Safe flying for an UAV helicopter

IEEE Robotics & Automation Magazine, 2012

rban search and rescue missions raise special requirements on robotic systems. Small aerial systems provide essential support to human task forces in situation assessment and surveillance. As external infrastructure for navigation and communication is usually not available, robotic systems must be able to operate autonomously. A limited payload of small aerial systems poses a great challenge to the system design. The optimal tradeoff between flight performance, sensors, and computing resources has to be found. Communication to external computers cannot be guaranteed; therefore, all

2012 International Conference of Robotics and Artificial Intelligence, 2012

In this paper we discuss the design and development of highly cost effective, semi-autonomous reconnaissance Unmanned Aerial Vehicle (UAV) for safe flights in close environments, with real time video feedback. The design and development is based on the modifications/up gradations, (predicated on the results of several small experiments), of a very low cost, small sized -toy helicopter, and a simple non-linear control system designed for the intelligent UAV features. The developed UAV helicopter has been successfully test-flown at higher altitudes, with heavier payload, for longer period as compared to similar helicopters developed at much higher costs.

IEEE Transactions on Industrial Electronics, 2011

This paper presents the architecture of an onboard controller developed for the HERO autonomous helicopter, which is a low-cost unmanned aerial vehicle research platform. An embedded digital-signal-processor-based low-level controller is devoted to flight control, while a PC-based high-level controller is used for onboard perception tasks and interaction with other agents in a distributed system. The functional design, software architecture, and implementation of the low-level controller are analyzed in detail, focusing mainly on its runtime environment (JULIET) and its capability for flexible reconfiguration. The connectivity functions of the low-level controller with external possibly distributed agents are also addressed. Finally, the results of real autonomous flight experiments are presented, including the tracking of a smooth 3-D path described by over two hundred waypoints. Index Terms-Aerial robotics, digital signal processor (DSP), embedded system, unmanned aerial vehicle (UAV). I. INTRODUCTION U NMANNED AERIAL VEHICLES (UAVs) have become a fundamental area in the field of robotics research during the last 15 years; in this time span, they have evolved from prototypes to real working systems. Although military applications have been a fundamental drive for their success, there also exists a wide set of civil applications where they have shown their usefulness, such as forest fire detection and monitoring, disaster management, inspection of inaccessible structures, or traffic surveillance. Over the years, the focus of research has expanded from the development of hardware platforms and basic control approaches [1]-[8] to the design of fault-tolerant systems, which are able to deal with uncertain environments and react to unexpected events. There is also a trend to integrate UAVs in complex distributed systems to cooperate with other aerial or ground robots [9], [10] and wireless sensor networks,

[1993 Proceedings] AIAA/IEEE Digital Avionics Systems Conference, 1993

The Georgia Tech Aerial Robotics Team has been working on autonomous control of an unmanned aerial vehicle for three years as both a research project and a competition entry. The three annual International Autonomous Aerial Vehicle Competitions (1 991 -93) have attracted university teams from across the country as well as from Europe, Canada, and Asia. At the third annual competition in June 1993, the Georgia Tech system was awarded first place for demonstrating completely autonomous flight including a safe landing. This success is a result of overcoming numerous challenges faced while creating a guided automatic flight control system for a small unmanned vehicle. The 1993 entry was refined through early and consistent test flights and the application of lessons learned from previous years. The Georgia Tech Aerial Robotics System (GTARS) features an on-board flight control computer, a six axis sensor suite, an off-board mission planning station, a vision tracking system, and a modified model helicopter of conventional configuration. Other features of the system include remote pilot control with safety override switch, two way telemetry data link, and a tethered autonomous sub-vehicle.

AIAA Proceedings.[np]. …, 2009

… and Automation, 2009 …, 2009

The development of a vision system to aid the autonomous navigation of an unmanned helicopter, primarily based on inertial sensors and GPS data, is presented. An unmanned helicopter has been equipped with appropriate sensors and a vision system fitted on a custom pan-tilt mechanism. Robust software, based on the Open Computer Vision Library (OpenCV), has been developed for handling images and video from a camera. Our implementation involves real time object recognition, histogram matching for real time video streaming, pattern matching and object tracking. Software implemented in C++ interacts with Matlab in order to aid the autonomous navigation of the helicopter.

2007

The Pima Community College UAV Club has designed an autonomous aerial vehicle system to compete in the International Aerial Robotics Competition (IARC). A primary air vehicle carries two cooperating subvehicles to perform the IARC mission, which consists of identifying a building, identifying and entering a portal in the building, and searching for a target inside the building. Vehicles are linked to ground-based computers, and the entire system is required to be completely autonomous.

2007 IEEE International Workshop on Safety, Security and Rescue Robotics, 2007

1994

We present an overview of the autonomous helicopter project at Carnegie Mellon's Robotics Institute. The goal of this project is to autonomously fly helicopters using com- puter vision closely integrated with other on-board sen- sors. We discuss a concrete example mission designed to demonstrate the viability of vision-based helicopter flight and specify the components necessary to accomplish this mission. Major

The utilization of unmanned vehicles has become increasingly more popular today and been successfully demonstrated for various civil and military applications. The unmanned aerial vehicles (UAVs) have shown applications in different areas including crop yield prediction, land use surveys in rural and urban regions, traffic surveillance and weather research. The unmanned small scale helicopters are particularly suitable for demanding problems which requires accurate low-speed maneuver and hovering capabilities such as detailed area mapping. Generally a certain level of autonomous flight capability is required for the vehicle to achieve its mission. The basic autonomy level is to maintain its stability following a desired path under embedded guidance, navigation and control algorithm. The UAV technology trends indicate that to cope with the more stringent operation requirements, the UAVs should rely less and less on the skill of the ground pilot and progressively more on the autonomous capabilities dictated by a reliable onboard computer system. To systematically develop and enhance flight autonomy, a rotary wing UAV (RUAV) or model helicopter has been proposed and used as a flying test-bed at various major research centers. The ability of the helicopter to operate in the hovering mode makes it an ideal platform for a step-by-step autonomous capability development. On the other hand, a small helicopter exhibits not only increased sensitivity to control inputs and disturbances, but also a much richer dynamics compared to conventional unmanned aerial vehicles (UAVs). The paper surveys recent advances in modeling, control and navigation of autonomous unmanned aerial vehicles. Without loss of generality, an autonomous small scale helicopter research program is taken as a case study. Approaches to modeling and control for such a vehicle are presented and discussed. Future directions in the advancement of UAV technologies are identified and key barriers highlighted.