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Automatic 3D Object Recognition and Localization for Robotic Grasping 18

Automatic 3D Object Recognition and Localization for Robotic...

引用

18th international conference on informatics in control, automation and robotics (ICINCO)

作者： Santo, Bruno Antao, Liliana Goncalves, Gil Univ Porto Fac Engn Res Ctr Syst & Technol SYSTEC Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9789897585227

With the emergence of Industry 4.0 and its highly re-configurable manufacturing context, the typical fixedposition grasping systems are no longer usable. This reality underlined the necessity for fully automatic and adaptable robotic grasping systems. With that in mind, the primary purpose of this paper is to join Machine Learning models for detection and pose estimation into an automatic system to be used in a grasping environment. The developed system uses Mask-RCNN and Densefusion models for the recognition and pose estimation of objects, respectively. The grasping is executed, taking into consideration both the pose and the object's ID, as well as allowing for user and application adaptability through an initial configuration. The system was tested both on a validation dataset and in a real-world environment. The main results show that the system has more difficulty with complex objects;however, it shows promising results for simpler objects, even with training on a reduced dataset. It is also able to generalize to objects slightly different than the ones seen in training. There is an 80% success rate in the best cases for simple grasping attempts.

关键词： robotics Grasping Object Pose Estimation Object Recognition

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User-Conditioned Neural control Policies for Mobile robotics

User-Conditioned Neural Control Policies for Mobile Robotics

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IEEE international conference on robotics and automation (ICRA)

作者： Leonard Bauersfeld Elia Kaufmann Davide Scaramuzza Department of Informatics Robotics and Perception Group University of Zurich Switzerland Department of Neuroinformatics University of Zurich and ETH Zurich Switzerland

Recently, learning-based controllers have been shown to push mobile robotic systems to their limits and provide the robustness needed for many real-world applications. However, only classical optimization-based control frameworks offer the inherent flexibility to be dynamically adjusted during execution by, for example, setting target speeds or actuator limits. We present a framework to overcome this shortcoming of neural controllers by conditioning them on an auxiliary input. This advance is enabled by including a feature-wise linear modulation layer (FiLM). We use model-free reinforcement-learning to train quadrotor control policies for the task of navigating through a sequence of waypoints in minimum time. By conditioning the policy on the maximum available thrust or the viewing direction relative to the next waypoint, a user can regulate the aggressiveness of the quadrotor's flight during deployment. We demonstrate in simulation and in real-world experiments that a single control policy can achieve close to time-optimal flight performance across the entire performance envelope of the robot, reaching up to 60 km/h and 4.5 g in acceleration. The ability to guide a learned controller during task execution has implications beyond agile quadrotor flight, as conditioning the control policy on human intent helps safely bringing learning based systems out of the well-defined laboratory environment into the wild. Video: https://***/rwT2QQZEH6U

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The Influence of the Gear Reduction Ratio on the Free-floating Space Manipulator's Dynamics 18

The Influence of the Gear Reduction Ratio on the Free-floati...

引用

18th international conference on informatics in control, automation and robotics (ICINCO)

作者： Wojtunik, Mateusz Seweryn, Karol Ctr Badan Kosmicznych Polskiej Akad Nauk CBK PAN Warsaw Poland

ISBN: (纸本)9789897585227

Utilisation of space manipulator mounted on the satellite is one the main methods for the proposed Active Debris Removal and On-Orbit Servicing missions. Precise numerical models of the manipulator's joint are very important as its dynamics has a strong effect on the behaviour of the system including the base where it is mounted. One of aspects that can be considered is the extension of manipulator's dynamical equations with gear kinematic constraints. To achieve this goal, dynamical equations of motion for planar 3DoF free-floating manipulator with gear kinematic constraints are presented in this paper. Open-loop analysis is performed to form conclusions concerning the influence of the gear reduction ratio on space manipulator's dynamics. Torques required to perform end-effector straight line trajectory are evaluated using inverse dynamics path planning algorithm and then utilised as motor driving torques for different gear reduction ratios. It appears that the gear reduction ratio influences the system mass matrix nonlinearly causing the end-effector trajectory to deviate from the straight line. These deviations are already observed for relatively low gear reduction ratios.

