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autonomous NAVIGATION systems FOR MOBILE ROBOTS BASE ON OMNIDIRECTIONAL VISION SYSTEM

12th International Conference on Climbing and Walking Robots the Support Technologies for Mobile Machines (CLAWAR)

作者： Kasaei, Seyed Hamidreza Mohades Kasaei, S. M. Kasaei, S. A. Taheri, M. Ahmadi, N. Delshad, M. Islamic Azad Univ Khorasgan Branch Young Researcher Club Esfahan Iran Islamic Azad Univ Dept Comp Khorasgan Branch Esfahan Iran

ISBN: (纸本)9789814291262

Due to their advantages, omni-directional mobile robots have found many applications especially in robotic soccer competitions. However, omni directional navigation system, omni-vision system and kicking mechanism in such mobile robots have not ever been combined. This situation brings the idea of a robot with no head direction into existence, a comprehensive omni directional mobile robot. Such a robot can respond more quickly and it would be capable for more sophisticated behaviors with multi-sensor data fusion algorithm for global localization. Despite recent advances, effective control and self-localization methods of omni-directional mobile robots remain as important and challenging issues. For this purpose, we utilize the sensor data fusion method in the control system parameters, self localization and world modeling. A vision-based self-localization and the conventional odometry systems are fused for robust self-localization. The methods have been tested in the many Robocup competition field middle size robots.. The localization algorithm includes filtering, sharing and integration of the data for different types of objects recognized in the environment. This paper has tried to focus on description of areas such as omni directional mechanisms, mechanical structure, omni-vision sensor for object detection, robot path planning, and other subjects related to mobile robot's software.

关键词： Mobile robot Machine vision Omni directional movement Multi agent Robot path planning Object Localization

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sensor fusion USING FUZZY INTEGRAL AND DIVERSE BAYESIAN NETWORKS

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IFAC Proceedings Volumes 2008年第2期41卷 10421-10426页

作者： Jamie Walls Albert Esterline Abdollah Homaifar Author Autonomous Control and Information Technology Center (ACIT) Electrical & Computer Engineering Department North Carolina Agricultural & Technical State University Greensboro NC

This paper investigates and contrasts the use of different Bayesian networks and a fuzzy integral for real-time sensor fusion using sonar and rangefinder laser values on an ActivMedia robot. Bayesian networks have become increasingly popular because of their ability to capitalize on the conditional probabilities present in an influence chain. The Choquet fuzzy integral, which has primarily been used for statistical analysis, has a great power of description. Comparison of the two methods indicates that noise within the sensor network can drastically affect the accuracy of the results, especially those obtained using the Bayesian network.

关键词： Information and sensor fusion autonomous robotic systems Data-fusion Multi sensor systems Guidance navigation and control Intelligent robotics

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Unmanned systems Technology XXII

Unmanned Systems Technology XXII

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Unmanned systems Technology XXII 2020

ISBN: (纸本)9781510636279

The proceedings contain 23 papers. The topics discussed include: safety model of automated vehicle-VRU conflict under uncertain weather conditions and sensors failure;simulated RF environments for UAV-mounted Bluetooth 5.0 receivers;RF emitter localization with robotic swarms;jellyfish: non-radio frequency counter drone technology;reference architecture of technological demonstrators for enhanced maritime situational awareness;protecting publish/subscribe interactions via TLS and a system-wide certificate validation engine;acoustic time-reversal based multi-sensor communication for autonomous structural health monitoring of aerial vehicles;compute-bound and low-bandwidth distributed 3D graph-SLAM;and decentralized PD control for multi-robot collective transport to a target location using minimal information.

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12th International Symposium on Distributed autonomous robotic systems, DARS 2014

12th International Symposium on Distributed Autonomous Robot...

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12th International Symposium on Distributed autonomous robotic systems, DARS 2014

ISBN: (纸本)9784431558774

The proceedings contain 32 papers. The special focus in this conference is on Collaborative Exploration, Localization, and Mapping. The topics include: Distributed online patrolling with multi-agent teams of sentinels and searchers;human-robot collaborative topological exploration for search and rescue applications;a repartitioning algorithm to guarantee complete, non-overlapping planar coverage with multiple robots;on combining multi-robot coverage and reciprocal collision avoidance;distributed safe deployment of networked robots;marsim, a simulation of the marsubots fleet using netlogo;scalable cooperative localization with minimal sensor configuration;towards cooperative localization in robotic swarms;multiple operator augmented RSLAM;multi-robot manipulation without communication;distributed path planning for collective transport using homogeneous multi-robot systems;collective construction of dynamic equilibrium structure through interaction of simple robots with semi-active blocks;cooperative mobile robot control architecture for lifting and transportation of any shape payload;a response threshold sigmoid function model for swarm robot collaboration;potential game-theoretic analysis of a market-based decentralized task allocation algorithm;the hybrid information and plan consensus algorithm with imperfect situational awareness;adaptive leader-follower formation in cluttered environment using dynamic target reconfiguration;a graph-based formation algorithm for odor plume tracing;multi-agent visibility-based target tracking game;analysis and experimental validation;path planning for multi-agent jellyfish removal robot system JEROS and experimental tests;motion planning of multiple mobile robots based on artificial potential for human behavior and robot congestion and cooperative pathfinding in distributed systems with limited sensing and communication range.

