Search and rescue missions can be efficiently and automatically performed by small, highly maneuverable unmanned aerial vehicle (UAV) teams. The search problem is complicated by a lack of prior information, nonlinear mapping between sensor observations and the physical world, and potentially non-Gaussian sensor noise models. To address these problems, a distributed control algorithm is proposed, using information theoretic methods with particle filters, to compute optimal control inputs for a multi-vehicle, coordinated localization of a stationary target. This technique exploits the structure of the probability distributions of the target state and of the sensor measurements to compute the control inputs that maneuver the UAVs to make observations that minimize the expected future uncertainty of the target state. Because the method directly uses the particle filter state and an accurate sensor noise model to compute the mutual information, it is no longer necessary to discard information by using linear and Gaussian approximations. To ensure safety of the vehicles, the algorithm incorporates collision avoidance and control authority constraints. The resulting information theoretic cost calculation is coupled amongst the vehicles and becomes prohibitive as the size of the UAV team becomes large. Therefore, single vehicle and pairwise approximations to the cost function are used that greatly reduce the computational burden and allow for development of a distributed algorithm for real-time optimization of vehicle trajectories. Simulation results are shown for a bearings-only sensor model with multiple vehicles. Initial flight tests of the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) show the feasibility of implementation of this algorithm on the quadrotor testbed and in real world situations.

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Distributed Cooperative Search using Information-Theoretic Costs for Particle Filters, with Quadrotor Applications ∗

The paper presents a decentralized approach to the solution of the distributed information gathering problem. The main design objectives are scalability with the number of network components, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. The results of two deployment scenarios on an indoor sensor network are presented.

/pdf/a-decentralized-architecture-for-active-sensor-networks-2d13gpj8jm.pdf

A decentralized architecture for Active Sensor Networks

A probabilistic framework for decentralized data fusion and sensor management based on the notion of Bayesian information value is presented. Use is made of information as the expected utility of actions within a framework of decentralized decision-theory. Entropy is used as an information metric. Some preliminary results justifying this approach are shown. >

On sensor management in decentralized data fusion

Fusion of disparate identity estimates for shared situation awareness in a network-centric environment

Robust information filter for decentralized estimation

Present two algorithms for tracking and identification in decentralized multi-sensor systems. Decentralized architectures have many benefits in terms of modularity, speed and robustness. The state estimation (tracking) algorithm is a decentralized Kalman filter (DKF) based on the extended Kalman filter. Identification is achieved by the decentralized Bayesian identification (DBI) algorithm, which identifies targets being tracked. For each of the algorithms The authors discuss optimality and the effect of reducing connectivity. The structure of the algorithms leads to the development of an architecture for a modular sensing node based on communication considerations. The authors present example implementations of both algorithms on actual transputer-based sensing nodes. They describe RCD (region of constant depth) tracking and for this develop a monopulse sonar arrangement with which they implement real-time autonomous tracking by a single sensor node. The second example implementation describes identification of targets being tracked by the DKF using the DBI on actual CCD camera-based nodes. >

B.S.Y. Rao

Papers

Decentralized algorithms and architecture for tracking and identification