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A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment

Research Square

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Research Square 2024年

作者： Khlif, Nessrine Khraief, Nahla Belghith, Safya Laboratory of Robotics Informatics and Complex Systems RISC lab - LR16ES07 National Engineering School of Tunis Electrical Engineering Department University of Tunis EL Manar Tunis Tunisia

Purpose: The field of autonomous mobile robots (AMRs) has experienced significant growth in recent years, propelled by advancements in autonomous driving and unmanned aerial vehicles (UAVs). The integration of intelligence into robotic systems necessitates addressing various research challenges, with navigation emerging as a pivotal aspect of mobile robotics. This paper explores the three fundamental questions central to the navigation problem: localization (determining the robot’s position), mapping (creating a representation of the environment), and path planning (determining the optimal route to the destination). The proposed solution to the mobile robot navigation problem involves the seamless integration of these three foundational navigation components. Methods: Our comparative analysis between the Q-learning modified method and a deep Q-network (DQN) in simulated gym pathfinding tasks reveals the efficacy of this approach. The modified Q-learning algorithm consistently outperforms DQN, demonstrating its superior ability to navigate complex environments and achieve optimal solutions. The transition from a definite environment to a simulated gym environment serves as a valuable validation of the method’s applicability in real-world scenarios. By rigorously evaluating our algorithm in a controlled setting, we can ensure its robustness and effectiveness across a broader range of applications. Results: In essence, our study establishes the modified Q-learning algorithm as a promising new approach to addressing the exploration-exploitation dilemma in reinforcement learning. Its superior performance in simulated gym environments suggests its potential for real-world applications in various domains, including robotics, autonomous navigation, and game development. Conclusion: The paper furnishes a comprehensive overview of research on autonomous mobile robot navigation. It begins with a succinct introduction to the diverse facets of navigation, followed by an examinat

关键词： Unmanned aerial vehicles (UAV)

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Trajectory Optimization for Quadruped Mobile Manipulators that Carry Heavy Payload

Trajectory Optimization for Quadruped Mobile Manipulators th...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ioannis Dadiotis Arturo Laurenzi Nikos Tsagarakis Humanoid and Human-Centered Mechatronics Research Line Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (纸本)9798350309805

This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. The proposed payload-aware formulation simultaneously plans locomotion, payload manipulation and considers both robot and payload model dynamics while remaining computationally efficient. At the presence of heavy payload, the approach exhibits reduced leg outstretching (thus increased manipulability) in kinematically demanding motions due to the contribution of payload manipulation in the optimization. The framework's computational efficiency and performance is validated through a number of simulation and experimental studies with the bi-manual quadruped CENTAURO robot carrying on its arms a payload that exceeds 15 % of its mass and traversing non-flat terrain.

关键词： Legged locomotion Computational modeling Dynamics Humanoid robots Computational efficiency Planning Quadrupedal robots

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The development and evaluation of a combined initial alignment algorithm for strapdown inertial navigation system 3

The development and evaluation of a combined initial alignme...

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3rf International Scientific Conference on Metrological Support of InnovativeTechnologies, ICMSIT 2022

作者： Afonin, A.A. Sulakov, A.S. Maamo, M.S. Shapovalov, N.A. Moscow University for Industry and Finance "synergy" Faculty of Robotics and Mechatronics 9/14 street Meshchanskaya Moscow129090 Russia Department of Control Systems Informatics and Power Engineering 4 Volokolamskoe shosse Moscow125993 Russia

This paper proposes a combined initial alignment algorithm for strapdown inertial navigation system, in which the initial alignment is carried out in two stages during the process of preflight preparations while the aircraft is not moving. This method of initial alignment has the advantages of being conducted based on the Earth's equatorial (Greenwich) coordinate system, which is also used by the aircraft onboard navigation system. In the first stage, the coarse initial alignment is carried out, and this is done according to the orientation parameters using the vector matching method. Then, in the second stage, a fine procedure is carried out, which may be looked at as an integrated system implemented using optimal Kalman filter. In this case, the initial parameters of orientation and navigation are refined, as well as the systematic errors of the inertial sensors are estimated and corrected. The simulation results show that the required level of initial alignment accuracy could be achieved by applying coarse and fine initial alignment procedures not sequentially but in a combination. © Published under licence by IOP Publishing Ltd.