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Situation Awareness within Maritime Applications

Situation Awareness within Maritime Applications

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OCEANS Conference

作者： Hamren, Rasmus Baumgart, Stephan Curuklu, Baran Ekstrom, Mikael Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden Nord Engn Partner Norrtalje Sweden Epiroc Orebro Sweden

ISBN: (纸本)9798350362077

The advent of powerful control units and the widespread availability of cheap computers have significantly increased the role of artificial intelligence (AI) in various sectors. In the field of maritime applications, this progress has led to the emergence of Edge AI as an important technology. This research focuses on the application of Edge AI to maritime vessels, addressing key aspects of maritime operations. Using Edge AI, we aim to improve the situation awareness and operational efficiency of marine vessels. This study explores Edge AI integration into marine environments and emphasizes its potential to improve on-board safety, navigation and decision-making processes. Our approach shows how smart units decentralized in large central systems can lead to more efficient and adaptive maritime operations and paving the way for a new era of technologically advanced and environmentally conscious maritime practices.

关键词： Edge AI Environmental Sustainability sensor fusion Wave Recognition autonomous Vessel Operation Fuel Conservation Maritime Safety Real-time Data Processing

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Approach of adaptive acquisition and modeling for free-form surface with structured-light vision sensor

Approach of adaptive acquisition and modeling for free-form ...

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sensor fusion and decentralized control in robotic systems III

作者： Chen, Kang-Ning Chen, Hang Liu, Zhi-gang Xi'an Jiaotong Univ Xi'an China

Rapid and high precision data acquisition methodology from coordinate components with free-form surface and geometrical model can be implemented widely. Typical application covers part localization, automatic calibration and reverse engineering. Integrated structured light vision sensor with CMM (Coordinate Measurement Machine) enhances the high-precision coordinate measurement capability. In this paper a curvature-based adaptive sampling approach and the evaluating index for the sampling precision are presented. The matching and subdividing algorithm for generating matrix-type mesh data from sample points is described. The methodology to register and merge the measured data from multiple viewpoints to model the free-form surface is also presented. Based on the given initial coordinate rotation matrix R and transformation vector T, the different viewpoints can be translated into a unique frame of reference. By introducing special coordinate of 3D space, the registered data is divided into mesh, which cover the whole surface of object. An application example for shoe modeling is described to illustrate the advantages.

关键词： Computer vision

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autonomous Landing of eVTOL Vehicles via Deep Q-Networks

Autonomous Landing of eVTOL Vehicles via Deep Q-Networks

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AIAA Aviation Forum

作者： Deniz, Sabrullah Wu, Yufei Shi, Yang Wang, Zhenbo Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Urban Air Mobility (UAM) promises a new mode of transportation system providing disruptive innovative operation safely and efficiently in a highly constrained urban environment. The UAM is attracting interest from a wide range of stakeholders, institutions, and businesses due to the technological advances in electric and distributed propulsion, which makes it easier to construct unique aircraft types with the capability for electric Vertical Take-off and Landing (eVTOL). The autonomous landing of an eVTOL vehicle to a ground vertiport needs highly automated systems, while in a piloted helicopter the landing is directed by the Air Traffic control unit and pilot. In this paper, we propose a deep reinforcement learning-based method to locate the landing marker and land the eVTOL vehicle on it using only low-resolution images captured by a camera pointed downward. The proposed approach is based on Deep Q-Networks (DQNs) using a high-level control policy to navigate the eVTOL vehicle toward the landing marker. We have implemented different approaches, including DQNs and double DQNs algorithms. We trained and tested the vehicle in the robotic Operating System (ROS) Gazebo simulation environment within a small-scale environmental setting. The performance of our model has a promising result to land the vehicle autonomously by following the generated policy throughout the network. The precise and safe landing of eVTOL vehicles is one of the key steps towards addressing conflict and delays during peak hours at the vertiport for future UAM applications.

