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Implementation and Validation of the CautiousBug Algorithm in ROS Noetic

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Implementation and Validation of the CautiousBug Algorithm i...

Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Roman Mishenin Alexander Eryomin Tatyana Tsoy Evgeni Magid Department of Computer Engineering (DCE) Moscow Institute of Electronics and Mathematics Higher School of Economics Moscow Russian Federation Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russian Federation

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

In this paper, we present an implementation of the CautiousBug algorithm within the Noetic distribution of the Robot Operating System (ROS). Bug algorithms address a challenge of robot navigation in unknown environments without relying on pre-existing maps or constructing new ones. These algorithms utilize odometry data, operate without a map, require minimal computational resources, and can be implemented on relatively simple hardware. A C++ software application was created to simulate a behavior of the CautiousBug algorithm in various environments within the Gazebo simulator. This application allows for an analysis of key metrics, including accumulated yaw, distance traversed and algorithm's runtime. We conducted a set of virtual experiments in the Gazebo to evaluate the CautiousBug performance.

关键词： Runtime Navigation Software architecture Working environment noise Operating systems Software algorithms Software Robustness Path planning Robots

Data-driven Bandwidth Adaptation for Radio Access Network Slices

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arXiv 2023年

作者： Nikolaidis, Panagiotis Zoulkarni, Asim Baras, John Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

The need to satisfy the QoS requirements of multiple network slices deployed at the same base station poses a major challenge to network operators. The problem becomes even harder when the desired QoS involves packet delays. In that case, network utility maximization is not directly applicable since the utilities of the slices are unknown. As a result, most related works learn online the utilities of all slices and how to split the resources among them. Unfortunately, this approach does not scale well for many slices. Instead, it is needed to perform learning separately for each slice. To this end, we develop a bandwidth demand estimator;a network function that periodically receives as input the traffic of the slice and outputs the amount of bandwidth that its MAC scheduler needs to deliver the desired QoS. We develop the bandwidth demand estimator for QoS involving packet delay metrics based on a model-based reinforcement learning algorithm. We implement the algorithm on a cellular testbed and conduct experiments with time-varying traffic loads. Results show that the algorithm delivers the desired QoS but with significantly less bandwidth than non-adaptive approaches and other baseline online learning algorithms. © 2023, CC BY.

关键词： Quality of service

Enhancing Parameter Estimation in Modified Spencer Models Using the Firefly Optimization Algorithm

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Enhancing Parameter Estimation in Modified Spencer Models Us...

Annual Conference of Industrial Electronics Society

作者： Fatemeh Behbahani Mehdi Behbahani Department of Artificial Intelligence and Robotics (CAIRO) Malaysia-Japan International Institute of Technology (MJIIT) UTM Kuala Lumpur Malaysia Department of Electrical and Computer Engineering Islamic Azad University of Mahshahr Khuzestan Iran

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Optimization algorithms are critical in solving complex engineering problems where parameter estimation is crucial. Among various metaheuristic algorithms, the Firefly Optimization Algorithm (FOA) has gained significant attention due to its simplicity and effectiveness in solving multimodal optimization problems. The objective of this paper is to evaluate the performance of the Firefly Optimization Algorithm (FOA) for parameter estimation in Modified Spencer Models, a specific type of Bouc-Wen hysteresis model used in various engineering applications, particularly in modeling and controlling magneto-rheological (MR) dampers. This study demonstrates the algorithm's effectiveness in estimating parameters with high accuracy and minimal computational effort. Additionally, its performance will be compared with the widely used Particle Swarm Optimization (PSO) algorithm. The results indicate that although the Firefly Optimization Algorithm (FOA) efficiently handles multimodal optimization tasks, making it a valuable tool in engineering and applied sciences, the Particle Swarm Optimization (PSO) algorithm performs better in minimizing error and achieving faster convergence.

关键词： Parameter estimation Accuracy Computational modeling Metaheuristics Mathematical models Shock absorbers Numerical models Particle swarm optimization Optimization Convergence

Implementation of Rev1 and Rev2 Bug Family Algorithms in ROS Noetic

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Implementation of Rev1 and Rev2 Bug Family Algorithms in ROS...

Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Maksim Roslavtsev Alexander Eryomin Ramil Safin Evgeni Magid Department of Computer Engineering (DCE) Moscow Institute of Electronics and Mathematics Higher School of Economics Moscow Russian Federation Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russian Federation

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

Modern map-dependent algorithms for mobile robot navigation typically overload a CPU and memory with a gradually increasing amount of environmental data. In contrast, Bug family local path planning algorithms operate without mapping and have significantly lower hardware requirements. Bug algorithms use real-time measurements from visual and touch sensors to make immediate decisions on direction of the robot’s motion. This paper presents an implementation of Rev1 and Rev2 Bug algorithms for the Robot Operating System (ROS) Noetic framework with a virtual model of the TurtleBot3 Burger robot. Given algorithms are validated in Gazebo simulation using various convex and maze environments. The results demonstrate superiority of Rev1 method over Rev2 in trajectory length in convex maps. However, Rev2 finds shorter paths in maze type environments.

关键词： Visualization Navigation Operating systems computer bugs Tactile sensors Real-time systems Trajectory Motion measurement Robots Testing

Learning Agent Interactions from Density Evolution in 3D Regions With Obstacles

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arXiv 2023年

作者： Tirumalai, Amoolya Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what current robots can do. Owing to the difficulty of observing and recovering the trajectories of the agents, we focus on the dynamics of their probability densities, which are governed by partial differential equations (PDEs), namely compressible Euler equations subject to non-local forces. We formulate the inverse problem of learning interactions as a PDE-constrained optimization problem of minimizing the squared Hellinger distance between the histogram of the flock and the distribution associated to our PDEs. The numerical methods used to efficiently solve the PDE-constrained optimization problem are described. Realistic flocking data are simulated using the Boids model of flocking agents, which differs in nature from the reconstruction models used in our PDEs. Our analysis and simulated experiments show that the behavior of cohesive flocks can be recovered accurately with approximate PDE solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

Reinforcement Learning Based Dynamic Power Control for UAV Mobility Management

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Reinforcement Learning Based Dynamic Power Control for UAV M...

Asilomar Conference on Signals, systems & computers

作者： Irshad A. Meer Karl-Ludwig Besser Mustafa Ozgerl H. Vincent Poor Cicek Cavdar Department of Electrical and Computer Engineering Princeton University USA Division of Communication Systems KTH Royal Institute of Technology Sweden

Modern communication systems need to fulfill multiple and often conflicting objectives at the same time. In particular, new applications require high reliability while operating at low transmit powers. Moreover, reliability constraints may vary over time depending on the current state of the system. One solution to address this problem is to use joint transmissions from a number of base stations (BSs) to meet the reliability requirements. However, this approach is inefficient when considering the overall total transmit power. In this work, we propose a reinforcement learning-based power allocation scheme for an unmanned aerial vehicle (UAV) communication system with varying communication reliability requirements. In particular, the proposed scheme aims to minimize the total transmit power of all BSs while achieving an outage probability that is less than a tolerated threshold. This threshold varies over time, e.g., when the UAV enters a critical zone with high-reliability requirements. Our results show that the proposed learning scheme uses dynamic power allocation to meet varying reliability requirements, thus effectively conserving energy.

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Combinatorial Multi-armed Bandits: Arm Selection via Group Testing

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arXiv 2024年

作者： Mukherjee, Arpan Ubaru, Shashanka Murugesan, Keerthiram Shanmugam, Karthikeyan Tajer, Ali Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States IBM T.J. Watson Research Center United States

This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving this problem typically involve two key sub-routines: (1) a parameter estimation routine that sequentially estimates a set of base-arm parameters, and (2) a super-arm selection policy for selecting a subset of base arms deemed optimal based on these parameters. State-of-the-art algorithms assume access to an exact oracle for super-arm selection with unbounded computational power. At each instance, this oracle evaluates a list of score functions, the number of which grows as low as linearly and as high as exponentially with the number of arms. This can be prohibitive in the regime of a large number of arms. This paper introduces a novel realistic alternative to the perfect oracle. This algorithm uses a combination of group-testing for selecting the super arms and quantized Thompson sampling for parameter estimation. Under a general separability assumption on the reward function, the proposed algorithm reduces the complexity of the super-arm-selection oracle to be logarithmic in the number of base arms while achieving the same regret order as the state-of-the-art algorithms that use exact oracles. This translates to at least an exponential reduction in complexity compared to the oracle-based approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Parameter estimation

Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

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arXiv 2023年

作者： Abu-Rasheed, Hasan Weber, Christian Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view.

关键词： Recommender systems

Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

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Fuzzy Object Ambiguity Determination and Human Attention Ass...

International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kevin Fan Melanie Jouaiti Kerstin Dautenhahn Chrystopher L. Nehaniv Department of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada Departments of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. 1

关键词： Fuzzy logic Service robots Source coding Sociology Human-robot interaction Data integration Reliability