关键词： Inertial navigation systems

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Compressive Image Scanning Microscope

arXiv

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arXiv 2023年

作者： Gunalan, Ajay Castello, Marco Piazza, Simonluca Li, Shunlei Diaspro, Alberto Mattos, Leonardo S. Bianchini, Paolo Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Genoa Instruments s.r.l. Genoa Italy Nanoscopy Istituto Italiano di Tecnologia Genoa Italy DIFILAB Department of Physics University of Genoa Genoa Italy

We present a novel approach to implement compressive sensing in laser scanning microscopes (LSM), specifically in image scanning microscopy (ISM), using a single-photon avalanche diode (SPAD) array detector. Our method addresses two significant limitations in applying compressive sensing to LSM: the time to compute the sampling matrix and the quality of reconstructed images. We employ a fixed sampling strategy, skipping alternate rows and columns during data acquisition, which reduces the number of points scanned by a factor of four and eliminates the need to compute different sampling matrices. By exploiting the parallel images generated by the SPAD array, we improve the quality of the reconstructed compressive-ISM images compared to standard compressive confocal LSM images. Our results demonstrate the effectiveness of our approach in producing higher-quality images with reduced data acquisition time and potential benefits in reducing photobleaching. Copyright © 2023, The Authors. All rights reserved.

关键词： Data acquisition

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Smart Learning Communities for Bridging the Gender Gap in STEM 6

Smart Learning Communities for Bridging the Gender Gap in ST...

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6th International Forum on Research and Technology for Society and Industry, RTSI 2021

作者： Coccoli, Mauro Ferrara, Filomena Floriana Maresca, Paolo DIBRIS-University of Genoa Dept. of Informatics Bioengineering Robotics and Systems Engineering Genoa Italy Fondazione IBM Italia IBM Italy DIETI Federico II Naples University Naples Italy

ISBN: (纸本)9781665441353

This paper presents the results achieved in four years of activity within the NERD? project, which was created by IBM Italy and La Sapienza University of Rome and is carried on in collaboration with several universities and schools, in Italy. The long-Term objective of the project is bridging the gender gap in the so-called STEM disciplines (science, technology, engineering and mathematics) starting from the secondary education. Specifically, we refer to the experience conducted in the field of informatics and computer engineering by the Federico II University of Naples, which empowered the original educational model of the NERD? project, introducing collaboration issues within a smart learning community. This involved people from industry, university and secondary schools, including both teachers and students at all the levels. © 2021 IEEE.

关键词： engineering education

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In-hand manipulation planning using human motion dictionary

arXiv

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arXiv 2023年

作者： Hammoud, Ali Belcamino, Valerio Carfi, Alessandro Perdereau, Veronique Mastrogiovanni, Fulvio Sorbonne Universite Institut des Systemes Intelligents et de Robotique Isir ParisF-75005 France Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa TheEngineRoom Via Opera Pia 13 Genoa16145 Italy

Dexterous in-hand manipulation is a peculiar and useful human skill. This ability requires the coordination of many senses and hand motion to adhere to many constraints. These constraints vary and can be influenced by the object characteristics or the specific application. One of the key elements for a robotic platform to implement reliable in-hand manipulation skills is to be able to integrate those constraints in their motion generations. These constraints can be implicitly modelled, learned through experience or human demonstrations. We propose a method based on motion primitives dictionaries to learn and reproduce in-hand manipulation skills. In particular, we focused on fingertip motions during the manipulation, and we defined an optimization process to combine motion primitives to reach specific fingertip configurations. The results of this work show that the proposed approach can generate manipulation motion coherent with the human one and that manipulation constraints are inherited even without an explicit formalization. © 2023, CC BY.

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A Shared Telemanipulation Interface to Facilitate Bimanual Grasping and Transportation of Objects of Unknown Mass

A Shared Telemanipulation Interface to Facilitate Bimanual G...

