José R. H. Carvalho

Visual servo control for the hovering of all outdoor robotic airship

Addresses the issue of automatic hovering of an outdoor autonomous airship using image-based visual servoing. The hovering controller is designed using a full dynamic model of the airship, in a PD error feedback scheme, taking the visual signals as output and extracted from an on-board camera. The behavior and stability of the airship motion during the task execution and subjected to the wind disturbance is studied. The approach is finally validated in simulation using an accurate airship model.

Optimal visual servoed guidance of outdoor autonomous robotic airships

This paper describes a visual servo control scheme for an outdoor autonomous airship in path following tasks. The visual based guidance takes full authority of the robotic airship and it is based on lines as image features, extracted from an on-board camera. An optimal controller is designed using the dynamic model of the system, which tackles the dynamics of the airship and the dynamics of the vision process. To prevent from losing image features during camera motion and to deal with noisy output measurements and modeling inaccuracies, a Kalman estimator was employed. The approach is validated through simulations using the underactuated airship of the AURORA project.

A fast vision-based road following strategy applied to the control of aerial robots

The utilization of the vision as a feedback sensor in closed-loop control schemes is of great interest, mainly due to the high density of information revealed by images. In this work, we present a method to perform road following tracking by aerial unmanned vehicles (AUV) based on visual input. Difficulties arise from the nonholonomic constraints of the AUV moving in 3D. The problems are overcome using a visual servoing approach based on an interaction matrix. Simulation results validating the strategy are shown.

Modelling, Control and Perception for an Autonomous Robotic Airship

Robotic unmanned aerial vehicles have an enormous potential as observation and data-gathering platforms for a wide variety of applications. These applications include environmental and biodiversity research and monitoring, urban planning and traffic control, inspection of man-made structures, mineral and archaeological prospecting, surveillance and law enforcement, communications, and many others. Robotic airships, in particular, are of great interest as observation platforms, due to their potential for extended mission times, low platform vibration characteristics, and hovering capability. In this paper we provide an overview of Project AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship), a research effort that focusses on the development of the technologies required for substantially autonomous robotic airships. We discuss airship modelling and control, autonomous navigation, and sensor-based flight control. We also present the hardware and software architectures developed for the airship. Additionally, we discuss our current research in airborne perception and monitoring, including mission-specific target acquisition, discrimination and identification tasks. The paper also presents experimental results from our work.

Lateral/directional control for an autonomous, unmanned airship

Project AURORA aims at the development of an unmanned airship capable of autonomous flight over user‐defined locations for aerial inspection and imagery acquisition. Presents a guidance control strategy for the trajectory path following of the AURORA airship, where the objective is to make the vehicle follow a set of pre‐defined points. The guidance strategy is based on a path tracking error generation methodology that takes into account both the distance and the angular errors of the airship with respect to the desired trajectory. The guidance system is composed of a path tracking guidance controller (as outer loop) and a heading controller (as inner loop), using the rudder deflection. Also proposes an additional roll controller, using the aileron input, in order to reduce rolling oscillations during yaw maneuvering and due to atmospheric turbulence.

Toward a perception and sensor fusion architecture for a robotic airship

Robotic unmanned aerial vehicles have an enormous potential as observation and data-gathering platforms for a wide variety of applications. This paper discusses components of a perception architecture being developed for AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship). The AURORA project focuses on the development of the technologies required for substantially autonomous unmanned aerial vehicles, and for robotic airships in particular. We describe our approach to spatial representation, which incorporates a Markov Random Field (MRF) model used for encoding spatial inferences obtained from sensor imagery. We present a dynamic approach to target recognition that uses a cycle of hypothesis formulation, experiment planning for hypothesis validation, experiment execution, and hypothesis evaluation to confirm or reject the classification of targets into object classes. We also discuss an approach to automatic hovering and landing using visual servoing techniques and interaction matrices, and present preliminary experimental results from our work.

Enhancement of Underwater Images in Low-to-High Turbidity Rivers

The demand for efficient enhancement methods of underwater images of the rivers in the Amazon region is increasing. However, most of those in the region present moderate turbidity and low luminosity. This work aims to improve these images by non-linear filtering techniques, which promote the minimization of light interaction characteristics with the environment, loss of the contrast and colors. The proposed method is compared with two others techniques that requires a unique image as input. The results of the proposed method is promising, with better visual quality considering a wide range of experiments with simulation data and real outdoor scenes.