关键词： Space robotics Free-floating Manipulators Gear-equipped Space Manipulators Gear Reduction Ratio

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Concept of a Robotic System for Autonomous Coarse Waste Recycling 18

Concept of a Robotic System for Autonomous Coarse Waste Recy...

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18th international conference on informatics in control, automation and robotics (ICINCO)

作者： Tiedemann, Tim Keppner, Matthis Runge, Tom Voegele, Thomas Wittmaier, Martin Wolff, Sebastian Univ Appl Sci Hamburg Dept CS Berliner Tor 7 Hamburg Germany Robot Innovat Ctr German Res Ctr Artificial Intelligence Bremen Germany Bremen Univ Appl Sci Inst Energy Recycling & Environm Protect Bremen Germany

ISBN: (纸本)9789897585227

The recycling of coarse waste such as construction and demolition waste (CDW), bulky waste, etc., is a process that is currently performed mechanically and manually. Unlike packaging waste, commercial waste and the like, which is usually cut or shredded into small pieces and then automatically separated and sorted on conveyor belt-based systems, coarse waste is separated by specialized personnel using wheel loaders, cranes or excavators. This paper presents the concept of a robotic system designed to autonomously separate recyclable coarse materials from bulky waste, demolition and construction waste, etc. The proposed solution explicitly uses existing heavy equipment (e.g., an excavator currently in use on-site) rather than developing a robot from scratch. A particular focus is set on the sensory system options used to identify and classify waste objects.

关键词： Smart Recycling Construction Waste Recycling Multispectral Image Processing Mobile robotics

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Decentralized Circle Formation control for Fish-like Robots in the Real-world via Reinforcement Learning

Decentralized Circle Formation Control for Fish-like Robots ...

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IEEE international conference on robotics and automation (ICRA)

作者： Zhang, Tianhao Li, Yueheng Li, Shuai Ye, Qiwei Wang, Chen Xie, Guangming Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst Intelligent Biomimet Design Lab Beijing 100871 Peoples R China Microsoft Res Asia Beijing 100871 Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing 100871 Peoples R China

ISBN: (纸本)9781728190778

In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalable with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.

关键词： Solid modeling automation Simulation conferences Reinforcement learning Manuals Aerospace electronics

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Custom-made Near Infrared Spectroscope as a Tool for Obtaining Information Regarding the Brain Condition

Custom-made Near Infrared Spectroscope as a Tool for Obtaini...

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international conference on Methods and Models in automation and robotics (MMAR)

作者： Anna Wieczorek Edward Jacek Gorzelańczyk Mariusz Pelc Saravanakumar Duraisamy Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We can obtain valuable information about the human brain using functional Near Infrared Spectroscopy (fNIRS). This paper describes the theoretical basis associated with this neuroimaging method through a custom-made prototype of a single-channel fNIRS device. The optodes were soldered to a milled Printed Circuit Board (PCB) and enclosed in a 3D printed housing. Using this fNIRS device, we performed a preliminary study to measure emotional responses from participants. Our results suggest that fNIRS allows for accurate measurement of emotions evoked by positive and negative images.

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Single-Channel EMG-Based Hand Gesture Classification Using Optimized Neural Networks 1

Single-Channel EMG-Based Hand Gesture Classification Using O...

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1st international conference on Smart Mechatronics, ICSMech 2024

作者： Dewangga, Stanley Adi Widodo, Romy Budhi Lomi, Abraham Soetedjo, Aryuanto Universitas Ma Chung Human-Machine Interaction Research Center Department of Informatics Engineering Malang Indonesia Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350368918

The increasing number of people with disabilities has driven the advancement of assistive technology to aid their activities. One of the current developments in assistive technology is bionic hands which involve machine learning to classify hand gestures. This paper presents the outcomes of a study on hand gesture classification using muscle activity signal data from electromyography sensors while performing four types of hand gestures. Data was gathered from eight subjects, with six subjects allocated as training data and two subjects as test data. The data was recorded using a single-channel surface electromyograph sensor placed on the surface of the extensor digitorum muscle. The selected muscle was focused on classifying extension actions at the Metacarpophalangeal (MCP) joint of the thumb, index and middle fingers. Signal data was then feature extracted through preprocessing and model creation stages. The classification model was created using the Artificial Neural Network (ANN) method and leveraging Genetic Algorithms to obtain the most optimal hidden layer configuration to enhance model accuracy. After four rounds of experimentation with Genetic Algorithm hyperparameter tuning, the best model evaluation result from the four experiments is 42.87%. Subsequently, considering the application of Principal Component Analysis (PCA) to reduce feature dimensions and the differences in signal patterns from subjects, the next experiment was conducted without PCA, eliminating some weakly correlated linear features and excluding data from one subject with distinct patterns. In this experiment, the accuracy result on the test data for all gestures increased to 59.52% and 84,85% on train data. © 2024 IEEE.