关键词： Electric Vertical Take off and Landing Urban Air Mobility Reinforcement Learning Air Traffic control Unit Helicopters Operating systems Convolutional Neural Network Unmanned Aerial Vehicle Proportional Integral Derivative sensor fusion

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Competing Objective Optimization in Networked Swarm systems

Competing Objective Optimization in Networked Swarm Systems

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IEEE National Aerospace and Electronics Conference (NAECON)

作者： Ragi, Shankarachary Dey, Shawon Ali, Azam Md Mittelmann, Hans D. South Dakota Sch Mines & Technol Dept Elect Engn Rapid City SD 57701 USA Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA

ISBN: (纸本)9781728114163

In this paper, we develop a decentralized collaborative sensing algorithm where the sensors are located on-board autonomous unmanned aerial vehicles. We develop this algorithm in the context of a target tracking application, where the objective is to maximize the tracking performance measured by the mean-squared error between the target state estimate and the ground truth. The tracking performance depends on the quality of the target measurements made at the sensors, which depends on the relative location of the sensors with respect to the target. Our goal is to control the motion of the swarm of vehicles with on-board sensors to maximize target tracking performance. Each sensor (on-board the vehicle) generates local noisy measurements of the target location, and the sensors maintain and update target state estimates via Bayesian data fusion rules using local measurements and the information received from neighboring sensors. The quality of the data fusion depends on the network graph over which the sensors exchange information, and this determines the overall target tracking performance. We also assume that each sensor is powered by a limited energy source;which we assume is drained by how frequently sensors exchange information. The goal is to optimize the collective motion of the vehicles/sensors (also determines the network graph connectivity) such that the mean-squared target tracking error and the network energy costs are jointly minimized. This problem belongs to a class of hard optimization problems called conflicting objective limited resource optimization (COLRO). We develop a fast heuristic algorithm, using dynamic programming principles, to solve this COLRO problem in real-time.

关键词： Swarm systems target tracking competing objectives sensor network

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Scalable and Modular Ultra-Wideband Aided Inertial Navigation

Scalable and Modular Ultra-Wideband Aided Inertial Navigatio...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jung, Roland Weiss, Stephan Univ Klagenfurt Dept Smart Syst Technol Control Networked Syst Grp Klagenfurt Austria

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Navigating accurately in potentially GPS-denied environments is a perquisite of autonomous systems. Relative localization based on ultra-wideband (UWB) is - especially indoors - a promising technology. In this paper, we present a probabilistic filter based Modular Multi-sensor fusion (MMSF) approach with the capability of using efficiently all information in a fully meshed UWB ranging network. This allows an accurate mobile agent state estimation and the calibration of the ranging network's spatial constellation. We advocate a new paradigm that includes elements from Collaborative State Estimation (CSE) and allows us considering all stationary UWB anchors and the mobile agent as a decentralized set of estimtors/filters. With this, our method can include all meshed (inter-)sensor observations tightly coupled in a modular estimator. We show that the application of our CSE-inspired method in such a context breaks the computational barrier. Otherwise, it would, for the sakeof complexity-reduction, prohibit the use of all available information or would lead to significant estimator inconsistencies due to coarse approximations. We compare the proposed approach against different MMSF strategies in terms of execution time, accuracy, and filter credibility on both synthetic data and on a dataset from real Unmanned Aerial Vehicles (UAVs).

关键词： Location awareness Mobile agents Inertial navigation Information filters Probabilistic logic Distance measurement State estimation

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21st International Conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2023

21st International Conference on Practical Applications of A...

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21st International Conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2023

ISBN: (纸本)9783031376153

The proceedings contain 37 papers. The special focus in this conference is on Practical Applications of Agents and Multi-Agent systems. The topics include: Fuzzy Hyperplane Based Twin-KSVC and Its Applications to Stock Price Movement Direction Prediction Based on News Articles;enhancing Safety Checking Coverage with Multi-swarm Particle Swarm Optimization;a decentralized Resource Allocation Protocol in Social Networks;multi-agent Learning of Causal Networks in the Internet of Things;Investigation of Integrating Solution Techniques for Lifelong MAPD Problem Considering Endpoints;towards Norm Entrepreneurship in Agent Societies;velluscinum: A Middleware for Using Digital Assets in Multi-agent systems;parking Problem with Multiple Gates;UAV Swarm control and Coordination Using Jason BDI Agents on Top of ROS;AJAN: An Engineering Framework for Semantic Web-Enabled Agents and Multi-Agent systems;traffic Assignment Optimization Using Flow-Based Multi-maps;a Monitoring Agent for Advancing Elderly Care Through Mobile Health Technology;extrinsic Emotion Regulation by Intelligent Agents: A Computational Model Based on Arousal-Valence Dimensions;optimization of Complex systems in Photonics by Multi-agent robotic control;GTG-CoL: A New decentralized Federated Learning Based on Consensus for Dynamic Networks;a Novel Framework for Multiagent Knowledge-Based Federated Learning systems;exploring Planner-Guided Swarms Running on Real Robots;integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents;a Multi-Agent Based Dynamic Network Slice Tarification Framework;An IDE to Support the Development of Embedded Multi-Agent systems;adaptive Consumption by Continuous Negotiation;bioinspired Artificial Cockroach Colony Strategy Combined with 2-Type Fuzzy Logic for the Priority-Based Sanitization of Railway Stations;embedding autonomous Agents into Low-Power Wireless sensor Networks;Safe autonomous Decision-Making with vGOAL;Towards Exception Handling

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