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Guest editorial emerging circuits and systems techniques for ultra-low power body sensor networks

IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2012年第1期2卷 1-3页

作者： Calhoun, Benton Chandrakasan, Anantha P. Otis, Brian Verma, Naveen Yoo, Hoi-Jun Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 United States Department of Electrical Engineering University of Washington Seattle WA 98195 United States Department of Electrical Engineering Princeton University Princeton NJ 08540 United States Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 08540 Korea Republic of

This special issue brings together 11 papers on approaches spanning circuits, architectures, algorithms, and system design that demonstrates promising solutions to emerging critical problems in body sensor networks.

关键词： Special issues and sections Low-power electronics Body sensor networks

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A Delay-tolerant, Potential field-based, Network Implementation of an Integrated Navigation System

A Delay-tolerant, Potential field-based, Network Implementat...

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2006 IEEE/RSJ International Conference on intelligent robots and Systems

作者： Rachana Ashok Gupta Ahmad A Masoud Mo-yuen Chow Electrical and Computer Engineering Department North Carolina State University Raleigh NC USA Lectrical Engineering Department KFUPM Dhahran Saudi Arabia Electrical Engineering Department KFUPM Dhahran Saudi Arabia

intelligent space (iSpace) is a large scale mechatronics system. It is a multidisciplinary effort whose aim is to produce a network structure and components that are capable of integrating sensors, actuators, DSP, communication, and control algorithms in a manner that suits time-sensitive applications including real-time navigation and/or obstacle avoidance. There are many challenges that must be overcome in order to put such a distributed, heterogeneous system together. This paper deals with one of these issues, i.e. the adverse effect of network and processing delays on the system. Here a delay-resistant sensory motor module for navigating a differential drive unmanned ground vehicle (UGV) in a cluttered environment is suggested. The module consists of an early vision edge detection stage, a harmonic potential field (HPF) planner, a network based quadratic curve fitting controller and gain schedule middleware, (GSM). Though the different techniques used to implement the navigation system have been well-studied as independent modules, the contribution in this paper is the way all these different modules are integrated together for the first time to create an efficient structure for a network based integrated navigation system. The structure of this module and its components are described. Thorough experimental results along with performance assessment comparing the suggested structure to a previous implementation of iSpace are also provided

关键词： Navigation intelligent sensors Delay systems intelligent systems intelligent actuators Large-scale systems Mechatronics Digital signal processing Communication system control Delay effects

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Augmented virtuality based immersive telepresence for control of mining robots

Augmented virtuality based immersive telepresence for contro...

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International Symposium on Computational Intelligence and intelligent Informatics, ISCIII

作者： Karthik Mahesh Varadarajan Markus Vincze Automation and Control Institute (ACIN) Technical University of Vienna (TU Wien) Vienna Austria

Vast mineral resources of precious metals such as gold remain trapped and unexploited due to the lack of economical and practical means of exploration. This requires the development of alternate exploitation techniques. Mining robots form a significant alternative to convention mining techniques. However, there are several practical limitations that make such systems difficult to implement in practice. The primary hurdle in realizing such systems is the difficulty in tele-operating the robot under high latency conditions, which is typical of mining of environments. This is further compounded by poor representation of the environment, resulting in reduced situational awareness. The latency in tele-operation can be caused by numerous factors - system latency, compression scheme, communication protocols, constraints on bandwidth, channel contention, poor line of sight and display overhead. This is typically countered by reduction of frame rate or display resolution or quality. This further affects remote navigation of the robot. Non-holistic scene displays further degrade situational perception. This is intricately tied to the effectiveness of the Operator Control Unit (OCU). Besides, improvements in these capabilities without any vehicle intelligence do little in reducing the operator task-load. In this paper, we present the design of a novel augmented virtuality based visualization and operator interface unit along supported by vehicular intelligence, which are targeted at overcoming the above issues. These design considerations and presented algorithms are expected to form the foundation of next generation mining robots.

关键词： Cameras Robot vision systems Rendering (computer graphics) Robot kinematics Vehicles

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Particle filter localization and real time obstacle avoidance control based on 3D perception for wheelchair mobile robot

Particle filter localization and real time obstacle avoidanc...

