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IEEE International Conference on Systems, Man and Cybernetics

作者： F.J. Toledo J.D. Luis L.M. Tomas M.A. Zamora H. Martinez Department of Computer Science and Elect University de Murcia Murcia Spain Vision and Robotics Group University of Murcia Murcia Spain

Map building and position estimation are basic tasks in mobile robot navigation with path planning. A method to generate a global map of the vehicle work environment using ultrasonic sensors is developed in this paper. Depending on the physical properties of the walls that form the room where the robot is navigating, sonar sensors show different behaviours. A neural network is utilized to interpret the range readings of ultrasonic sensors in the different environments. A local map composed of squared cells is formed through the neural network that gives the occupancy probabilities for each cell. Finally, a global map is built achieving integration of different views of the environment using Bayes' rule. Results of the method implementation in the construction in a specular environment as well as in rough wall environments are shown in this paper.

关键词： Indoor environments Neural networks Robot sensing systems Reflection Mobile robots Sonar navigation Path planning Robustness Testing Sonar measurements

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***: Transforming Colour into Melody and Implementing the Result in a Colour Sensor Device

***: Transforming Colour into Melody and Implementing the Re...

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International Conference on Visualisation, VIS

作者： Jessica Rossi Francisco Jose Perales Javier Varona Miquel Roca Computer Graphics Vision and Artificial Intelligence Group Mathematics and Computers Science Department University of Balearic Islands Spain

This paper represents a system which explores the reality of colour in relation to sound and dedicated firstly to blind people, the challenges they live with, their psychology and their emotions. Itpsilas an exploration of the infinite range of colours that they have either never been able to see or that they have lost the possibility to see. The system developed is based on the concept of synesthesia: a dasialiaisonpsila between colour and sound, colour that becomes sound. This paper synthesizes a wider range of study regarding people with diverse psychological and movement conditions. It represents a new method of expression and of self development through music therapy and game, dedicated mainly to children aged between 5 and 13. Finally we conclude with a portable prototype device that has been validated and patented. It is capable of detecting colour and transforming it into sound: a colour sensor identifies colours and transforms this data into musical notes. The prototype is totally adaptable to the requirements of the end user meaning that the user can choose the best combination between colour and sound in order to improve their communication with the environment. We are now developing a mathematical theoretical background in order to formalize the correspondence between colour and musical space.

关键词： Psychology Acoustic sensors Prototypes Cultural differences Auditory system vision defects Visualization intelligent sensors Sensor systems computer graphics

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Online discovery of AUV control policies to overcome thruster failures

Online discovery of AUV control policies to overcome thruste...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Seyed Reza Ahmadzadeh Matteo Leonetti Arnau Carrera Marc Carreras Petar Kormushev Darwin G. Caldwell Department of Advanced Robotics Istituto Italiano di Tecnologia Genova Italy Department of Computer Science The University of Texas at Austin Austin TX Computer Vision and Robotics Group (VICOROB) University of Girona Girona Spain

We investigate methods to improve fault-tolerance of Autonomous Underwater Vehicles (AUVs) to increase their reliability and persistent autonomy. We propose a learning-based approach that is able to discover new control policies to overcome thruster failures as they happen. The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the AUV. The model is adapted to a new condition when a fault is detected and isolated. Since the approach generates an optimal trajectory, the learned fault-tolerant policy is able to navigate the AUV towards a specified target with minimum cost. Finally, the learned policy is executed on the real robot in a closed-loop using the state feedback of the AUV. Unlike most existing methods which rely on the redundancy of thrusters, our approach is also applicable when the AUV becomes under-actuated in the presence of a fault. To validate the feasibility and efficiency of the presented approach, we evaluate it with three learning algorithms and three policy representations with increasing complexity. The proposed method is tested on a real AUV, Girona500.

关键词： Optimization Trajectory Vectors Vehicles Robots Fault tolerance Fault tolerant systems

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haozhe Ma, Liyan Ying, Shihui Institute of Artificial Intelligence Shanghai University Shanghai200444 China School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Department of Mathematics School of Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350368604

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects. © 2024 IEEE.

关键词： Active learning

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Similarity-Based Clustering 1

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丛书名： Lecture Notes in computer science

1000年

作者： Michael Biehl Barbara Hammer Michel Verleysen Thomas Villmann

ISBN: (数字)9783642018053

ISBN: (纸本)9783642018046

Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standingofbiologicalsystems,reliablediagnosisoffaults,andtherapyofdiseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the speci?cs of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.

关键词： Life sciences, general Computational Biology/Bioinformatics Biomedicine general Data Mining and Knowledge Discovery Information Storage and Retrieval computer Imaging, vision, Pattern Recognition and Graphics

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Bézier curve based dynamic obstacle avoidance and trajectory learning for autonomous mobile robots

Bézier curve based dynamic obstacle avoidance and trajector...

