Bruno Joao Nogueira Guerreiro

Globally Asymptotically Stable Sensor-Based Simultaneous Localization and Mapping

This paper presents the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM), with application to unmanned aerial vehicles. The SLAM problem is formulated in a sensor-based framework and modified in such a way that the underlying system structure can be regarded as linear time varying for observability analysis and filter design purposes, from which a linear Kalman filter with GAS error dynamics follows naturally. The performance and consistency validation of the proposed sensor-based SLAM filter are successfully assessed with real data, acquired indoors, using an instrumented quadrotor.

Trajectory Tracking Nonlinear Model Predictive Control for Autonomous Surface Craft

This paper presents a solution to the problem of trajectory tracking control for autonomous surface craft (ASC) in the presence of ocean currents. The proposed solution is rooted in nonlinear model predictive control (NMPC) techniques and addresses explicitly state and input constraints. Whereas state saturation constraints are added to the underlying optimization cost functional as penalties, input saturation constraints are made intrinsic to the nonlinear model used in the optimization problem, thus reducing the computational burden of the resulting NMPC algorithm. Simulation results, obtained with a nonlinear dynamic model of a prototype ASC, show that the NMPC strategy adopted yields good performance in the presence of constant currents. Experimental results are also provided to validate the real-time implementation of the NMPC techniques for ASCs.

Trajectory tracking nonlinear model predictive control for autonomous surface craft

This paper presents a solution to the problem of trajectory-tracking control for autonomous surface craft (ASC) in the presence of ocean currents. The proposed solution is rooted in nonlinear model predictive control (NMPC) techniques and addresses explicitly state and input constraints. Whereas state saturation constraints are added to the underlying optimization cost functional as penalties, input saturation constraints are made intrinsic to the nonlinear model used in the optimization problem, thus reducing the computational burden of the resulting NMPC algorithm. Simulation and experimental results show that the NMPC strategy adopted yields good performance in the presence of constant currents and validate the real-time implementation of the proposed techniques.

TRAJECTORY TRACKING H2 CONTROLLER FOR AUTONOMOUS HELICOPTERS: AN APPLICATION TO INDUSTRIAL CHIMNEY INSPECTION

Abstract This paper addresses the problem of industrial chimney inspection using autonomous helicopters. The importance of using these platforms is evidenced by the maintenance cost and risk reduction stemming from the replacement of the standard procedures and the improved detection of structural defects. The approach presented relies on the definition of a trajectory-dependent error space to express the dynamic model of the vehicle, and adopts a Linear Parameter Varying (LPV) representation with piecewise affine dependence on the parameters to model the error dynamics over a set of predefined operating regions. The synthesis problem is stated as a continuous-time H 2 control problem, solved using Linear Matrix Inequalities (LMIs) and implemented within the scope of gain-scheduling control theory. The effectiveness of the proposed controller is assessed in simulation using the full nonlinear model of a small-scale helicopter.

Preliminary results on globally asymptotically stable simultaneous localization and mapping in 3-D

This paper presents the design, analysis, performance evaluation, and preliminary experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM) with application to unmanned aerial vehicles (UAVs). The SLAM problem is formulated in a sensor-based framework and modified in such a way that the system structure may be regarded as linear time-varying for observability purposes, from which a Kalman filter with GAS error dynamics follows naturally. The proposed solution includes the estimation of both body-fixed linear velocity and rate-gyro measurement biases. Both simulation results and preliminary experimental results, using an instrumented quadrotor equipped with a RGB-D camera, are included in the paper to illustrate the performance of the algorithm under realistic conditions.

L 1 adaptive control for autonomous rotorcraft

In this paper, the L1 adaptive control theory is used to design a high bandwidth inner loop controller to provide attitude and velocity stabilization of an autonomous small-scale rotorcraft in the presence of wind disturbances. The nonlinear model of the vehicle is expressed as a linear time-varying system for a predefined region of operation, for which an L1 adaptive controller is designed. The L1 adaptive controller ensures that an uncertain linear time-varying system has uniformly bounded transient response for systems input and output signals, in addition to stable tracking. The performance bounds of L1 adaptive controller can be systematically improved by increasing the adaptation rate without hurting the robustness of the system. The performance achieved with the L1 controller is compared with that obtained via a linear state feedback controller for demanding reference signals in the presence of wind disturbances. Simulation results show that the performance of the L1 surpasses that of the linear controller illustrating the advantages of fast adaptation.

Sensor-based simultaneous localization and mapping — Part I: GAS robocentric filter

This paper presents the design, analysis, and experimental validation of a sensor-based globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM) with application to uninhabited aerial vehicles (UAVs). The SLAM problem is first formulated in a sensor-based framework, without any type of vehicle pose information, and modified in such a way that the underlying system structure can be regarded as linear time varying for observability, filter design, and convergence analysis purposes. Thus, a Kalman filter follows naturally with GAS error dynamics that estimates, in a robocentric coordinate frame, the positions of the landmarks, the velocity of the vehicle, and the bias of the angular velocity measurement. The online inertial map and trajectory estimation is detailed in a companion paper and follows from the estimation solution provided by the SLAM filter herein presented. The performance and consistency of the proposed method are successfully validated experimentally in a structured real world environment using a quadrotor instrumented platform.

Simultaneous localization and mapping for aerial vehicles: a 3-D sensor-based GAS filter

This paper presents the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM) with application to unmanned aerial vehicles. The main contributions of this paper are the results of global convergence and stability for SLAM in tridimensional (3-D) environments. The SLAM problem is formulated in a sensor-based framework and modified in such a way that the structure may be regarded as linear time-varying for observability purposes, from which a Kalman filter with GAS error dynamics follows naturally. The proposed solution includes the estimation of both body-fixed linear velocity and rate gyro measurement biases. Experimental results from several runs, using an instrumented quadrotor equipped with a RGB-D camera, are included in the paper to illustrate the performance of the algorithm under realistic conditions.

Sensor-based simultaneous localization and mapping — Part II: Online inertial map and trajectory estimation

A novel sensor-based filter for simultaneous localization and mapping (SLAM), featuring globally asymptotically stable error dynamics, is proposed in a companion paper, with application to uninhabited aerial vehicles (UAVs). This paper presents the second part of the algorithm, detailing a computationally efficient and numerically robust method for online inertial map and trajectory estimation based on the estimates provided by the SLAM filter previously derived. Central to the solution is the formulation of an optimization problem, that of finding the translation and the rotation that best explain the transformation between two sets of landmarks, with known associations, for consecutive time instants. The validation, performance, and consistency assessment of the proposed SLAM algorithm is successfully performed with real data, which was acquired by an instrumented quadrotor.

Terrain Avoidance Model Predictive Control for Autonomous Rotorcraft

Abstract This paper presents a terrain avoidance control methodology for autonomous rotorcraft applied to low altitude flight. A model predictive control formulation is used to adequately address the terrain avoidance problem, which involves stabilizing a nonlinear highly coupled dynamic model, while avoiding collisions with the terrain and preventing input and state saturations. Computing the model predictive control law amounts to solving a finite horizon open-loop optimal control problem subject to the state difference equations that describe the rotorcraft nonlinear dynamic model. State and input saturations are added to the optimization cost functional as penalties and terrain avoidance is achieved by penalizing the distance between the vehicle and the closest point on the terrain, yielding smooth and collision-free trajectories. Simulation results, obtained with a simplified version of a small-scale helicopter nonlinear dynamic model, are presented to assess the performance of the methodology with different reference paths and terrain profiles, including the extreme case where a desired path leads to collision with the terrain.