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distributed Formation Estimation Via Pairwise Distance Measurements

IEEE roboticS AND AUTOMATION LETTERS 2021年第2期6卷 3017-3024页

作者： Ziegler, Thomas Karrer, Marco Schmuck, Patrik Chli, Margarita Swiss Fed Inst Technol Vis Robot Lab CH-8000 Zurich Switzerland

Continuously and reliably estimating the relative configuration of robotic swarms in real-time constitutes a core functionality when pursuing the autonomy of such a swarm. Relying on external positioning systems, such as GPS or motion tracking systems, can provide the required information, but significantly limits the generality of an approach. In this letter, we target formation estimation for autonomous flights of swarms of small UAVs, as they pose particularly challenging restrictions on onboard resources, while opening up a large variety of practical scenarios for a multi-robot setup. While state-of-the-art has been addressing efficient formation estimation, scalability remains limited to only very few agents that can be handled in real-time, with the workload of each agent depending on the total number of agents in the swarm. Aiming for scalable multi-robot systems, here we propose a distributed formation estimation approach, where the computational load of each agent is decoupled from the swarm size. This approach is implemented in a setup with minimal communication effort, requiring only ego-motion estimates from each agent and pairwise distance measurements between them, constraining their configuration globally. Evaluations on swarms of up to 49 UAVs demonstrate the power of our approach to handle large swarms, while keeping the computational load bounded for individual agents and requiring only little data exchange between two robots.

关键词： Robot sensing systems Estimation Unmanned aerial vehicles Scalability Location awareness Global Positioning System Collaboration Aerial systems multi-robot systems perception and autonomy sensor fusion

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Robot/UAV Indoor Visual SLAM in Smart Cities Based on Remote Data Processing

Robot/UAV Indoor Visual SLAM in Smart Cities Based on Remote...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Jharko, Elena Mamchenko, Mark Khripunov, Sergey P. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Laboratory of Distributed Information and Control Systems Moscow Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Laboratory of Cyber-Physical Systems Moscow Russia

ISBN: (纸本)9781665464291

Indoor navigation is a topical issue in the use of robotics and unmanned aerial vehicles (UAVs) in a smart city, especially in the absence/weak signals of satellite navigation. One of the promising solutions of these issues is the use of visual simultaneous localization and mapping (vSLAM) algorithms, and one or more remote data processing servers for localization and map construction of the robots and the UAVs. The paper analyzes the current state of remote vSLAM and gives the requirements of a promising similar framework for UAVs in a smart city. In general, it is possible to distinguish two approaches to remote vSLAM systems for robots/UAVs. The first one implies sending raw data from sensors (video stream) via wireless connection directly to a remote server, which can be used for cloud/fog/edge computing. The second approach involves data pre-processing on the robots/UAVs, with subsequent transmission of data to the remote cloud/fog/edge computing server for further processing, acquiring map keyframes and map updates. The considered approaches, algorithms and solutions are classified by the type and the number of servers used, the maximum number of robotic agents/UAVs, the presence of navigation data fusion, as well as the type of video sensor used. Based on the analysis of existing solutions and vSLAM classification, it is possible to form a set of requirements for promising vSLAM systems for indoor navigation of robots/UAVs, including their camera input, resolution, number of frames per second (FPS), bandwidth, support of wireless standards, channels, and protocols, multi-robot/multi-server support, and time consumption. © 2023 IEEE.

关键词： Robots

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Robot/UAV Indoor Visual SLAM in Smart Cities Based on Remote Data Processing

Robot/UAV Indoor Visual SLAM in Smart Cities Based on Remote...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Elena Jharko Mark Mamchenko Sergey P. Khripunov Laboratory of Distributed Information and Control Systems V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Laboratory of Cyber-Physical Systems V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

关键词： Wireless communication Wireless sensor networks Visualization Simultaneous localization and mapping Smart cities Indoor navigation sensor fusion

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Consensus, cooperative learning, and flocking for multiagent predator avoidance

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INTERNATIONAL JOURNAL OF ADVANCED robotic SYSTEMS 2020年第5期17卷

作者： Young, Zachary La, Hung Manh Univ Nevada Dept Comp Sci & Engn Adv Robot & Automat ARA Lab Reno NV 89557 USA

Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this article, a hybrid multiagent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learns to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multiagent coordination tasks, but this article focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state-space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments, resulting in an efficient cooperative learning behavior. In the future, the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multiagent tasks.

