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180
On a Stochastic Sensor Selection Algorithm with Applications in Sensor Scheduling and Sensor Coverage
- AUTOMATICA
, 2006
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Current State of the Art in Distributed Autonomous Mobile Robotics
- Distributed Autonomous Robotic Systems
, 2000
"... As research progresses in distributed robotic systems, more and more aspects of multi-robot systems are being explored. This article surveys the current state of the art in distributed mobile robot systems. Our focus is principally on research that has been demonstrated in physical robot implementat ..."
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Cited by 89 (1 self)
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As research progresses in distributed robotic systems, more and more aspects of multi-robot systems are being explored. This article surveys the current state of the art in distributed mobile robot systems. Our focus is principally on research that has been demonstrated in physical robot implementations. We have identified eight primary research topics within multi-robot systems -- biological inspirations, communication, architectures, localization/mapping/exploration, object transport and manipulation, motion coordination, reconfigurable robots, and learning - and discuss the current state of research in these areas. As we describe each research area, we identify some key open issues in multi-robot team research. We conclude by identifying several additional open research issues in distributed mobile robotic systems.
Cooperative Concurrent Mapping and Localization
, 2002
"... Autonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional informa ..."
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Cited by 77 (3 self)
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Autonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional information available when multiple vehicles operate simultaneously. Multiple vehicles have the potential to map an environment more quickly and robustly than a single vehicle. This paper presents a cooperative CML algorithm that merges sensor and navigation information from multiple autonomous vehicles. The algorithm presented is based on stochastic estimation and uses a feature-based approach to extract landmarks from the environment. The theoretical framework for the collaborative CML algorithm is presented, and a convergence theorem central to the cooperative CML problem is proved for the rst time. This theorem quanties the performance gains of collaboration, allowing for determination of the number of cooperating vehicles required to accomplish a task. A simulated implementation of the collaborative CML algorithm demonstrates substantial performance improvement over non-cooperative CML.
On a decentralized active sensing strategy using mobile sensor platforms in a network
- in 43rd IEEE Conference on Decision and Control
, 2004
"... Abstract — In this paper, we consider the problem of active sensing using mobile nodes as a sensor network to estimate the state of a dynamic target. We propose a gradient-searchbased decentralized algorithm that demonstrates the benefits of distributed sensing. We then examine the task of tracking ..."
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Cited by 61 (5 self)
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Abstract — In this paper, we consider the problem of active sensing using mobile nodes as a sensor network to estimate the state of a dynamic target. We propose a gradient-searchbased decentralized algorithm that demonstrates the benefits of distributed sensing. We then examine the task of tracking multiple targets, and address it via a simple extension to our algorithm. Simulation results show that our simple decentralized approach performs quite well and leads to interesting cooperative behavior. I.
Performance Analysis of Multirobot Cooperative Localization
, 2006
"... This paper studies the accuracy of position estimation for groups of mobile robots performing Cooperative Localization (CL). We consider the case of teams comprised of possibly heterogeneous robots and provide analytical expressions for the upper bound on their expected positioning uncertainty. Thi ..."
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Cited by 48 (6 self)
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This paper studies the accuracy of position estimation for groups of mobile robots performing Cooperative Localization (CL). We consider the case of teams comprised of possibly heterogeneous robots and provide analytical expressions for the upper bound on their expected positioning uncertainty. This bound is determined as a function of the sensors ’ noise covariance and the eigenvalues of the Relative Position Measurement Graph (RPMG), i.e., the weighted directed graph which represents the network of robot-to-robot exteroceptive measurements. The RPMG is employed as a key element in this analysis and its properties are related to the localization performance of the team. It is shown that for a robot group of certain size, the maximum expected rate of uncertainty increase is independent of the accuracy and number of relative position measurements and depends only on the accuracy of the proprioceptive and orientation sensors on the robots. Additionally, the effects of changes in the topology of the RPMG are studied and it is shown that at steady state, these reconfigurations do not inflict any loss in localization precision. Experimental data, as well as simulation results that validate the theoretical analysis are presented.
