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IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance Systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

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A Miniaturised Camera-based Multi-Modal Tactile Sensor

A Miniaturised Camera-based Multi-Modal Tactile Sensor

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IEEE International Conference on robotics and Automation (ICRA)

作者： Kaspar Althoefer Yonggen Ling Wanlin Li Xinyuan Qian Wang Wei Lee Peng Qi The Centre for Advanced Robotics @ Queen Mary (ARQ) Queen Mary University of London London United Kingdom Tencent Robotics X Lab Shenzhen China Beijing Institute for General Artificial Intelligence (BIGAI) Beijing China The Department of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China

In conjunction with huge recent progress in cam-era and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information.

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Study on the Impact of Situational Explanations and Prior Information Given to Users on Trust and Perceived Intelligence in Autonomous Driving in a Video-based 2x2 Design

Study on the Impact of Situational Explanations and Prior In...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Kolja Kühnlenz Barbara Kühnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

In this paper, results from a video-based study on the influence of prior information given to users and explanations situationally given by the vehicle itself on trust and perceived intelligence are presented using a simulated autonomous vehicle in an ambiguous driving situation. A 2x2 between-subjects design is chosen with two independent variables ‘prior information’ (extended/short) and ‘explanations’ (yes/no) with users pseudo-randomly assigned to one of the four conditions. Significant results from 189 test persons reveal, that trust depends on how the capabilities of the intelligent vehicle are explained a priori and not on situational explanations, while perceived intelligence is influenced by both variables. Additional interactions of prior information and user gender is noted with respect to perceived intelligence. As one side effect, it is found, that male users felt significantly more safe than female users with also higher ratings of intention to use the vehicle independently of given information and explanations. Another side effect is that situational explanations lead to better ratings of subjective performance, while also here a significant interaction of gender and prior information is noted. Thus, contrary to expectations, a dominant role of continuous situational explanations (Explainable AI) of the intelligent vehicle for increasing trust is not confirmed and the extent of given prior information seems the deciding factor for initial trust building, which is an important aspect for the introduction of new intelligent technology into society. This is remarkable as at the same time perceived intelligence seems to be dependent on both variables. So it appears, that a vehicle being able to explain its actions may well appear more intelligent, but not necessarily appear more trustworthy.

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Quantum Exploration-based Reinforcement Learning for Efficient Robot Path Planning in Sparse-Reward Environment

Quantum Exploration-based Reinforcement Learning for Efficie...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Chao Huang Yibei Guo Zhihui Zhu Mei Si Daniel Blankenberg Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Ohio State University Columbus OH USA Department of Cognitive Science Rensselaer Polytechnic Institute Troy NY USA Center for Computational Life Sciences Cleveland Clinic Cleveland OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

With the latest developments in sensors, battery, and Artificial Intelligence (AI) technologies, robots can perform missions in unstructured environments (e.g., disaster sites). However, their adaptation speed is still slow due to their limited onboard computing capability and dynamic disruption from environments, leading to inaccurate planning and delayed emergency reactions. Even though reinforcement learning increases robots’ exploration speed by fusing effective guidance, the numerous interactions make the learning time-consuming and even risky (e.g., collisions). To fundamentally improve robot adaptation speed, this work seeks help from quantum power. A novel Quantum Exploration based Dreamer model (QED) was developed to facilitate reinforcement learning explorations. QED based on stochastic quantum walker quickly explores environments, evaluates action quality, and obtains a global optimal exploration strategy; then, these high-quality exploration samples will be used to facilitate reinforcement learning speed. A theoretical benefit of QED is facilitating reinforcement learning speed without changing the underlying learning architecture, which makes the proposed QED applicable to general robot learning scenarios. To validate QED effectiveness, a robot path-planning task in an obstacle-dense environment was designed. The number of needed training episodes validated the effectiveness. The results show that QED achieves around ten times faster policy learning compared with Monte Carlo tree-based reinforcement learning methods and vanilla reinforcement learning methods.

关键词： Training Quantum mechanics Stochastic processes Reinforcement learning Robot sensing systems Robot learning Collision avoidance Artificial intelligence Quantum circuit Robots

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AirLine: Efficient Learnable Line Detection with Local Edge Voting

arXiv

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arXiv 2023年

作者： Lin, Xiao Wang, Chen College of Computing Georgia Institute of Technology AtlantaGA30332 United States Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-level precision, yet with a 3 − 80× runtime acceleration compared to other learning-based methods, which is critical for low-power robots. Copyright © 2023, The Authors. All rights reserved.

关键词： robotics

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Robust Model Predictive Control with Control Barrier Functions for Autonomous Surface Vessels

Robust Model Predictive Control with Control Barrier Functio...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Wei Wang Wei Xiao Alejandro Gonzalez-Garcia Jan Swevers Carlo Ratti Daniela Rus Computer Science and Artificial Intelligence Lab (CSAIL) Massachusetts Institute of Technology Cambridge MA USA SENSEable City Laboratory Massachusetts Institute of Technology Cambridge MA USA Marine Robotics Lab Department of Mechanical Engineering College of Engineering University of Wisconsin-Madison Madison WI USA Department of Mechanical Engineering MECO Research Team KU Leuven Belgium Flanders Make@KU Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In autonomous robot navigation, the trajectories from path planners are considered to be safe regions, and deviations could endanger vessels. Model Predictive Control (MPC) stands as a popular choice for trajectory tracking problems as it naturally addresses operational constraints, such as dynamics and control constraints. Nevertheless, achieving robustness in changing environments like oceans and rivers, which are constantly subject to significant external disturbances, remains an ongoing challenge for MPC. It must consistently keep the system within a predefined safe region (such as a reference trajectory) even in the presence of model inaccuracies and perturbations. To address this challenge, we present a robust model predictive control strategy utilizing Control Barrier Functions (CBFs), which increases the disturbance-rejection abilities. We verify our method on an autonomous surface vessel in simulation and natural waters, both with external disturbances. Specifically, compared with the traditional MPC method, our proposed MPC-CBF strategy reduces tracking errors by 17.82% and 40.26% in simulations and field experiments, respectively. Although the control effort slightly increases by 7.78% and 4.20%, respectively, these results clearly demonstrate the enhanced resilience of MPC-CBF to disturbances.