关键词： distributed algorithms for multirobot coordination mobile robots and multirobot systems multiagent robot teams mobile sensor networks swarm robotics multiagent learning function approximation consensus flocking control

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Robot Drivers: Learning to Drive by Trial & Error 15

Robot Drivers: Learning to Drive by Trial & Error

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15th International Conference on Mobile Ad-Hoc and sensor Networks (MSN)

作者： Bosello, Michael Tse, Rita Pau, Giovanni Univ Bologna DISI Dept Comp Sci & Engn Cesena Italy Macao Polytech Inst Sch Appl Sci Macau Peoples R China UCLA Dept Comp Sci Los Angeles CA 90024 USA Sorbonne Univ LIP 6 Paris France

ISBN: (纸本)9781728152127

Autonomous cars have been in the making for over 15 years. Skepticism has taken the place of initial hype and enthusiasm. Current autonomous driving systems give no guarantee of 100% correctness and reliability, and users are not willing to take a chance on a car that is unable to cope with all the possible driving scenarios. robotic drivers are expected to be perfect. Major players such as Tesla and Waymo rely on highly detailed maps and very large sensor data in a race to build the ultimate robotic driver to cope with all possible driving scenarios. This approach optimizes for safety but delays the dream of fully autonomous cars. In this paper we consider robot-drivers as teen-drivers eager to learn how to drive but prone to mistakes in the beginning. The question we are trying to investigate is "what if we allow autonomous cars to make mistakes like young human drives do?" In this paper, we explore reinforcement learning for small size autonomous vehicles fusing information from several sensors including a camera, color sensors, and sonar sensors. The robot-drivers have initially no information about the driving scenarios they learn with experience through a mechanism of rewards designed to quickly help our robot-teen to learn its driving skills.

关键词： sensor fusion Reinforcement Learning self-driving multi-agents

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Biobotic Insect Swarm based sensor Networks for Search and Rescue

Biobotic Insect Swarm based Sensor Networks for Search and R...

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Conference on Signal Processing, sensor/Information fusion, and Target Recognition XXIII

作者： Bozkurt, Alper Lobaton, Edgar Sichitiu, Mihail Hedrick, Tyson Latif, Tahmid Dirafzoon, Alireza Whitmire, Eric Verderber, Alexander Marin, Juan Xiong, Hong North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Univ N Carolina Dept Biol Chapel Hill NC 27599 USA Middle Creek High Sch Apex NC 27539 USA

ISBN: (纸本)9781628410280

The potential benefits of distributed robotics systems in applications requiring situational awareness, such as search-and-rescue in emergency situations, are indisputable. The efficiency of such systems requires robotic agents capable of coping with uncertain and dynamic environmental conditions. For example, after an earthquake, a tremendous effort is spent for days to reach to surviving victims where robotic swarms or other distributed robotic systems might play a great role in achieving this faster. However, current technology falls short of offering centimeter scale mobile agents that can function effectively under such conditions. Insects, the inspiration of many robotic swarms, exhibit an unmatched ability to navigate through such environments while successfully maintaining control and stability. We have benefitted from recent developments in neural engineering and neuromuscular stimulation research to fuse the locomotory advantages of insects with the latest developments in wireless networking technologies to enable biobotic insect agents to function as search-and-rescue agents. Our research efforts towards this goal include development of biobot electronic backpack technologies, establishment of biobot tracking testbeds to evaluate locomotion control efficiency, investigation of biobotic control strategies with Gromphadorhina portentosa cockroaches and Manduca sexta moths, establishment of a localization and communication infrastructure, modeling and controlling collective motion by learning deterministic and stochastic motion models, topological motion modeling based on these models, and the development of a swarm robotic platform to be used as a testbed for our algorithms.

关键词： Insect biobots Seach and rescue Neural engineering Swarm robotics Topological mapping sensor networks

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Fast weighted exponential product rules for robust general multi-robot data fusion

Fast weighted exponential product rules for robust general m...

