy of ship dynamics identification and its explainability. Evaluation of the model on the provided test data demonstrates that the APO-optimized ARX model provides excellent identification compared to results from existing methods, achieving a mean R 2 score of 0.9437, thus providing a reliable and efficient tool for ship maneuvering identification.

关键词： Adaptation models Accuracy Heuristic algorithms Computational modeling Metaheuristics Predictive models Data models System identification Reliability Marine vehicles

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POMDP-Based Trajectory Planning for On-Ramp Highway Merging

arXiv

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arXiv 2024年

This paper addresses the trajectory planning problem for automated vehicle on-ramp highway merging. To tackle this challenge, we extend our previous work on trajectory planning at unsignalized intersections using Partially Observable Markov Decision Processes (POMDPs). The method utilizes the Adaptive Belief Tree (ABT) algorithm, an approximate sampling-based approach to solve POMDPs efficiently. We outline the POMDP formulation process, beginning with discretizing the highway topology to reduce problem complexity. Additionally, we describe the dynamics and measurement models used to predict future states and establish the relationship between available noisy measurements and predictions. Building on our previous work, the dynamics model is expanded to account for lateral movements necessary for lane changes during the merging process. We also define the reward function, which serves as the primary mechanism for specifying the desired behavior of the automated vehicle, combining multiple goals such as avoiding collisions or maintaining appropriate velocity. Our simulation results, conducted on three scenarios based on real-life traffic data from German highways, demonstrate the method’s ability to generate safe, collision-free, and efficient merging trajectories. This work shows the versatility of this POMDP-based approach in tackling various automated driving problems. © 2024, CC BY-NC-ND.

关键词： Markov processes

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Delayed dynamic-feedback controller design for multi-frequency vibration suppression

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IFAC-PapersOnLine 2024年第27期58卷 196-201页

作者： Adrian Saldanha Adam Peichl Wim Michiels Tomas Vyhlidal 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

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.

关键词： vibration suppression time-delay DDAE

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Real-Time Local Map Generation and Collision-Free Trajectory Planning for Autonomous Vehicles in Dynamic Environments

Real-Time Local Map Generation and Collision-Free Trajectory...

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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 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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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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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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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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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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