Abstract—This paper addresses the problem of coordinating multiple spacecraft to fly in tightly controlled formations. The main contribution of the paper is to introduce a coordination architecture that subsumes leader-following, behavioral, and virtual-structure approaches to the multiagent coordination problem. The architecture is illustrated through a detailed application of the ideas to the problem of synthesizing a multiple spacecraft interferometer in deep space. Index Terms—Control architecture, coordinated control, interferometry, spacecraft formation flying. I.

We describe a dataset of several thousand calibrated, time-stamped, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination conditions in an outdoor region spanning several hundred meters. The image data is grouped into several regions which have little mutual inter-visibility. For each group, the calibration data is globally consistent on average to roughly five centimeters and 0.1 # , or about four pixels of epipolar registration. All image, feature and calibration data is available for interactive inspection and downloading at http://city.lcs.mit.edu/data.

For autonomous helicopter flight, it is common to separate the flight control problem into an innerloop that controls attitude and an outerloop that controls the trajectory of the helicopter. The outerloop generates attitude commands that orient the main rotor forces appropriately to generate required translational accelerations. Recent work in Neural Network based adaptive flight control may be applied to control a helicopter where the reference commands include position, velocity, attitude and angular rate. The outerloop is used to correct the commanded attitude in order to follow position and velocity commands. This however generally requires a model of the translational dynamics which has some model error. This paper introduces adaptation in the outerloop using Pseudo Control Hedging in a way that prevents adaptation to the innerloop dynamics. Additionally, hedging is used in the innerloop to avoid incorrect adaptation while at control limits. Such an approach along with correct placement of the combined poles of the linearized system mitigates inner/outer loop interaction problems and allows one to increase bandwidth in the outerloop, thus, improving tracking performance further.

This article covers research work about unmanned helicopters that is being developed by the Portuguese SME IntRoSys, S.A. in the context of a generic project targeting the development of a Multi-Robot system for landmine detection. As helicopters are complex multivariable nonlinear underactuated systems with strong coupling in some control loops, they are an interesting subject of research for the control and robotic research communities. Therefore, a wide range of techniques have been proposed in the literature to control such vehicles, ranging from classic control to soft-computing methods. The main goal for this article is to present a survey about existing low level control architectures for unmanned helicopters and to discuss which control design criteria (design requirements) are relevant when they are operating in the context of Humanitarian Demining.

cloning for the development of low-level helicopter automation modules. Over the course of this project several Behavioural cloning approaches have been investigated. The results of the most effective Behavioural cloning approach are then compared to PID modules designed for the same aircraft. The comparison takes into consideration development time, reliability, and control performance. It has been found that Behavioural cloning techniques employing local approximators and a wide state-space coverage during training can produce stabilising control modules in less time than tuning PID controllers. However, performance and reliabity deficits have been found to exist with the Behavioural Cloning, attributable largely to the time variant nature of the dynamics due to the operating environment, and the pilot actions being poor for teaching. The final conclusion drawn here is that tuning PID modules remains superior to behavioural cloning for low-level helicopter automation.

Abstract — Perch and stare is a maneuver where a vehicle flies to an overhead vantage point to provide a user with improved tactical information. This may include landing on rooftops, flying from rooftop to rooftop, or to windowsills all while carrying cameras or other intelligence gathering sensors. Miniature rotorcraft are ideal surveillance platforms, especially for perch and stare maneuvers because of their unique ability to take off and land vertically. Minimal vehicle size and weight also greatly enhance portability. Real world environmental influences such as fog, smoke, wind and cluttered or moving landing areas greatly complicate perch and stare maneuvers. This paper describes the application of optic flow and ultrasonic range finding sensors to increase miniature robotic rotorcraft autonomy for perch and stare maneuvers, especially in degraded environments.

Abstract—In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether. Index Terms—Attitude control, position control, robustness, tethered helicopter. I.

This paper details the development of an online adaptive control system, designed to learn from the actions of an instructing pilot. Three learning architectures, single layer neural networks (SLNN), multi-layer neural networks (MLNN), and fuzzy associative memories (FAM) are considerd. Each method has been tested in simulation. While the SLNN and MLNN provided adequate control under some simulation conditions, the addition of pilot noise and pilot variation during simulation training caused these methods to fail. FAMs alone were found to be suitable under all simulation conditions. The performance of an FAM velocity control being used by a higher level waypoint controller in simulation is presented. As a first step toward performing real helicopter tests, an FAM attitude controller has also been developed in simulation, and its tracking performance presented. of the non-conventional studies have moved beyond simulations. While many of the neuro/fuzzy approaches incorporate some form of limited authority, online adaption, the majority of control authority is learnt offline. This offline controller learning has been performed using actor/critic type reinformcement learning [8], policy search methods based on MDP models built from flight data [2], and back propagation of errors through neural forward models [10]. Only two methods have used pilot control signals to directly train a feedback controller [14, 5]. While the FAMs in [14] showed promise in simulation, the controllers failed to stabilise the real helicopter. The MLNN in [5], on the other hand, managed to stabilise the real helicopter for brief periods, however later investigations showed that the system had many hidden, potential instabilities [3, 4]. 1