Distributed cooperative active sensing using consensus filters.
- In IEEE Int. Conf. on Robotics and Automation,
, 2007
"... Abstract-We consider the problem of multiple mobile sensor agents tracking the position of one or more moving targets. In our formulation, each agent maintains a target estimate, and each agent moves so as to maximize the expected information from its sensor, relative to the current uncertainty in ..."
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Cited by 46 (0 self)
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Abstract-We consider the problem of multiple mobile sensor agents tracking the position of one or more moving targets. In our formulation, each agent maintains a target estimate, and each agent moves so as to maximize the expected information from its sensor, relative to the current uncertainty in the estimate. The novelty of our approach is that each agent need only communicate with one-hop neighbors in a communication network, resulting in a fully distributed and scalable algorithm, yet the performance of the system approximates that of a centralized optimal solution to the same problem. We provide two fully distributed algorithms based on one-time measurements and a Kalman filter approach, and we validate the algorithms with simulations.
Distributed maximum a posteriori estimation for multi-robot cooperative localization
- in Proceedings of the IEEE International Conference on Robotics and Automation
, 2009
"... Abstract — This paper presents a distributed Maximum A Posteriori (MAP) estimator for multi-robot Cooperative Localization (CL). As opposed to centralized MAP-based CL, the proposed algorithm reduces the memory and processing requirements by distributing data and computations amongst the robots. Spe ..."
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Cited by 40 (8 self)
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Abstract — This paper presents a distributed Maximum A Posteriori (MAP) estimator for multi-robot Cooperative Localization (CL). As opposed to centralized MAP-based CL, the proposed algorithm reduces the memory and processing requirements by distributing data and computations amongst the robots. Specifically, a distributed data-allocation scheme is presented that enables robots to simultaneously process and update their local data. Additionally, a distributed Conjugate Gradient algorithm is employed that reduces the cost of computing the MAP estimates, while utilizing all available resources in the team and increasing robustness to single-point failures. Finally, a computationally efficient distributed marginalization of past robot poses is introduced for limiting the size of the optimization problem. The communication and computational complexity of the proposed algorithm is described in detail, while extensive simulation studies are presented for validating the performance of the distributed MAP estimator and comparing its accuracy to that of existing approaches. I.
Ubiquitous networking robotics in urban settings
- In Proceedings of the IEEE/RSJ IROS Workshop on Network Robot Systems
"... Abstract- In this paper we will present the objectives of a ..."
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Cited by 32 (16 self)
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Abstract- In this paper we will present the objectives of a
Rescue robotics - a crucial milestone on the road to autonomous systems
- Advanced Robotics Journal
"... In this article we argue that rescue robotics is an important steppingstone in the scientific challenge to create autonomous systems. We motivate why we believe that there is a significant market for rescue robots, which has unique features that allow a fruitful combination of application oriented d ..."
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Cited by 29 (12 self)
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In this article we argue that rescue robotics is an important steppingstone in the scientific challenge to create autonomous systems. We motivate why we believe that there is a significant market for rescue robots, which has unique features that allow a fruitful combination of application oriented developments and basic research. Based on several application examples, for example the estimate that about 2000 road accidents per year in Germany alone involve hazardous goods, we conclude that there is a tremendous need for rescue robots. Unlike other markets for advanced robotics systems like service robots, the rescue robotics domain benefits from the fact that there is a human in the loop, which allows a stepwise transition from dumb teleoperated devices to truly autonomous systems. Current teleoperated devices are already very useful in this domain and they benefit from any bit of autonomy added as human rescue workers are a scarce resource at disaster scenarios and a single operator should ideally supervise a multitude of robots. We present results from the rescue robots and the International University Bremen (IUB) in a core area regarding autonomy, namely mapping.