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International Conference on robotics Science and Systems, RSS 2012

作者： Ahmed, Nisar Schoenberg, Jonathan Campbell, Mark Autonomous Systems Laboratory Cornell University IthacaNY14853 United States

ISBN: (纸本)9780262519687

This paper considers the distributed data fusion (DDF) problem for general multi-Agent robotic sensor networks in applications such as 3D mapping and target search. In particular, this paper focuses on the use of conservative fusion via the weighted exponential product (WEP) rule to combat inconsistencies that arise from double-counting common information between fusion agents. WEP fusion is ideal for fusing arbitrarily distributed estimates in ad-hoc communication network topologies, but current WEP rule variants have limited applicability to general multi-robot DDF. To address these issues, new information-Theoretic WEP metrics are presented along with novel optimization algorithms for efficiently performing DDF within a recursive Bayesian estimation framework. While the proposed WEP fusion methods are generalizable to arbitrary probability distribution functions (pdfs), emphasis is placed here on widely-used Bernoulli and Gaussian mixture pdfs. Experimental results for multi-robot 3D mapping and target search applications show the effectiveness of the proposed methods. © 2013 Massachusetts Institute of Technology.

关键词： Mapping

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A Multi-agent Architecture Based on the BDI Model for Data fusion in Visual sensor Networks

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JOURNAL OF INTELLIGENT & robotic SYSTEMS 2011年第3-4期62卷 299-328页

作者： Castanedo, Federico Garcia, Jesus Patricio, Miguel A. Molina, Jose M. Univ Carlos III Madrid Dept Comp Sci Appl Artificial Intelligence Grp Madrid Spain

The newest surveillance applications is attempting more complex tasks such as the analysis of the behavior of individuals and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent redundancy of the overlapped field of views. This article, presents a Multi-agent architecture based on the Belief-Desire-Intention (BDI) model for processing the information and fusing the data in a distributed visual sensor network. Instead of exchanging raw images between the agents involved in the visual network, local signal processing is performed and only the key observed features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes: (1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2) to improve the quality of the tracking by means of data fusion techniques, and by discarding non reliable sensors. Experimental results on two different scenarios show that the designed architecture can successfully track an object even when occlusions or sensor's errors take place. The sensor's errors are reduced by exploiting the inherent redundancy of a visual sensor network with overlapped field of views.

关键词： distributed visual sensor networks Multi-agent Systems Data fusion

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Multi-Scale Adaptive Sampling with Mobile agents for Mapping of Forest Fires

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JOURNAL OF INTELLIGENT & robotic SYSTEMS 2009年第4期54卷 535-565页

作者： Mysorewala, Muhammad F. Popa, Dan O. Lewis, Frank L. Univ Texas Arlington Automat & Robot Res Inst Arlington TX 76019 USA

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, "EKF-NN-GAS" is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields "truth models", approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.

关键词： Adaptive sampling sensor fusion Kalman filter Potential fields Environmental monitoring Forest fires

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Mobile Agent-based Remote Vision sensor fusion

Mobile Agent-based Remote Vision Sensor Fusion

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IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications

作者： Nestinger, Stephen S. Ko, David Rumer, Alex Cheng, Harry H. Univ Calif Davis Dept Mech & Aeronaut Engn Davis CA 95616 USA

ISBN: (纸本)9781424423675

Vision systems have become popular for remote vision sensing in geographically distributed environments due to vast amount of information they provide. However, remote vision sensors are generally plagued with power and communication bandwidth constraints. Mobile agent technology is a salient solution to geographically distributed and dynamic domains that require subsystems to interact with each other. Mobile agent technology increases power efficiency by reducing communication requirements and increases fusion processing by allowing in-situ integration of on-demand visual processing and analysis algorithms. A mobile agent can dynamically migrate from one vision sensor to another and combine all necessary sensor data in a desired manner specific to the system requesting the data. This paper presents a remote vision fusion architecture based on mobile agent technology that provides a flexible vision fusion solution. The architecture utilizes the Mobile-C library as a basis for the mobile agency along with OpenCV and ImageMagick for vision processing and manipulation. An application example is provided that demonstrates the benefit of using mobile agents for vision fusion in remote vision systems. The validity of the architecture is proven in an experimental setup with a retrofitted robotic cell comprised of a Puma 560, IBM 7575, a conveyor system, and a vision system.

关键词： computer vision Mobile agents vision sensors fusion Remote Building construction ARCHITECTURE visual processing sensor Data SUB-SYSTEM Vision processes

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