A mission control system for an autonomous underwater vehicle

The presented work focuses on the theoretical and practical aspects concerning the design and development of a formal method to build a mission control system for autonomous underwater vehicles bringing systematic design principles for the formal description of missions using Petri nets. The proposed methodology compounds Petri net building blocks within it to de_ne a mission plan for which it is proved that formal properties, such as reachability and reusability, hold as long as these same properties are also guaranteed by each Petri net building block. To simplify the de_nition of these Petri net blocks as well as their composition, a high level language called Mission Control Language has been developed. Moreover, a methodology to ensure coordination constraints for teams of multiple robots as well as the de_nition of an interface between the proposed system and an on-board planner able to plan/replan sequences of prede_ned mission plans is included as well. Results of experiments with several real underwater vehicles and simulations involving an autonomous surface craft and an autonomous underwater vehicles are presented to show the system's capabilities.

Optimal kinematic control of an autonomous underwater vehicle

This note investigates the motion control of an autonomous underwater vehicle (AUV). The AUV is modeled as a nonholonomic system as any lateral motion of a conventional, slender AUV is quickly damped out. The problem is formulated as an optimal kinematic control problem on the Euclidean Group of Motions SE(3), where the cost function to be minimized is equal to the integral of a quadratic function of the velocity components. An application of the Maximum Principle to this optimal control problem yields the appropriate Hamiltonian and the corresponding vector fields give the necessary conditions for optimality. For a special case of the cost function, the necessary conditions for optimality can be characterized more easily and we proceed to investigate its solutions. Finally, it is shown that a particular set of optimal motions trace helical paths. Throughout this note we highlight a particular case where the quadratic cost function is weighted in such a way that it equates to the Lagrangian (kinetic energy) of the AUV. For this case, the regular extremal curves are constrained to equate to the AUV's components of momentum and the resulting vector fields are the d'Alembert-Lagrange equations in Hamiltonian form.

Bioinspired autonomous visual vertical control of a quadrotor unmanned aerial vehicle

Near-ground maneuvers, such as hover, approach, and landing, are key elements of autonomy in unmanned aerial vehicles. Such maneuvers have been tackled conventionally by measuring or estimating the velocity and the height above the ground, often using ultrasonic or laser range finders. Near-ground maneuvers are naturally mastered by flying birds and insects because objects below may be of interest for food or shelter. These animals perform such maneuvers efficiently using only the available vision and vestibular sensory information. In this paper, the time-tocontact (tau) theory, which conceptualizes the visual strategy with which many species are believed to approach objects, is presented as a solution for relative ground distance control for unmanned aerial vehicles. The paper shows how such an approach can be visually guided without knowledge of height and velocity relative to the ground. A control scheme that implements the tau strategy is developed employing only visual information from a monocular camera and an inertial measurement unit. To achieve reliable visual information at a high rate, a novel filtering system is proposed to complement the control system. The proposed system is implemented onboard an experimental quadrotor unmannedaerial vehicle and is shown to not only successfully land and approach ground, but also to enable the user to choose the dynamic characteristics of the approach. The methods presented in this paper are applicable to both aerial and space autonomous vehicles.

Non-linear Control of an Autonomous Ground Vehicle

In this paper, in order to select a speed controller for a specific non-linear autonomous ground vehicle, proportional-integral-derivative (PID), Fuzzy, and linear quadratic regulator (LQR) controllers were designed. Here, in order to carry out the tuning of the above controllers, a multicomputer genetic algorithm (MGA) was designed. Then, the results of the MGA were used to parameterize the PID, Fuzzy and LQR controllers and to test them under laboratory conditions. Finally, a comparative analysis of the performance of the three controllers was conducted.

Autonomous Landing of an Unmanned Aerial Vehicle using Image-Based Fuzzy Control

This paper presents a vision based autonomous landing control approach for unmanned aerial vehicles (UAV). The 3D position of an unmanned helicopter is estimated based on the homographies estimated of a known landmark. The translation and altitude estimation of the helicopter against the helipad position are the only information that is used to control the longitudinal, lateral and descend speeds of the vehicle. The control system approach consists in three Fuzzy controllers to manage the speeds of each 3D axis of the aircraft s coordinate system. The 3D position estimation was proven rst, comparing it with the GPS + IMU data with very good results. The robust of the vision algorithm against occlusions was also tested. The excellent behavior of the Fuzzy control approach using the 3D position estimation based in homographies was proved in an outdoors test using a real unmanned helicopter.

In situ assessment of initial seafloor massive sulfide mining operation in Papua New Guinea using a cabled autonomous underwater vehicle system: a proposition

Senior thesis written for Oceanography 445

The autonomous navigation of an electric vehicle

A navigation and positioning system for an electric automatic guided vehicle has been designed and implemented on an industrial pallet truck. The system includes an optical sensor mounted on the vehicle, capable of recognizing special markers at a distance of 0.3m. Software implemented in a z-80 microprocessor controls the sensor, performs all data processing and contains the decision making processes necessary for the vehicle to navigate its way to its task location. A second microprocessor is used to control the vehicle's drive motors under instruction from the navigation unit, to accurately position the vehicle at its destination. The sensor reliably recognises markers at vehicle speeds up to 1ms- 1, and the system has been integrated into a multiprocessor controlled wire-guidance system and applied to a prototype vehicle.

Automation of an underground mining vehicle using reactive navigation and opportunistic localization

This paper describes the implementation of an autonomous navigation system onto a 30 tonne Load-Haul-Dump truck. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made - a technique we refer to as opportunistic localization. The truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Air vehicle simulator : An application for a cable array robot

The development of autonomous air vehicles can be an expensive research pursuit. To alleviate some of the financial burden of this process, we have constructed a system consisting of four winches each attached to a central pod (the simulated air vehicle) via cables - a cable-array robot. The system is capable of precisely controlling the three dimensional position of the pod allowing effective testing of sensing and control strategies before experimentation on a free-flying vehicle. In this paper, we present a brief overview of the system and provide a practical control strategy for such a system. ©2005 IEEE.

Reactive navigation and opportunistic localization for autonomous underground mining vehicles

This paper describes an autonomous navigation system for a large underground mining vehicle. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made – a technique we refer to as opportunistic localization. The paper briefly reviews absolute and relative navigation strategies, and describes an implementation of a reactive navigation system on a 30 tonne Load-Haul-Dump truck. This truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Vision-based navigational competencies for a car-like vehicle

If mobile robots are to perform useful tasks in the real-world they will require a catalog of fundamental navigation competencies and a means to select between them. In this paper we describe our work on strongly vision-based competencies: road-following, person or vehicle following, pose and position stabilization. Results from experiments on an outdoor autonomous tractor, a car-like vehicle, are presented.