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International IEEE Conference on intelligent Systems, IS

作者： Emna Baklouti Nader Ben Amor Mohamed Jallouli Ecole Nationale d'Ingénieur de Sfax Université de Sfax Tunisie Computer & Embedded Systems Laboratory (CES)

Obstacle detection and localization behaviours have shown to work robustly in 2D perceived environment. With the progress of proximity sensors, precisely the emergence of 3D vision techniques, it would be interesting to examine these motion controllers whether for 3D perceived environment. 2D basic algorithms for obstacle avoidance or robot location, needs reformulation in order to process data from 3D perception devices. In this work, we introduce a 3D obstacle detection controller, combined with particle filter localization. The obstacle detection control described in this paper address 3D obstacles with different shapes and the particle filter uses 3D environment data to correct the wheelchair localization based on his kinematics model.

关键词： Three-dimensional displays Wheelchairs robots Two dimensional displays Kinematics Collision avoidance Sensors

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Hy-Tech Shopping Cart Using Object Detection and Human Following Technology

Hy-Tech Shopping Cart Using Object Detection and Human Follo...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Anita Joshi Tanishq Thuse Prashant Thorve Raj Thakur Anshul Thote Isha Thakur Department Of Engineering Science and Humanities Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Recent advancements in artificial intelligence and robotics have transformed the retail industry, leading to innovative solutions that enhance the shopping experience. Our paper presents the High-Tech Shopping Cart, an intelligent system that leverages object detection and human-following technology to provide a seamless, hands-free shopping experience. The cart utilizes state-of-the-art object detection algorithms to identify and track items selected by shoppers while employing a robust human-following mechanism to navigate autonomously through stores. Our system integrates computer vision techniques, sensor fusion, and advanced path-planning algorithms within a modular framework, featuring cameras and depth sensors for real-time processing. Extensive experiments demonstrate high accuracy in object detection and reliable navigation in complex environments. User satisfaction surveys indicate a positive reception, highlighting the cart's potential to significantly enhance the shopping experience.

关键词： Surveys Navigation Service robots Object detection Sensor fusion Robot sensing systems Robustness Real-time systems Object recognition intelligent systems

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CAMAL: Context-aware multi-scale attention framework for lightweight visual place recognition

arXiv

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arXiv 2019年

作者： Khaliq, Ahmad Ehsan, Shoaib Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab in School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art performances for Visual Place Recognition (VPR). Their prestigious generalization power has played a vital role in identifying persistent image regions under changing conditions and viewpoints. However, against the computation intensive D-CNNs based VPR algorithms, lightweight VPR techniques are preferred for resource-constraints mobile robots. This paper presents a lightweight CNN-based VPR technique that captures multilayer context-aware attentions robust under changing environment and viewpoints. Evaluation of challenging benchmark datasets reveals better performance at low memory and resources utilization over state-of-the-art contemporary VPR methodologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Comparative Analysis of Reinforcement Learning techniques in Simulated Robotics Environments

Comparative Analysis of Reinforcement Learning Techniques in...

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IEEE International Conference of intelligent Robotic and Control Engineering (IRCE)

作者： Abey Yoseph M. Hassan Tanveer Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Department of AI and Data Science National University of Computer and Emerging Sciences Karachi Pakistan

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

Machine learning is a critical component of artificial intelligence and computer science that uses data and algorithms to emulate the way that humans learn and predict. Reinforcement learning, one of the many forms of machine learning, trains an agent through trial and error, with successful outcomes being rewarded and unsuccessful outcomes being penalized. The implementation of three reinforcement learning algorithms, Qlearning, Deep Q-Learning, and Double Deep Q-Learning, was tested using a series of ROS (Robot Operating System) packages in the Gazebo simulation environment. The Python openai_ros package and Gym libraries were used to input a robot into a configurable environment and test out the performance of a specific reinforcement learning algorithm. The agent’s action space was discretized into three specific actions, move forward, turn left, or turn right, that the robot could take. The reward or punishment was given after each step based on if the current action moved the robot towards the goal state or away from it. For each algorithm, the agent was first pre-trained on an obstacle free environment for 200 episodes and then was placed in an environment with two box-shaped obstacles to avoid. The algorithm/neural network parameters from the pre-training environment were loaded prior to the agent being tested in the new environment. The goal state at each episode was randomly generated and the robot’s performance was evaluated using the cumulative reward achieved per episode and the number of times it reached the goal.

关键词： Training Measurement Q-learning Machine learning algorithms Operating systems Robot vision systems Turning Prediction algorithms Libraries robots

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A New Landmark and Sensor Selection Method for Vehicle Localization and Guidance

A New Landmark and Sensor Selection Method for Vehicle Local...

