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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Liu, Cheng Xu, Yingfu van Kampen, Erik-Jan de Croon, Guido Delft Univ Technol Aerosp Engn Delft Netherlands

ISBN: (纸本)9781713872344

In this paper, we propose an obstacle avoidance solution for a 34-gram quadcopter equipped with a monocular camera. The perception of obstacles is tackled by a lightweight convolutional neural network predicting a dense depth map from a captured grey-scale image. The depth network performs self-supervised learning and thus requires no ground-truth labels that are costly to acquire. Based on depth map, the control strategy is implemented by a behavior state machine that balances the efficiency to explore the environment and the safety of avoiding obstacles. In real-world flight experiments, our solution demonstrates the efficacy of predicting trust-worthy depth maps and a stable control strategy in various cluttered environments. Copyright (c) 2023 The Authors.

关键词： Autonomous robotic systems Perception and sensing Flying robots embedded robotics Monocular depth prediction Self-supervised learning

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robotics in master's degree including multi-disciplinary projects Case of Semester in first year of master's degree 13

Robotics in master's degree including multi-disciplinary pro...

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13th IFAC Symposium on Advances in Control Education (ACE)

作者： Bergeon, Yves Motsch, Jean Krivanek, Vaclav AMSCC Mil Acad St Cyr Coetquidan F-56381 Guer France Univ Def Kounicova 65 Brno 61210 Czech Republic

The development of robotics is increasing in many countries and many jobs will be available in the future in this area. But creating robots involve the mobilization of many different competencies: mechanics, electronics, computer science, power electronics etc. This paper describes the process used to develop a syllabus for a full semester in robotics at master's degree for European students from different countries. It also emphasizes on good practices while dealing with students having heterogeneous background acquired during their bachelor's degree. It shows the nice adequation between the chosen underlayer technical and organizational infrastructure providing a secure system highly reliable. Copyright (C) 2022 The Authors.

关键词： Robotic systems Robot Operating System ROS Mobile robots embedded robotics Mechatronics for Mobility Systems Master's degree studies

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Development of an Field-Programmable Gate Arrays-Based Three-Wheeled Omnidirectional Sensor Mobile Robot for the Teaching of embedded robotics

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SENSOR LETTERS 2013年第11期11卷 2145-2148页

作者： Wu, Ter-Feng Huang, Hsu-Chih Chien, Ying-Ren Natl Ilan Univ Dept Elect Engn Yilan 26041 Taiwan

This paper presents an interesting pedagogical tool, a three-wheeled omnidirectional sensor mobile robot for the teaching of the embedded robotics in undergraduate electrical and mechanical engineering courses. Both embedded system and dynamic control method are integrated in one field-programmable gate arrays (FPGA) to construct an experimental sensor mobile robot by using system-on-a-programmable-chip (SoPC) technology. Such a pedagogical omnidirectional sensor mobile robot with the embedded dynamic control law can be applied to achieve trajectory tracking task. The performance and applicability of the proposed embedded motion controller are exemplified by conducting experiments on an autonomous three-wheeled omnidirectional sensor mobile robot. This pedagogical mobile robot is shown to be significantly effective in helping students to learn embedded robotics.

关键词： embedded robotics FPGA Omnidirectional Sensor Mobile Robot Teaching

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Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network

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IFAC-PapersOnLine 2023年第2期56卷 9312-9317页

作者： Cheng Liu Yingfu Xu Erik-Jan van Kampen Guido de Croon Aerospace Engineering Delft University of Technology

In this paper, we propose an obstacle avoidance solution for a 34-gram quadcopter equipped with a monocular camera. The perception of obstacles is tackled by a lightweight convolutional neural network predicting a dense depth map from a captured grey-scale image. The depth network performs self-supervised learning and thus requires no ground-truth labels that are costly to acquire. Based on the depth map, the control strategy is implemented by a behavior state machine that balances the efficiency to explore the environment and the safety of avoiding obstacles. In real-world flight experiments, our solution demonstrates the efficacy of predicting trust-worthy depth maps and a stable control strategy in various cluttered environments.

关键词： Autonomous robotic systems Perception and sensing Flying robots embedded robotics Monocular depth prediction Self-supervised learning

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robotics in master's degree including multi-disciplinary projects Case of Semester in first year of master's degree

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IFAC-PapersOnLine 2022年第17期55卷 168-173页

作者： Yves Bergeon Jean Motsch Václav Křivánek AMSCC: Military Academy of St-Cyr Coëtquidan 56381 GUER Cedex France University of Defence Kounicova 65 612 10 Brno Czech Republic

关键词： Robotic systems Robot Operating System ROS Mobile robots embedded robotics Mechatronics for Mobility Systems Master's degree studies

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ICS-Zooids - An Experimental Testbed for Cooperative Control Strategies 21st

ICS-Zooids - An Experimental Testbed for Cooperative Control...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Rathakrishnan, Nirmal Goettsch, Patrick Werner, Herbert Hamburg Univ Technol Inst Control Syst Hamburg Germany

In this paper, we present an experimental test bench to implement various cooperative control strategies for multi-agent systems, and illustrate its use with experimental results for a source-seeking problem, where a group of small wheeled robots termed as Zooids should locate a source of a given spatial scalar field. This algorithm is implemented as a validation to demonstrate the capabilities of the test bench. We propose to achieve this by utilising an internal target-based position controller, under the assumptions of convexity of the scalar, continuous/discrete field and availability of local measurements of the field, so that agents can calculate its gradient and its Hessian. We then show in experiments, that using estimated gradients and Hessians (with data communicated from neighbours) in the presence of noisy measurements of the field strength provides satisfactory results for convex fields, under various algorithms such as Steepest Descent, Gauss-Newton, Levenberg Marquardt. These algorithms are analysed, and experimental results are discussed. Copyright (C) 2020 The Authors.

