Frédéric Bourgault

Recursive Bayesian search-and-tracking using coordinated uavs for lost targets

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

Coordinated decentralized search for a lost target in a Bayesian world

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.

Decentralized Bayesian negotiation for cooperative search

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.

Process model, constraints, and the coordinated search strategy

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.

Coordinated search for a lost target in a Bayesian world

This paper describes a decentralized Bayesian approach to the problem of coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the probability density function (PDF) of the target state through a general decentralized Bayesian sensor network, enabling them 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 framework is implemented on a real-time high-fidelity multi-vehicle simulator system. The effectiveness of the method is demonstrated in different scenarios for a team of airborne search vehicles looking for both a stationary and a drifting target lost at sea.

Dynamic allocation and control of coordinated UAVs to engage multiple targets in a time-optimal manner

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.

Time-optimal coordinated control of the relative formation of multiple vehicles

This paper presents a solution to the time-optimal control of the relative formation of multiple vehicles. This is a problem in cooperative time-optimal control with a free terminal state constraint. In this paper, a canonical formulation of the problem is first derived. Then, a numerical technique to solve this class of problem is proposed. Numerical results demonstrate the efficacy of the proposed formulation and solution to the problem of expeditiously building and controlling formations of cooperative autonomous vehicles.

An Indoor Experiment in Decentralized Coordinated Search

This paper addresses the problem of coordinating a team of multiple heterogeneous sensing platforms searching for a single mobile target in a dynamic environment. The proposed implementation of an active sensor network architecture combines a general decentralized Bayesian filtering algorithm with a decentralized coordinated control strategy. In this approach, by communicating with their neighbors on the network, each decision maker builds an equivalent representation of the probability density function of the target state on which they base their control decision.

Multiple cooperative unmanned air vehicles engaging multiple targets time-optimally

This article presents a solution to the real-time control of cooperative unmanned air vehicles that engage multiple targets in a time-optimal manner. Techniques to allocate vehicles dynamically to targets and to find the time-optimal control actions of vehicles are proposed. The effectiveness of the time-optimal control technique is first demonstrated through numerical examples. The proposed control strategy is then applied to two practical problems: firstly ten vehicles engage four targets where each target must be engaged by three vehicles, and secondly, four vehicles engage ten targets where each target must be engaged by two vehicles. The numerical results demonstrate the effectiveness of the proposed strategy, and the applicability to the real-time control and the scalability of the proposed control strategy are further discussed.