关键词： Sea surface Trajectory tracking Navigation Perturbation methods Robustness Trajectory Rivers

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Adaptive feature extraction in EEG-based motor imagery BCI: Tracking mental fatigue

Adaptive feature extraction in EEG-based motor imagery BCI: ...

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作者： Talukdar, Upasana Hazarika, Shyamanta M. Gan, John Q. Biomimetic and Cognitive Robotics Lab Department of Computer Science and Engineering Tezpur University Tezpur India Mechatronics and Robotics Lab Department of Mechanical Engineering Indian Institute of Technology Guwahati India School of Computer Science and Electronic Engineering University of Essex United Kingdom

Objective. Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state. In this paper, we put forward an unsupervised adaptive scheme to adapt the feature extractor of motor imagery (MI) BCIs by tracking the fatigue level of the user. Approach. Eleven subjects participated in the study during which they accomplished MI tasks while self-reporting their perceived levels of mental fatigue. Out of the 11 subjects, only six completed the whole experiment, while the others quit in the middle because of experiencing high fatigue. The adaptive feature extractor is attained through the adaptation of the common spatial patterns (CSP), one of the most popular feature extraction algorithms in EEG-based BCIs. The proposed method was analyzed in two ways: offline and in near real-time. The separability of the MI EEG features extracted by the proposed adaptive CSP (ADCSP) has been compared with that by the conventional CSP (C-CSP) and another CSP based adaptive method (ACSP) in terms of: Davies Bouldin index (DBI), Fisher score (FS) and Dunn's index (DI). Main results. Experimental results show significant improvement in the separability of MI EEG features extracted by ADCSP as compared to that by C-CSP and ACSP. Significance. Collectively, the results of the experiments in this study suggest that adapting CSP based on mental fatigue can improve the class separability of MI EEG features. © 2020 IOP Publishing Ltd.

关键词： Electroencephalography

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Single Frame Lidar-Camera Calibration Using Registration of 3D Planes

Single Frame Lidar-Camera Calibration Using Registration of ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Ashutosh Singandhupe Hung Manh La Quang Phuc Ha Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA School of Electrical and Data Engineering University of Technology Sydney Australia

ISBN: (纸本)9781665472616

This work focuses on finding the extrinsic parameters (rotation and translation) between Lidar and an RGB camera sensor. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The plane coefficients extracted from the sensor’s data are used to construct a well-structured set of 3D points. These 3D points are then ’aligned,’ which gives the relative transformation between the two sensors. We use our proposed Correntropy Similarity Matrix Iterative Closest Point (CoSMICP) Algorithm to estimate the relative transformation. This work uses a single frame of the point cloud data acquired from the Lidar sensor and a single frame from the calibrated camera data to perform this operation. From the camera image, we use the projection of the calibration target’s corner points to compute the 3D points, and along the process, we calculate the 3D plane equation using the corner points. We evaluate our approach on a simulated dataset with complex environment settings, making use of the freedom to assess under multiple configurations. Through the obtained results, we verify our method under various configurations.

关键词： Point cloud compression Three-dimensional displays Laser radar Transmission line matrix methods Robot vision systems Cameras Real-time systems

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Effective Searching for the Honeybee Queen in a Living Colony

Effective Searching for the Honeybee Queen in a Living Colon...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Jan Blaha Jan Mikula Tomáš Vintr Jiří Janota Jiří Ulrich Tomáš Rouček Fatemeh Rekabi-Bana Laurenz Alexander Fedotoff Martin Stefanec Thomas Schmickl Farshad Arvin Miroslav Kulich Tomáš Krajník Faculty of Electrical Engineering Czech Technical University in Prague Czechia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czechia Computer Science Department Durham University United Kingdom Department of Zoology Artificial Life Lab Institute of Biology University of Graz Graz Austria

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Despite the importance of honeybees as pollinators for the entire ecosystem and their recent decline threatening agricultural production, the dynamics of the living colony are not well understood. In our EU H2020 RoboRoyale project, we aim to support the pollination activity of the honeybees through robots interacting with the core element of the honeybee colony, the honeybee queen. In order to achieve that, we need to understand how the honeybee queen behaves and interacts with the surrounding worker bees. To gather the necessary data, we observe the queen with a moving camera, and occasionally, we instruct the system to perform selective observations elsewhere. In this paper, we deal with the problem of searching for the honeybee queen inside a living colony. We demonstrate that combining spatio-temporal models of queen presence with efficient search methods significantly decreases the time required to find her. This will minimize the chance of missing interesting data on the infrequent behaviors or queen-worker interactions, leading to a better understanding of the queen’s behavior over time. Moreover, a faster search for the queen allows the robot to leave her more frequently and gather more data in other areas of the honeybee colony.

关键词： computer aided software engineering Automation Biological system modeling Ecosystems Robot vision systems Production Search problems Cameras Path planning Robots

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