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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sean McGovern Huitan Mao Jing Xiao Robotics Engineering Program Worcester Polytechnic Institute Department of Computer Science University of North Carolina at Charlotte

This paper introduces a reinforcement learning algorithm with robot manipulation to learn an arbitrary object's center of mass whose physical material composition is unknown. Robot learning is through manipulation of the object in a sequence of actions. The effectiveness of the algorithm is demonstrated in simulation to locate the centers of mass of rocks with complex shapes, with even or uneven mass distributions, and confirmed by vertically stacking the rocks along their learned centers of mass both in simulation and in real experiments.

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Topological Approximate Dynamic programming under Temporal Logic Constraints

arXiv

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arXiv 2019年

作者： Li, Lening Fu, Jie Robotics Engineering Program Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we develop a model-free approximate dynamic programming method for stochastic systems modeled as Markov decision processes to maximize the probability of satisfying high-level system specifications expressed in a subclass of temporal logic formulas—syntactically co-safe linear temporal logic. Our proposed method includes two steps: First, we decompose the planning problem into a sequence of sub-problems based on the topological property of the task automaton which is translated from a temporal logic formula. Second, we extend a model-free approximate dynamic programming method to solve value functions, one for each state in the task automaton, in an order reverse to the causal dependency. Particularly, we show that the run-time of the proposed algorithm does not grow exponentially with the size of specifications. The correctness and efficiency of the algorithm are demonstrated using a robotic motion planning example. Copyright © 2019, The Authors. All rights reserved.

关键词： Specifications

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Haptic display for virtual reality: progress and challenges

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Virtual Reality & Intelligent Hardware 2019年第2期1卷 136-162页

作者： Dangxiao WANG Yuan GUO Shiyi LIU Yuru ZHANG Weiliang XU Jing XIAO State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100083China Peng Cheng Laboratory Shenzhen 518055China Department of Mechanical Engineering University of AucklandAuckland 1142New Zealand School of Computer Science and the Robotics Engineering Program Worcester Polytechnic InstituteWorcesterMA 01609-2280USA

Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed.

关键词： Haptic display Virtual reality Desktop haptics Surface haptics Wearable haptics Multimodal haptics

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Opportunistic synthesis in reactive games under information asymmetry

arXiv

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arXiv 2019年

作者： Kulkarni, Abhishek N. Fu, Jie Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA01604 United States Department of Electrical and Computer Engineering Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA01604 United States

Reactive synthesis is a class of methods to construct a provably-correct control system, referred to as a robot, with respect to a temporal logic specification in the presence of a dynamic and uncontrollable environment. This is achieved by modeling the interaction between the robot and its environment as a two-player zero-sum game. However, existing reactive synthesis methods assume both players to have complete information, which is not the case in many strategic interactions. In this paper, we use a variant of hypergames to model the interaction between the robot and its environment;which has incomplete information about the specification of the robot. This model allows us to identify a subset of game states from where the robot can leverage the asymmetrical information to achieve a better outcome, which is not possible if both players have symmetrical and complete information. We then introduce a novel method of opportunistic synthesis by defining a Markov Decision Process (MDP) using the hypergame under temporal logic specifications. When the environment plays some stochastic strategy in its perceived sure-winning and sure-losing regions of the game, we show that by following the opportunistic strategy, the robot is ensured to only improve the outcome of the game—measured by satisfaction of sub-specifications—whenever an opportunity becomes available. We demonstrate the correctness and optimality of this method using a robot motion planning example in the presence of an adversary. Copyright © 2019, The Authors. All rights reserved.

关键词： Specifications

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Mobile Robot Localization with Reinforcement Learning Map Update Decision aided by an Absolute Indoor Positioning System

Mobile Robot Localization with Reinforcement Learning Map Up...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luís Garrote Miguel Torres Tiago Barros João Perdiz Cristiano Premebida Urbano J. Nunes Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Dept. of Aeronautical and Automotive Engineering Loughborough University UK

This paper introduces a new mobile robot localization solution consisting of two main modules: a Particle-Filter based Localization (PFL) and a Reinforcement-Learning based map updating, integrating relative measurements and absolute indoor positioning sensor (A-IPS) data. Concerning localization using 2D-LiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate A-IPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random sampling-based nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.

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Measuring hand use in the home after cervical spinal cord injury using egocentric video

arXiv

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arXiv 2022年

作者： Bandini, Andrea Dousty, Mehdy Hitzig, Sander L. Craven, B. Catharine Kalsi-Ryan, Sukhvinder Zariffa, José KITE Research Institute Toronto Rehabilitation Institute University Health Network Canada The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Institute of Biomedical Engineering University of Toronto TorontoON Canada Rehabilitation Sciences Institute Temerty Faculty of Medicine University of Toronto TorontoON Canada St. John's Rehab Research Program Sunnybrook Research Institute Sunnybrook Health Sciences Centre TorontoON Canada Department of Occupational Science & Occupational Therapy Temerty Faculty of Medicine University of Toronto TorontoON Canada Brain and Spinal Cord Rehabilitation Program Toronto Rehabilitation Institute University Health Network TorontoON Canada Division of Physical Medicine and Rehabilitation Department of Medicine Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Physical Therapy University of Toronto TorontoON Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. The aim of this study was to develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 hours of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc);the average duration of individual interactions (Dur);the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal Cord Independent Measure - SCIM). Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use. © 2022, CC BY-NC-ND.