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IEEE-RAS International Conference on Humanoid Robots

作者： Davide Torielli Luca Muratore Alessio De Luca Nikos Tsagarakis Humanoids and Human Centered Mechatronics (HHCM) Istituto Italiano di Tecnologia Genova Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Genova Italy

ISBN: (纸本)9798350309805

The teleoperation of robots with human-like capabilities may pose significant challenges to the human operator due to the kinematic complexity and redundancy of these robots. Bimanual telemanipulation represents such a challenging task that requires precise coordination of the two arms to perform a stable bimanual grasp on an object and eventually transport the object while maintaining the grasp. In this work, we present a shared control telemanipulation interface to facilitate the bimanual grasping and transportation of objects of unknown mass. With the proposed method, the robot is able to transport the object maintaining autonomously a sufficient amount of grasping force while accepting commands from the operator to reach the desired location. As humans do, it is not necessary to know the weight of the object in advance; instead, the robot estimates it during the lifting phase. On the basis of the estimated weight, the required amount of grasping force is computed. During object transportation, the robot autonomously regulates the grasping forces in a shared control fashion, allowing the operator to seamlessly command only the trajectories of the object. The proposed method has been implemented and validated on the CENTAURO robot, a quadrupedal platform with a humanoid dual arm upper body, performing experiment where objects of different weights and dimensions must be picked up and transported.

关键词： Robot kinematics Force Transportation Humanoid robots Grasping Robot sensing systems End effectors

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A framework inspired by cognitive memory to learn planning domains from demonstrations 6

A framework inspired by cognitive memory to learn planning d...

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6th Italian Workshop on Artificial Intelligence and robotics, AIRO 2019

作者： Buoncompagni, Luca Mastrogiovanni, Fulvio Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy

We introduce a framework for acquiring structured knowledge from human-lead demonstrations and generate task planning domains for robots. It is based on a novel algorithm that builds symbolic models of environmental states as structured memory items, which are stored and retrieved after reasoning processes. The paper addresses the formalisation of memory items and its management over time through cognitive-like functions, i.e., encoding, storing, retrieving, consolidating and forgetting. Based on the two simple scenarios, we present preliminary results and we discuss the benefits and limitations of our approach. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Data description

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Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

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npj Flexible Electronics 2024年第1期8卷 856-868页

作者： Jinwoo Lee Kangkyu Kwon Ira Soltis Jared Matthews Yoon Jae Lee Hojoong Kim Lissette Romero Nathan Zavanelli Youngjin Kwon Shinjae Kwon Jimin Lee Yewon Na Sung Hoon Lee Ki Jun Yu Minoru Shinohara Frank L.Hammond Woon-Hong Yeo Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoul 04620Republic of Korea IEN Center for Wearable Intelligent Systems and Healthcare Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Industrial Design Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Biological Sciences Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Materials Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper ***,we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength *** embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities,which are simultaneously computed to determine the user’s intended *** deep learning predicts four upper-limb joint motions with an average accuracy of 96.2%at a 500–550 ms response rate,suggesting that the exoskeleton operates just by human *** addition,an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87mm at *** intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

关键词： skeleton learning utilize

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Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks

arXiv

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arXiv 2023年

作者： Ledda, Emanuele Fumera, Giorgio Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via Dodecaneso 35 Genova16146 Italy Department of Electric and Electronic Engineering University of Cagliari Via Marengo 3 Cagliari09100 Italy

Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during inference for evaluating uncertainty. This approach, which we call dropout injection, provides clear benefits over its traditional counterpart (which we call embedded dropout) since it allows one to obtain a post hoc uncertainty measure for any existing network previously trained without dropout, avoiding an additional, time-consuming training process. Unfortunately, no previous work compared injected and embedded dropout;therefore, we provide the first thorough investigation, focusing on regression problems. The main contribution of our work is to provide guidelines on the effective use of injected dropout so that it can be a practical alternative to the current use of embedded dropout. In particular, we show that its effectiveness strongly relies on a suitable scaling of the corresponding uncertainty measure, and we discuss the trade-off between negative log-likelihood and calibration error as a function of the scale factor. Experimental results on UCI data sets and crowd counting benchmarks support our claim that dropout injection can effectively behave as a competitive post hoc uncertainty quantification technique. © 2023, CC BY.

关键词： Monte Carlo methods

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