This paper introduces the Distributed Flight Array which is being developed at ETH Zurich. This multi-propeller platform consists of autonomous single-propeller modules that are able to drive, dock with their peers, and fly in a coordinated fashion. These modules are organized as distributed computational units with minimal sensory input. This is a complex system that is rich in dynamics with plenty of room to explore various distributed estimation and control strategies. Experimental demonstrations in docking, driving, and flight have proven its feasibility.

In this paper, scalable collaborative human-robot systems for information gathering applications are approached as a decentralized Bayesian sensor network problem. Human-computer augmented nodes and autonomous mobile sensor platforms are collaborating on a peer-to-peer basis by sharing information via wireless communication network. For each node, a computer (onboard the platform or carried by the human) implements both a decentralized Bayesian data fusion algorithm and a decentralized Bayesian control negotiation algorithm. The individual node controllers iteratively negotiate anonymously with each other in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the platforms (humans and robots) motion models, their sensors detection functions, as well as the target arbitrary motion model. The results of a collaborative multi-target search experiment conducted with a team of four autonomous mobile sensor platforms and five humans carrying small portable computers with wireless communication are presented to demonstrate the efficiency of the approach.

Coupled operator-multiple vehicle systems are modelled in a unified framework using probabilistic graphs to yield a methodology for analyzing semi-autonomous systems. The framework uses conditional probabilistic dependencies between all elements, leading to a Bayesian network (BN) with probabilistic evaluation capability. Vehicle attitude/navigation states and target/classification states can be evaluated using nonlinear estimators such as the EKF, multiple model filter, information filter, or other approaches. Discrete operator decisions are being modeled as Bayesian network blocks, with conditional dependencies on the vehicle and tracking estimators. Initial decision models use combinations of softmax and discrete probability distributions.

This paper presents a coordinated control technique that allows heterogeneous vehicles to autonomously search for and track multiple targets using recursive Bayesian filtering. A unified sensor model and a unified objective function are proposed to enable search-and-tracking (SAT) within the recursive Bayesian filter framework. The strength of the proposed technique is that a vehicle can switch its task mode between search and tracking while maintaining and using information collected during the operation. Numerical results first show the effectiveness of the proposed technique when a found target becomes lost and must be searched for again. The proposed technique was then applied to a practical marine search-and-rescue (SAR) scenario where heterogeneous vehicles coordinated to search for and track multiple targets. The result demonstrates the applicability of the technique to real search world scenarios

This paper presents a Bayesian approach to the problem of searching for multiple lost targets in a dynamic environment by a team of autonomous sensor platforms. The probability density function (PDF) for each individual target location is accurately maintained by an independent instance of a general Bayesian filter. The team utility for the search vehicles trajectories is given by the sum of the `cumulative' probability of detection for each target. A dual-objective switching function is also introduced to direct the search towards the mode of the nearest target PDF when the utility becomes too low in a region to distinguish between trajectories. Simulation results for both clustered and isolated targets demonstrate the effectiveness of the proposed search strategy for multiple targets.

This paper deals with the problem of coordinating a team of mobile sensor platforms searching for a single mobile non-evading target. It follows the general Bayesian active sensor network approach introduced in [2] where each decision maker plans locally based on an equivalent representation of the target state probability density function (PDF). This paper focuses on the prediction stage of the decentralized Bayesian filter. It looks at how different types of realistic external constraints may affect the target motion and how they may be taken into account in the process model. Two general classes of constraints are identified soft and hard. A few constraint examples from each class are given to illustrate their impact on the evolution of the target state PDF. Multiple constraints of various types can be combined to increase the accuracy of the predicted PDF estimate, thus affecting the individual trajectories of the search platforms. The effectiveness of the framework is demonstrated for a team of airborne search vehicles looking for a drifting target lost in a storm at sea.

This paper presents the real-time control of cooperative unmanned air vehicles (UAV) that dynamically engage multiple targets in a time-optimal manner. Techniques to dynamically allocate vehicles to targets and to subsequently find the time-optimal control actions are proposed. The decentralization of the proposed control strategy is further presented such that the vehicles can be controlled in real-time without significant time delay. The proposed strategy is men applied to various practical battlefield problems, and numerical results show the efficiency of the proposed strategy.

This paper addresses the problem of coordinating a team of multiple heterogeneous sensing platforms searching for a single lost target. In this approach, the utility of a control sequence is a function of the probability density function (PDF) of the target state. Each decision maker builds an equivalent estimate of this PDF by communicating and fusing the information from the other sensor nodes. Coupled utilities incite the agents to collaborate and to agree on the next best set of actions. Decentralized cooperative planning is achieved via anonymous negotiation based on communication of expected observed information. Simulation results demonstrate the efficiency of the cooperative trajectories for a team of autonomous airborne search vehicles.

This paper describes a decentralized Bayesian approach to coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the target state PDF through a Bayesian DDF network enabling him or her to coordinate their actions without exchanging any information about their plans. The advantage of the approach is that a high degree of scalability and real time adaptability can be achieved. The effectiveness of the approach is demonstrated in different scenarios by implementing the framework for a team of airborne search vehicles looking for a stationary, and a drifting target lost at sea.

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy. The map building and exploration task is modeled using an Occupancy Grid (OG) with concurrent localisation performed using a feature-based Simultaneous Localisation And Mapping (SLAM) algorithm. Adaptive sensing aims at maximizing the map information by simultaneously maximizing the expected Shannon information gain (Mutual Information) on the OG map and minimizing the uncertainty of the vehicle pose and map feature uncertainty in the SLAM process. The resulting map building system is demonstrated in an indoor environment using data from a laser scanner mounted on a mobile platform.

Integrated exploration strategy advocated in this paper refers to a tight coupling between the tasks of localization, mapping, and motion control and the effect of this. coupling on the overall effectiveness of an exploration strategy. Our approach to exploration calls for a balanced evaluation of alternative motion actions from the point of view of information gain, localization quality, and navigation cost. To provide a uniform basis of comparison of localization quality between different locations, a "localizability" metric is introduced. It is based on the estimate of the lowest vehicle pose covariance attainable from a given location.