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IEEE Symposium on intelligent Vehicle

作者： Cedric Tessier Michel Berducat Roland Chapuis Frederic Chausse CEMA GREF Aubiere France LASMEA - UMR 6602 24 avenue des Landais 63177 Aubière France. e-mail: chapuis@lasmea.univ-bpclermont.fr LASMEA - UMR 6602 24 avenue des Landais 63177 Aubière France

Markov localization is one of the effective techniques for determining the physical locations of an autonomous vehicle whose the perceptions of the environment are limited. To improve the localization, a multi-sensor approach is used. A landmark selection process is usually employed. The aim of this selection strategy is to select the landmark that answers at best to a criterion. In general, the selected landmark is the one that improve the most the vehicle's location. In this paper, we extend the landmark selection problem into a resource selection (i.e. sensor and feature detection algorithm) problem. This selection is also based on a criterion. However, this criterion is defined in function of the application's objectives. Here, the application concerns vehicle's guidance. This last one requires an accurate and reliable estimation. Thus, we propose a novel selection strategy of the landmark, the sensor, and the feature detection algorithm to offer an accurate and reliable localization. We demonstrate the practicality of this approach by guiding an experimental vehicle in real outdoor environment.

关键词： Navigation Remotely operated vehicles Mobile robots Robot sensing systems computer vision Detection algorithms Cameras Sensor systems Entropy intelligent vehicles

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Stereo Analysis Extension Based on BRDF Reciprocity

Stereo Analysis Extension Based on BRDF Reciprocity

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International Conference on Electronics, Communications and computers (CONIELECOMP)

作者： A. Morales-Reyes M. Arias-Estrada Optics and Electronics Computer Science Department National Institute for Astrophysics Optics and Electronics Tonantzintla Puebla Mexico

This paper presents an extension of the binocular technique which considers reciprocal reflectance of surfaces. This particular property allows to recover depth information for surfaces where others stereo techniques failed, for example, textureless and highly specular surfaces. The consideration of an arbitrary BRDF (Bidirectional Reflectance Distribution Function) improves conventional stereo algorithms, in such a way that explicit or implicit assumptions about a reflectance function are not necessary and dense depth maps are obtained. Fixed specularities and correspondence between half-occluded regions reduce processing complexity which is an important advantage of this methodology. This paper provides a general geometry assembly to this approach extending its applicability to some specific areas where accurate depth measures are required. Several results from our implementation are shown using rendered ray-tracing stereo images.

关键词： Reflectivity Surface texture Stereo vision Lighting Rendering (computer graphics) Ray tracing intelligent robots Quality control Biomedical imaging Layout

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On the Development of intelligent Autonomous Agricultural Robot

On the Development of Intelligent Autonomous Agricultural Ro...

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Advances in Science and Engineering Technology International Conferences (ASET)

作者： Abdel Ilah N. Alshbatat Moath Awawdeh Department of Communication Electronic and Computer Engineering Tafila Technical University Tafila Jordan Department of Electrical Engineering Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

This study discuss the development of an intelligent autonomous agricultural robot, designed to detect and treat diseases affecting herbaceous medicinal plants using artificial intelligence (AI) techniques. Herbaceous plants are particularly prone to diseases and require regular monitoring due to their rapid growth and short flowering periods. Leveraging technologies such as Internet of Things (IoT) and artificial intelligence, farmers can now efficiently identify and address plant diseases at early stage. In this work, we present an intelligent autonomous robot capable of proactively identifying and treating herbaceous diseases as a preventive measure. We developed a robust algorithm to control the robot's movements within predefined areas inside plant greenhouses. Through the use of Pixy camera, the robot can detect plant yellowing, prompting it to halt and direct a nozzle towards the identified plant, initiating a preventive spraying process at predetermined times and positions. Furthermore, the robot is equipped with a companion computer responsible for disease identification using deep learning algorithms. This identification capability enables farmers to make immediate decisions and initiate disease treatments promptly. The entire system was developed, implemented, and initially tested outside our labs. The testing results corroborate the system's practicality and demonstrate that the prototype may be simply implemented to provide an added-value to the farmers of herbaceous medicinal plants.

关键词： Medicinal plants Agricultural robots Plant diseases Robot vision systems Spraying Prototypes Internet of Things Monitoring Diseases Testing

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