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2010 10th International Conference on intelligent Systems Design and Applications, ISDA'10

作者： Chaudhry, Tayyab Gulrez, Tauseef Zia, Ali Zaheer, Shyba Intelligent Machines and Robotics Research Group Department of Computer Science COMSATS Institute of Information Technology Lahore M.A. Jinnah Campus Defence Road Lahore Pakistan Department of Electrical Engineering College of Engineering and Applied Sciences Al Ghurair University Dubai United Arab Emirates

ISBN: (纸本)9781424481354

This paper addresses the problem of avoiding dynamic obstacles while following the learned trajectory through non-point based maps directly through laser data. The geometric representation of free configuration area changes while a moving obstacle enters into the safety region of autonomous mobile robot. We have applied the Bézier curve properties to the free configuration eigenspaces to satisfy the dynamic obstacle avoidance path constraints. The algorithm is designed to accurately represent the mobile robot's characteristics while avoiding obstacle such as minimum turning radius. Moreover, we also discuss the obstacle avoided path feasibility as a vectorial combination of free configuration eigen-vectors at discrete time scan-frames to manifest a trajectory, which once followed and mapped onto the two control signals of mobile robot will enable it to build an efficient and accurate online environment map. Preliminary results in Matlab have been shown to validate the idea, while the same has been implemented in Player/stage (robotics real-time software) to analyze the performance of the proposed system. © 2010 IEEE.

关键词： Trajectories

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Process Model Parameterisation in Posegraphs

Process Model Parameterisation in Posegraphs

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American Control Conference

作者： Simon J. Julier Zhaojie Ju Department of Computer Science University College London UK S Intelligent Systems and Biomedical Robotics Group at the University of Portsmouth UK

ISBN: (纸本)9781479901777

Through propagating information over time, process models serve a vital in any multi-time step estimation algorithm. However, they can introduce nonlinearities which can significantly degrade the performance of an estimator. In this paper, we investigate the impact of the parameter-isation of the process model in posegraph-based formulations of filtering and estimation algorithms. Exploiting the flexibility and conditional independence structure of a posegraph, we develop two formulations of a process noise model of a vehicle - one in Euclidean space, the other in polar space. Using moment-matching, we develop exact closed form solutions for the first two moments of a Gaussian distribution propagated through both models. We analyse the effects of both formulations in the context of a Simultaneous Localisation and Mapping (SLAM) problem. We show that, by representing the "arc-like" nature of the prediction error more accurately, the polar form is more accurate, more robust, and is less computationally expensive than the Euclidean form.

关键词： process prediction Euclidean Euclidean procedural model parameterization Gaussian distribution simultaneous localization and mapping Conditional Independence Euclidean space Error of prediction nonlinearity Filtration

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Deep Learning and computer vision Techniques for Estimating Snow Coverage on Roads using Surveillance Cameras

Deep Learning and Computer Vision Techniques for Estimating ...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： François-Guillaume Landry Moulay A. Akhloufi Department of Computer Science Perception Robotics and Intelligent Machines Research Group (PRIME) Université de Moncton Moncton NB Canada

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Road surface monitoring in winter conditions is of great importance to ensure the safety of road users. Estimation of snow coverage on roads can be included in intelligent transportation systems to alert drivers or improve snow removal processes. Several models have been proposed for estimating snow coverage using surveillance cameras, but these models have focused on predicting few snow levels, which limits their usefulness in practice. In this paper, we present a model that allows a more granular estimation of the percentage of road surface covered by snow by predicting snow coverage from 0% (no snow) to 100% (fully snow-covered) using increments of 10%. We propose an ensemble learning model combining a deep convolutional neural network (CNN) and a support-vector machine (SVM). The accuracy of our model is similar to the state-of-the-art accuracy despite the higher task complexity associated with the increased granularity of predictions.

关键词： Support vector machines Image segmentation Snow Computational modeling Roads Surveillance Predictive models

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Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments

arXiv

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arXiv 2023年

作者： Bukhari, Syed Tanweer Shah Anima, Bashira Akter Feil-Seifer, David Qazi, Wajahat Mahmood Cognitive Robotics Group Faculty of IT and CS University of Central Punjab Lahore Pakistan Intelligent Machines & Robotics Group Department of Computer Science COMSATS University Islamabad Lahore Campus Pakistan Socially Assistive Robotics Group Department of Computer Science & Engineering University of Nevada Reno RenoNV89557 United States

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying "what to do" in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make "tea" and "sandwich". We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says "I am thirsty" or "It is cold outside" the robot will start to perform the tea-making skill. In contrast, if the person says, "I am hungry" or "I need something to eat", the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill. © 2023, CC BY.

关键词： Anthropomorphic robots

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Gait Analysis and Control of Rimless Wheel Rovers for Planetary Exploration

Gait Analysis and Control of Rimless Wheel Rovers for Planet...

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Space robotics (iSpaRo), International Conference on

作者： Dennis Schlee Shubham Vyas Rauĺ Domínguez Frank Kirchner DFKI GmbH - Robotics Innovation Center Bremen Germany Intelligent Autonomous Systems Group Technical University of Darmstadt Darmstadt Germany Department of Mathematics and Computer Science AG Robotics University of Bremen Bremen Germany

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

Rimless wheeled robots are a hybrid variant of traditionally wheeled and legged robots. These hybrid systems combine the benefits of both approaches, such as power efficiency and good terrain traversability. However, the simplicity of the design of the rimless wheel has the disadvantage of limiting the flexibility of foot placement and the impacts caused on the body when navigating rigid surfaces. In this work, we focus on analyzing the effects of different gaits on the robot’s center body. For that, a novel gait classification approach for a rimless wheeled system is introduced and the effects of selected gaits are evaluated in a simulation using a simplified robot model. The analysis of simulation results shows that the chosen gait has a strong effect on the vertical motion of the robot’s center body.

关键词： Legged locomotion Limiting Navigation Simulation Wheels Aerospace electronics Hybrid power systems

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