A. Ollero

Multi-UAV Cooperation and Control for Load Transportation and Deployment

This paper deals with the cooperation and control of multiple UAVs with sensing and actuation capabilities. An architecture to perform cooperative missions with a multi-UAV platform is presented. The interactions between UAVs are not only information exchanges but also physical couplings required to cooperate in the joint transportation of a single load. Then, the paper also presents the control system for the transportation of a slung load by means of one or several helicopters. Experimental results of the load transportation system with one and three helicopters are shown. On the other hand, the UAVs considered in the platform can also deploy small objects, such as sensor nodes, on different locations if it is required. This feature along with the whole platform architecture are illustrated in the paper with a real multi-UAV mission for the deployment of sensor nodes to repair the connectivity of a wireless sensor network.

A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires

This paper presents a cooperative perception system for multiple heterogeneous unmanned aerial vehicles (UAVs). It considers different kind of sensors: infrared and visual cameras and fire detectors. The system is based on a set of multipurpose low-level image-processing functions including segmentation, stabilization of sequences of images, and geo-referencing, and it also involves data fusion algorithms for cooperative perception. It has been tested in field experiments that pursued autonomous multi-UAV cooperative detection, monitoring, and measurement of forest fires. This paper presents the overall architecture of the perception system, describes some of the implemented cooperative perception techniques, and shows experimental results on automatic forest fire detection and localization with cooperating UAVs.

Autonomous transportation and deployment with aerial robots for search and rescue missions

It is generally accepted that systems composed of multiple aerial robots with autonomous cooperation capabilities can assist responders in many search and rescue (SAR) scenarios. In most of the previous research work, the aerial robots are mainly considered as platforms for environmental sensing and have not been used to assist victims. In this paper, outdoor field experiments of transportation and accurate deployment of loads with single/multiple autonomous aerial vehicles are presented. This is a novel feature that opens the possibility to use aerial robots to assist victims during rescue phase operations. Accuracy in the deployment location is a critical issue in SAR scenarios in which injured people may have very limited mobility. The presented system is composed of up to three small-size helicopters and features cooperative sensing, using several different sensor types. The system supports several forms of cooperative actuation as well, ranging from the cooperative deployment of small sensors/objects to the coupled transportation of slung loads. The complete system is described, outlining the hardware and software framework used, as well as the approaches for modeling and control used. Additionally, the results of several flight field experiments are presented, including a description of the worldwide first successful autonomous load transportation experiment, using three coupled small-size helicopters (conducted in December 2007). During these experiments strong, steady winds and wind gusts were present. Various solutions and lessons learned from the design and operation of the system are also provided.

An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement

The paper presents an Unmanned Aircraft System (UAS), consisting of several aerial vehicles and a central station, for forest fire monitoring. Fire monitoring is defined as the computation in real-time of the evolution of the fire front shape and potentially other parameters related to the fire propagation, and is very important for forest fire fighting. The paper shows how an UAS can automatically obtain this information by means of on-board infrared or visual cameras. Moreover, it is shown how multiple aerial vehicles can collaborate in this application, allowing to cover bigger areas or to obtain complementary views of a fire. The paper presents results obtained in experiments considering actual controlled forest fires in quasi-operational conditions, involving a fleet of three vehicles, two autonomous helicopters and one blimp.

Experimental Results in Multi-UAV Coordination for Disaster Management and Civil Security Applications

This paper describes a multi-UAV distributed decisional architecture developed in the framework of the AWARE Project together with a set of tests with real Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) to validate this approach in disaster management and civil security applications. The paper presents the different components of the AWARE platform and the scenario in which the multi-UAV missions were carried out. The missions described in this paper include surveillance with multiple UAVs, sensor deployment and fire threat confirmation. In order to avoid redundancies, instead of describing the operation of the full architecture for every mission, only non-overlapping aspects are highlighted in each one. Key issues in multi-UAV systems such as distributed task allocation, conflict resolution and plan refining are solved in the execution of the missions.

Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

This paper proposes vision-based techniques for localizing an unmanned aerial vehicle (UAV) by means of an on-board camera. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. First, it is described a monocular visual odometer which could be used as a backup system when the accuracy of GPS is reduced to critical levels. Homography-based techniques are used to compute the UAV relative translation and rotation by means of the images gathered by an onboard camera. The analysis of the problem takes into account the stochastic nature of the estimation and practical implementation issues. The visual odometer is then integrated into a simultaneous localization and mapping (SLAM) scheme in order to reduce the impact of cumulative errors in odometry-based position estimation approaches. Novel prediction and landmark initialization for SLAM in UAVs are presented. The paper is supported by an extensive experimental work where the proposed algorithms have been tested and validated using real UAVs.

Control and perception techniques for aerial robotics

This paper review methods and technologies that have been applied in aerial robotics. The paper presents several unmanned aerial vehicle platforms. Then summarizes different control techniques including both control architectures and control methods. Furthermore, computer vision techniques for aerial robotics are briefly considered. Finally, the paper presents systems and projects involving multiple autonomous aerial and ground systems. # 2004 Published by Elsevier Ltd.

An intelligent system for false alarm reduction in infrared forest-fire detection

Forest fires cause many environmental disasters, creating economical and ecological damage as well as endangering peoples lives. Heightened interest in automatic surveillance and early forest-fire detection has taken precedence over traditional human surveillance because the latters subjectivity affects detection reliability, which is the main issue for forest-fire detection systems. In current systems, the process is tedious, and human operators must manually validate many false alarms. Our approach, the False Alarm Reduction system, proposes an alternative real-time infrared-visual system that overcomes this problem. The FAR system consists of applying new infrared-image processing techniques and artificial neural networks (ANNs), using additional information from meteorological sensors and from a geographical information database, taking advantage of the information redundancy from visual and infrared cameras through a matching process, and designing a fuzzy expert rule base to develop a decision function. Furthermore, the system provides the human operator with new software tools to verify alarms.

Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms

This paper focuses on the problem of cooperatively searching a given area to detect objects of interest, using a team of heterogenous unmanned air vehicles (UAVs). The paper presents algorithms to divide the whole area taking into account UAV’s relative capabilities and initial locations. Resulting areas are assigned among the UAVs, who could cover them using a zigzag pattern. Each UAV has to compute the sweep direction which minimizes the number of turns needed along a zigzag pattern. Algorithms are developed considering their computational complexity in order to allow near-real time operation. Results demonstrating the feasibility of the cooperative search in a scenario of the COMETS multi-UAV project are presented.

The autonomous mobile robot AURORA for greenhouse operation

AURORA has been conceived in order to substitute hard and unhealthy human work inside greenhouses by means of an autonomous mobile robot outfitted with appropriate sensors and operation devices. Emphasis has been put in the development of a new robotic platform specifically designed for greenhouse tasks, governed by a control architecture that supports both autonomous navigation and shared human control.