关键词： Video recording

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Perovskite superlattices with efficient carrier dynamics

Research Square

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Research Square 2021年

作者： Xu, Sheng Lei, Yusheng Li, Yuheng Lu, Chengchangfeng Yan, Qizhang Gong, Huaxin Zhang, Song Zhou, Jiayun Zhang, Ruiqi Chen, Yimu Tsai, Hsinhan Gu, Yue Hu, Hongjie Lo, Yu-Hwa Nie, Wanyi Lee, Taeyoon Luo, Jian Yang, Kesong Jang, Kyung-In Department of Nanoengineering University of California San Diego La Jolla CA92093-0448 United States Department of Chemical Engineering Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Material Science and Engineering Program University of California San Diego La Jolla CA92093-0418 United States Los Alamos National Laboratory Los AlamosNM87545 United States School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Department of Bio and Brain Engineering Korea Institute of Science and Technology Korea Republic of Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu42988 Korea Republic of Department of Bioengineering University of California San Diego La Jolla CA92093-0412 United States

Compared with their three-dimensional counterparts, low-dimensional metal halide perovskites with periodic inorganic/organic structures have shown promising stability and hysteresis-free electrical performance, which paves the way for next-generation optoelectronic devices. However, when integrated in devices, they have relatively limited efficiencies because devices usually require carrier transport through the film thickness direction. In conventionally grown single crystals, the carrier transport in the thickness direction is hindered by the insulating organic spacers. In addition, the strong quantum confinement from the organic spacers limits the generation and transport of free carriers. The carrier dynamics is further compromised by the presence of grain boundaries in polycrystals. Here, we report a low-dimensional metal halide perovskite superlattice with efficient carrier transport in three dimensions by epitaxial growth. Epitaxy on a slightly lattice-mismatched substrate compresses the organic spacers in the superlattice, which weakens the quantum confinement and further improves carrier dynamics. The performance of a low-dimensional perovskite superlattice solar cell has been certified under the quasi-steady state for the first time. Moreover, the device shows an unusually high open-circuit voltage, due to a unique intra-band exciton relaxation mechanism. © 2021, CC BY.

关键词： Grain boundaries

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Intelligent automatic V6 and V8 engine sound detection based on artificial neural network 1

Intelligent automatic V6 and V8 engine sound detection based...

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1st International Conference on automotive, Manufacturing, and Mechanical engineering, IC-AMME 2018

作者： Vincent, Wenny Winda, Astuti Solihin, Mahmud Iwan Automotive and Robotics Engineering Department BINUS ASO School of Engineering Bina Nusantara University Alam Sutera Boulevard no. 1 Alam Sutera-Serpong Tangerang-Banten15325 Indonesia Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia No. 1 Jalan Menara Gading Cheras Kuala Lumpur56000 Malaysia

The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively. © 2019 The Authors, published by EDP Sciences.

关键词： Support vector machines

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3D Printed 6-Axis Collaborative Arm Robot Using Force Limiting Feature for Service Robot

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Raymond Saerang David Wiliem Feraldy Tinggogoy Deddi Carles M. Dani Agri Suwandi Endra Oey Sofyan Tan Automotive and Robotics EngineeringProgram Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480

Collaborative arm robot is a robot that can work alongside humans. By using force limiting (FL) feature, where the force of the arm robot is limited and controlled, the robot can sense collision and stop its movement when it occurs. The idea is to apply the mentioned concept for service purposes. In this research a 6-axis arm robot with collision detection is developed. The design of the arm robot adopted the design of Universal Robot's UR3 with each joint capable of 360° rotation. The design approach is chosen for its flexibility to work in tight space with a maximum reach of half meter, as it is intended to work closely with human. This initial version robot is not intended to carry heavy load or move at high precision. With a maximum load of one Kg, this arm robot targets numerous applications that help human to do complex repetitive tasks, extend the reach of human operators, and help humans on many daily chores that usually does not involve heavy loads. The whole frame, which also serve as the cover of the robot, was made by 3D printing with multiple materials that fit to the strength and temperature requirement at each part of the robot. In order to detect collision, the arm robot employed smart servos at each joint with the ability to detect overcurrent and overvoltage, which allow the implementation of force limiting (FL) feature that will stop the robot in case of collision. The resulting arm robot managed to lift1 kg load and able stop its movement in the event of collision. Future research to improve the movement precision of the arm robot by adaptive methods is promising.

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automotive start-stop engine based on face recognition system 1

Automotive start-stop engine based on face recognition syste...

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1st International Conference on automotive, Manufacturing, and Mechanical engineering, IC-AMME 2018

作者： William, Lim Winda, Astuti Satrio, Dewanto Sofyan, Tan Solihin, Mahmud Iwan Automotive and Robotics Engineering Department BINUS ASO School of Engineering Bina Nusantara University l. Alam Sutera Boulevard no.1 Alam Sutera-Serpong Tangerang-Banten15325 Indonesia Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia

There are so many security devices such as code pin, dual control procedures, and ID card. However, those devices have the potential to be lost, stolen or duplicated by someone. Due to that reason, the authentication for driver by using their face is one of the potential solutions. In this proposed system, the real time person's face is detected using a camera. The detected face is then processed inthe system which will recognize. The recognized face is then used as input to the Arduino, which connected to the automotive relay to active the engine's starter vehicle. Viola-Jones method is applied as a method to detect and crop face area of the face image. The Canny edge method is applied as a method for segmenting the detected face. The Canny edge method will detect a wide range of edges from facealso reducing noise image. Fast Fourier Transform is applied as a feature extraction technique to extract the segmented face image. The extracted image is then used as input to the Artificial Neural Network (ANN) in order to recognize the face of the authorize person. Experimental results show the training and testing accuracy of 100 % and 100 %, respectively. © 2019 The Authors, published by EDP Sciences.

关键词： Support vector machines

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