关键词： Multi-agent systems embedded robotics Autonomous robotic systems Networked robotic system modeling and control Networked embedded control systems Control under communication constraints Control under computation constraints Formation control Source-seeking Experimental-platform

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embedded Event-based Visual Odometry 6

Embedded Event-based Visual Odometry

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6th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)

作者： Bertrand, Johan Yigit, Arda Durand, Sylvain Strasbourg Univ ICube Lab INSA Strasbourg CNRS UMR 7357 Strasbourg France

ISBN: (纸本)9781728195810

This paper presents an event-based visual pose estimation algorithm, specifically designed and optimized for embedded robotic platforms. The visual data is provided by a neuromorphic vision sensor. The fully event-based proposed approach is based on Spiking Neural Networks and a modified Hough transform. The method is developed to detect a square visual feature. The multi-thread algorithm is implemented on a Raspberry Pi, the well-known single-board computer used on many embedded platforms, that is connected to a Dynamic Vision Sensor (DVS) through its USB interface. Validation is done on two different experimental platforms and highlights the ability of the odometry algorithm to determine the relative pose of a robot with respect to a square target, in the aim to be integrated in an event-based visual servoing in a future work.

关键词： Event-based odometry embedded robotics DVS spiking Hough transform

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Nengo and Low-Power AI Hardware for Robust, embedded Neurorobotics

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FRONTIERS IN NEUROrobotics 2020年 14卷 568359页

作者： DeWolf, Travis Jaworski, Pawel Eliasmith, Chris Appl Brain Res Waterloo ON Canada Univ Waterloo Ctr Theoret Neurosci Waterloo ON Canada

In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using tools they are already familiar with, such as Keras and Python. We identify four primary challenges in building robust, embedded neurorobotic systems, including: (1) developing infrastructure for interfacing with the environment and sensors;(2) processing task specific sensory signals;(3) generating robust, explainable control signals;and (4) compiling neural networks to run on target hardware. Nengo helps to address these challenges by: (1) providing the NengoInterfaces library, which defines a simple but powerful API for users to interact with simulations and hardware;(2) providing the NengoDL library, which lets users use the Keras and TensorFlow API to develop Nengo models;(3) implementing the Neural Engineering Framework, which provides white-box methods for implementing known functions and circuits;and (4) providing multiple backend libraries, such as NengoLoihi, that enable users to compile the same model to different hardware. We present two examples using Nengo to develop neural networks that run on CPUs and GPUs as well as Intel's neuromorphic chip, Loihi, to demonstrate two variations on this workflow. The first example is an implementation of an end-to-end spiking neural network in Nengo that controls a rover simulated in Mujoco. The network integrates a deep convolutional network that processes visual input from cameras mounted on the rover to track a target, and a control system implementing steering and drive functions in connection weights to guide the rover to the target. The second example uses Nengo as a smaller component in a system that has addressed some but not all of those challenges. Specifically it is used to augment a force-based operational space controller with neural adaptive control to improve performance during a reaching task usi

关键词： Nengo neuromorphic neurorobotic spiking neural networks robotic control adaptive control embedded robotics

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ICS-Zooids - An Experimental Testbed for Cooperative Control Strategies

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IFAC-PapersOnLine 2020年第2期53卷 9734-9739页

作者： Nirmal Rathakrishnan Patrick Göttsch Herbert Werner Institute of Control Systems Hamburg University of Technology Germany

关键词： Multi-agent systems embedded robotics Autonomous robotic systems Networked robotic system modeling control Networked embedded control systems Control under communication constraints Control under computation constraints Formation control Source-seeking Experimental-platform

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Modeling and Calibration of a Tactile Sensor for Robust Grasping

Modeling and Calibration of a Tactile Sensor for Robust Gras...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Cirillo, A. Cirillo, P. De Maria, G. Natale, C. Pirozzi, S. Univ Campania Luigi Vanvitelli Dipartimento Ingn Ind & Informaz Via Roma 29 I-81031 Aversa Italy

Robust grasping of everyday objects is still an open problem in robotics due to uncertainties affecting object physical properties like weight and friction. The present paper proposes to exploit the perception data provided by a tactile sensor to obtain useful information on the contact state, like normal and tangential components of the contact force. A novel mechanical model of the contact between the soft fingertip and the grasped object is here presented and supported by both FEM analysis and experimental verification. The proposed algorithm to extract such information from tactile raw data, based on this model, is simple enough to allow implementation of the grasp control strategy on the control hardware embedded into a standard robotic parallel gripper, so as to mimic human reactive grip responses, which occur when the control of an held object appears uncertain. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： robotics technology Perception and sensing Robots manipulators embedded robotics Autonomous robotic systems

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