Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian approach to optimal sensor placement
International Journal of Robotics Research
Elements of information theory
Elements of information theory
Distributed Algorithms
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
The Journal of Machine Learning Research
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
Continuous trajectory planning of mobile sensors for informative forecasting
Automatica (Journal of IFAC)
Brief paper: A hyperparameter consensus method for agreement under uncertainty
Automatica (Journal of IFAC)
Journal of Intelligent and Robotic Systems
Decentralized Environmental Modeling by Mobile Sensor Networks
IEEE Transactions on Robotics
Distributed Connectivity Control of Mobile Networks
IEEE Transactions on Robotics
Representation of Mutual Information Via Input Estimates
IEEE Transactions on Information Theory
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
Distributed multi-robot patrol: A scalable and fault-tolerant framework
Robotics and Autonomous Systems
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In this paper we present an information-theoretic approach to distributively control multiple robots equipped with sensors to infer the state of an environment. The robots iteratively estimate the environment state using a sequential Bayesian filter, while continuously moving along the gradient of mutual information to maximize the informativeness of the observations provided by their sensors. The gradient-based controller is proven to be convergent between observations and, in its most general form, locally optimal. However, the computational complexity of the general form is shown to be intractable, and thus non-parametric methods are incorporated to allow the controller to scale with respect to the number of robots. For decentralized operation, both the sequential Bayesian filter and the gradient-based controller use a novel consensus-based algorithm to approximate the robots' joint measurement probabilities, even when the network diameter, the maximum in/out degree, and the number of robots are unknown. The approach is validated in two separate hardware experiments each using five quadrotor flying robots, and scalability is emphasized in simulations using 100 robots.