Joakim Haugen

Monitoring Moving Objects Using Aerial Mobile Sensors

We propose an optimization-based path-planning framework for an aerial mobile sensor network. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides collision-free mobile sensor trajectories that are feasible with respect to user-defined vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects, represented as the trace of the augmented state and parameter estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming problem using the direct transcription method known as simultaneous collocation. The optimization problem is solved with a receding horizon and both a field experiment and a numerical simulation illustrate the approach.

Optimization-based autonomous remote sensing of surface objects using an unmanned aerial vehicle

This manuscript presents an optimization-based approach for path planning of an aerial mobile sensor that monitors a set of moving surface objects. The purpose of the optimization problem is to obtain feasible mobile sensor trajectories with an objective to minimize the uncertainty of the objects, represented as the trace of the state estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming (NLP) problem using the direct transcription method known as simultaneous collocation. The numerical simulation periodically provides desired sensor trajectories and thus illustrates the approach.

Monitoring an Advection-Diffusion Process Using Aerial Mobile Sensors

A path planning framework for regional surveillance of a planar advection-diffusion process by aerial mobile sensors is proposed. The goal of the path planning is to produce feasible and collision-free trajectories for a set of aerial mobile sensors that minimize some uncertainty measure of the process under observation. The problem is formulated as a dynamic optimization problem and discretized into a large-scale nonlinear programming (NLP) problem using the Petrov–Galerkin finite element method in space and simultaneous collocation in time. Receding horizon optimization problems are solved in simulations with an advection-dominated ice concentration field. Simulations illustrate the usefulness of the proposed method.

UAV Path Planning for Multitarget Tracking with Experiments

Abstract We apply an optimization-based path planning framework for an unmanned aerial vehicle. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides mobile sensor trajectories that are feasible with respect to nonholonomic vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects, represented as the trace of the state estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming (NLP) problem using the direct transcription method known as simultaneous collocation. A field experiment illustrates the approach.

State estimation of ice thickness distribution using mobile sensors

This paper considers the problem of guiding a mobile sensor network in a distributed parameter system. The ice thickness distribution of sea ice is an example of such a system. The objective is to improve the convergence of the state estimation error, compared to a nominal sensor network, by employing a gradient-based guidance scheme. The ice thickness is modeled as a continuity equation and the sensor dynamics as fully actuated 2-input-2-output mass-spring-damper systems. The approach builds on Lyapunov functions to construct the guidance law that achieves, under certain assumptions, a uniformly globally asymptotically stable system. A numerical example illustrates the approach.

A Speed Control Algorithm for Planar Path Maneuvering

Abstract This paper considers the problem of speed control for vehicles maneuvering along planar paths. Maneuverability constraints are taken into account when determining a purposefully commanded forward speed which prevents a given vehicle from derailing when traversing a given path. A lookahead system is first developed to collect relevant path curvature information ahead of the vehicle. This system is able to maintain a constant along-path distance in front of the current vehicle location, even for paths that are not arc-length parameterized. A speed assignment algorithm is subsequently developed which employs both the lookahead system information and known vehicle maneuverability constraints to yield feasible speed commands. Finally, the behavior of the proposed speed control system is illustrated through simulations which also demonstrate its improved performance compared to using constant speed.