关键词： Genetic algorithms

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Receding-Constraint Model Predictive control using a Learned Approximate control-Invariant Set

Receding-Constraint Model Predictive Control using a Learned...

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IEEE international conference on robotics and automation (ICRA)

作者： Gianni Lunardi asia La Rocca Matteo Saveriano Andrea Del Prete Industrial Engineering Department University of Trento Trento Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In recent years, advanced model-based and data-driven control methods are unlocking the potential of complex robotics systems, and we can expect this trend to continue at an exponential rate in the near future. However, ensuring safety with these advanced control methods remains a challenge. A well-known tool to make controllers (either Model Predictive controllers or Reinforcement Learning policies) safe, is the so-called control-invariant set (a.k.a. safe set). Unfortunately, for nonlinear systems, such a set cannot be exactly computed in general. Numerical algorithms exist for computing approximate control-invariant sets, but classic theoretic control methods break down if the set is not exact. This paper presents our recent efforts to address this issue. We present a novel Model Predictive control scheme that can guarantee recursive feasibility and/or safety under weaker assumptions than classic methods. In particular, recursive feasibility is guaranteed by making the safe-set constraint move backward over the horizon, and assuming that such set satisfies a condition that is weaker than control invariance. Safety is instead guaranteed under an even weaker assumption on the safe set, triggering a safe task-abortion strategy whenever a risk of constraint violation is detected. We evaluated our approach on a simulated robot manipulator, empirically demonstrating that it leads to less constraint violations than state-of-the-art approaches, while retaining reasonable performance in terms of tracking cost, number of completed tasks, and computation time.

关键词： Uncertainty Reinforcement learning Predictive models Manipulators Prediction algorithms Safety Task analysis

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From 2D to 3D Mixed Reality Human-Robot Interface in Hazardous Robotic Interventions with the Use of Redundant Mobile Manipulator 18

From 2D to 3D Mixed Reality Human-Robot Interface in Hazardo...

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18th international conference on informatics in control, automation and robotics (ICINCO)

作者： Szczurek, Krzysztof Adam Prades, Raul Marin Matheson, Eloise Perier, Hugo Buonocore, Luca Rosario Di Castro, Mario European Org Nucl Res Meyrin Switzerland Jaume I Univ Castellon Castellon De La Plana Spain

ISBN: (纸本)9789897585227

3D Mixed Reality (MR) Human-Robot Interfaces (HRI) show promise for robotic operators to complete tasks more quickly, safely and with less training. The objective of this study is to assess the use of 3D MR HRI environment in comparison with a standard 2D Graphical User Interface (GUI) in order to control a redundant mobile manipulator. The experimental data was taken during operation with a 9 DOF manipulator mounted in a robotized train, CERN Train Inspection Monorail (TIM), used for the Beam Loss Monitor robotic measurement task in a complex hazardous intervention scenario at CERN. The efficiency and workload of an operator were compared with the use of both types of interfaces with NASA TLX method. The usage of heart rate and Galvanic Skin Response parameters for operator condition and stress monitoring was tested. The results show that teleoperation with 3D MR HRI mitigates cognitive fatigue and stress by improving the operators understanding of both the robot's pose and the surrounding environment or scene.

关键词： Human-Robot Interface robotics Teleoperation Virtual Reality Mixed Reality Operator Workload Galvanic Skin Response

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Differentiable Collision Detection: a Randomized Smoothing Approach

Differentiable Collision Detection: a Randomized Smoothing A...

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IEEE international conference on robotics and automation (ICRA)

作者： Louis Montaut Quentin Le Lidec Antoine Bambade Vladimir Petrik Josef Sivic Justin Carpentier Inria - Département d‘Informatique de l’École normale supérieure PSL Research University Czech Institute - of Informatics Robotics and Cybernetics Czech Technical University

Collision detection is an important component of many robotics applications, from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications, including differentiable simulation.

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