scholarly journals A Generic Interface Enabling Combinations of State-of-the-Art Path Planning and Tracking Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 788
Author(s):  
Johannes Rumetshofer ◽  
Michael Stolz ◽  
Daniel Watzenig
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Tracking Error ◽  
Building Blocks ◽  
Software Components ◽  
Automated Driving ◽  
Internal Reference ◽  
Driving Function ◽  
Tracking Algorithms ◽  
Path Representation

In the development of Level 4 automated driving functions, very specific, but diverse, requirements with respect to the operational design domain have to be considered. In order to accelerate this development, it is advantageous to combine dedicated state-of-the-art software components, as building blocks in modular automated driving function architectures, instead of developing special solutions from scratch. However, e.g., in local motion planning and control, the combination of components is still limited in practice, due to necessary interface alignments, which might yield sub-optimal solutions and additional development overhead. The application of generic interfaces, which manage the data transfer between the software components, has the potential to avoid these drawbacks and hence, to further boost this development approach. This publication contributes such a generic interface concept between the local path planning and path tracking systems. The crucial point is a generalization of the lateral tracking error computation, based on an introduced error classification. It substantiates the integration of an internal reference path representation into the interface, to resolve the component interdependencies. The resulting, proposed interface enables arbitrary combinations of components from a comprehensive set of state-of-the-art path planning and tracking algorithms. Two interface implementations are finally applied in an exemplary automated driving function assembly task.

scholarly journals Design of True Random Number Circuit with Controllable Frequency

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1517
Author(s):  
Xinsheng Wang ◽  
Xiyue Wang
Keyword(s):  
Random Number ◽  
Noise Source ◽  
State Of The Art ◽  
Building Blocks ◽  
Encryption Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Random Telegraph Noise ◽  
Telegraph Noise ◽  
Novel Method

True random number generators (TRNGs) have been a research hotspot due to secure encryption algorithm requirements. Therefore, such circuits are necessary building blocks in state-of-the-art security controllers. In this paper, a TRNG based on random telegraph noise (RTN) with a controllable rate is proposed. A novel method of noise array circuits is presented, which consists of digital decoder circuits and RTN noise circuits. The frequency of generating random numbers is controlled by the speed of selecting different gating signals. The results of simulation show that the array circuits consist of 64 noise source circuits that can generate random numbers by a frequency from 1 kHz to 16 kHz.

An experimental study on feature-based SLAM for multi-legged robots with RGB-D sensors

2017 ◽  
Vol 44 (4) ◽  
pp. 428-441 ◽  
Author(s):  
Michał R. Nowicki ◽  
Dominik Belter ◽  
Aleksander Kostusiak ◽  
Petr Cížek ◽  
Jan Faigl ◽  
...  
Keyword(s):  
Performance Metrics ◽  
State Of The Art ◽  
Building Blocks ◽  
Bundle Adjustment ◽  
The State ◽  
Legged Robots ◽  
Evaluation Methodology ◽  
Walking Robots ◽  
Content Type ◽  
Feature Based

Purpose This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics. Design/methodology/approach Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process. Findings The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory. Research limitations/implications The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research. Practical implications The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining. Originality/value The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

scholarly journals Composition, Separation of Roles and Model-Driven Approaches as Enabler of a Robotics Software Ecosystem

2020 ◽  
pp. 53-108
Author(s):  
Christian Schlegel ◽  
Alex Lotz ◽  
Matthias Lutz ◽  
Dennis Stampfer
Keyword(s):  
Complex Systems ◽  
Open Source ◽  
Building Block ◽  
Building Blocks ◽  
Software Systems ◽  
Software Components ◽  
Separation Of Concerns ◽  
Software Ecosystem ◽  
Model Driven ◽  
Successful Approach

AbstractSuccessful engineering principles for building software systems rely on the separation of concerns for mastering complexity. However, just working on different concerns of a system in a collaborative way is not good enough for economically feasible tailored solutions. A successful approach for this is the composition of complex systems out of commodity building blocks. These come as is and can be represented as blocks with ports via data sheets. Data sheets are models and allow a proper selection and configuration as well as the prediction of the behavior of a building block in a specific context. This chapter explains how model-driven approaches can be used to support separation of roles and composition for robotics software systems. The models, open-source tools, open-source robotics software components and fully deployable robotics software systems shape a robotics software ecosystem.

scholarly journals Regarding Jump Point Search and Subgoal Graphs

2019 ◽  
Author(s):  
Daniel D. Harabor ◽  
Tansel Uras ◽  
Peter J. Stuckey ◽  
Sven Koenig
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Jump Point ◽  
Point Search ◽  
Planning Technique ◽  
First Time ◽  
Grid Based

In this paper, we define Jump Point Graphs (JP), a preprocessing-based path-planning technique similar to Subgoal Graphs (SG). JP allows for the first time the combination of Jump Point Search style pruning in the context of abstraction-based speedup techniques, such as Contraction Hierarchies. We compare JP with SG and its variants and report new state-of-the-art results for grid-based pathfinding.

scholarly journals A Cyber-Physical Gaming System for Vocational Training

2017 ◽  
Author(s):  
Aparajithan Sivanathan ◽  
Scott Mcgibbon ◽  
Theodore Lim ◽  
James Ritchie ◽  
Mohamed Abdel-Wahab
Keyword(s):  
Virtual Environments ◽  
Vocational Training ◽  
Building Blocks ◽  
Vocational Education And Training ◽  
Stone Masonry ◽  
Software Components ◽  
Physical Systems ◽  
Effective Training ◽  
Game Environment ◽  
And Training

Cyber-physical systems enable new digital ecologies in industrial and workplace lifelong learning. This paper reports on early efforts in delivering a virtual environment and system for vocational education and training (VET), in particular targeting the needs of craft and trade skills. The domain of stone masonry is presented herein, where its underpinning activities are learning through virtual environments, simulation and role play. The challenges are not only the synchronicity between physical and software components but also the in-game mechanics that incorporate building blocks of effective training and skills development strategies. A prototype body-area sensor network in a cyber-physical game environment demonstrates the interaction between virtual objects and the player-learner.

scholarly journals Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

2020 ◽  
Vol 10 (20) ◽  
pp. 7201
Author(s):  
Xiao-Xia Yin ◽  
Lihua Yin ◽  
Sillas Hadjiloucas
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Information Fusion ◽  
State Of The Art ◽  
General Structure ◽  
Building Blocks ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Dce Mri ◽  
Computer Aided

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

scholarly journals An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme

2019 ◽  
Vol 2 (2) ◽  
pp. 67-77
Author(s):  
Wei Xue ◽  
Rencheng Zheng ◽  
Bo Yang ◽  
Zheng Wang ◽  
Tsutomu Kaizuka ◽  
...  
Keyword(s):  
Path Planning ◽  
Human Error ◽  
Driving Safety ◽  
Planning Problem ◽  
Adaptive Model ◽  
Sensor Failure ◽  
Automated Driving ◽  
Content Type ◽  
Automated Vehicle ◽  
Virtual Vehicle

Purpose Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction. Design/methodology/approach Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes. Findings It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios. Originality/value This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

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