An Unmanned Helicopter for Autonomous Flights in Urban Terrain

Autonomous aviation continuously becomes more and more important. Algorithms that enable this autonomy have developed quickly in the last years. This paper describes a concept for a reactive path planning algorithm. The aim is to develop a method for static obstacle avoidance of an unmanned aerial vehicle (UAV) by calculating collision-free paths within the field of view of a UAV's obstacle detection sensor. In contrast to other algorithms, this method considers the properties of the obstacle detection sensors, plans paths that the UAV is able to track, and is applied in three-dimensional space without access to an inner loop controller. In this work we represent the field of view of a UAV as a grid map and apply the wavefront algorithm as the local path planning algorithm. We reduce the configuration space of UAVs within the field of view by calculating an approximated worst-case reachable set based on a linearized reference model. We evaluate the method with approximated specifications for the unmanned helicopters ARTIS and Yamaha RMAX, and with specifications

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“…Design parameters are given in [3]. For the detection of obstacles in daylight a stereo camera that generates depth images is currently applied.…”

Section: Helicopter Constraintsmentioning

confidence: 99%

Model-Based Local Path Planning for UAVs

Hebecker

Buchholz

Ortmeier

2014

J Intell Robot Syst

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“…Whereas this work focuses on software verification and certification issues, a general overview about ARTIS can be found in [43], where the following onboard software components and functionalities for decisional autonomy are also summarized: 1) payload-directed mission elements (vision-based gate passing and vision-based ground vehicle tracking) [44], 2) real-time world modeling and path replanning [45,46], 3) online exploration behavior in urban terrain [47] (Fig. 2), 4) usage of a large urban terrain database (city of Berlin) [48], 5) composite flight behaviors that solve abstract guidance tasks (search pattern planning and safe perimeter tracking) [49], 6) trajectory following of linear segments and cubic splines [50], and 7) evaluation of the roadmap-based path planning (performance, smoothness, and safety) [51] (Fig.…”

Section: Artis Uav Fleetmentioning

confidence: 99%

“…A new mission was sent to the UAV. As a direct [43], which can be optionally integrated into the aforementioned SIL or HIL simulation setup.…”

Section: Artis Simulation and System Integration Approachmentioning

confidence: 99%

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Certification and Software Verification Considerations for Autonomous Unmanned Aircraft

Torens

Adolf

Goormann

2014

Journal of Aerospace Information Systems

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Software verification for highly automatic unmanned aerial vehicles is not only a problem itself, it is furthermore constrained by certification standards and regulatory rules. These, however, are themselves still under development. As a top-level view, the current status of unmanned aerial vehicle verification, certification, and regulation is addressed and corresponding challenges are discussed. From a low-level view, this work presents the processes and tools that were established for the software development, verification, and validation of the unmanned rotorcraft software testbed ARTIS. Large efforts have been put into the software verification process to cope with the growing complexity of the autonomous system and the validation of the software behavior. Automated tests drive the development of the mission planning, mission management, and sensor fusion systems. High-level behavior is tested by complex simulation scenarios. To connect the aforementioned top-and low-level views, a comparison between the RTCA DO-178C standard ("Software Considerations in Airborne Systems and Equipment Certification") and corresponding ARTIS software development practices is elaborated to assess the efforts that would be necessary for a small research team to develop software according to the standard. It shows that the currently used practices are not incompatible, but there are still some gaps to the desired level of compliance.

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“…A mission planner for high level mission planning problems such as for example described in Ref. 2 for the application with the ARTIS helicopter solves high level task planning problems and returns a concatenation of behaviors -the so called behavior sequence -which are executed by the mission manager. Thus, the mission manager needs to provide an interface to receive operator inputs and mission commands at different levels of abstraction.…”

Section: Introductionmentioning

confidence: 99%

Behavior-Based Onboard Mission Management for an Unmanned Fixed-Wing Aircraft

Niendorf

Adolf

Gerhard

2012

Infotech@Aerospace 2012

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This paper presents a behavior-based onboard mission management system for the Prometheus unmanned fixed-wing aircraft. The mission manager allows for interaction with the unmanned aircraft at various levels of abstraction ranging from behavior commands to remote controlled flight. The use of behaviors helps to assure a modular mission management concept. As the aircraft cannot hover, transitions between subsequent behaviors are computed onboard by path planning modules with respect to the constraints of the fixed-wing aircraft. The presented mission manager is derived from previous work considering autonomous helicopters. The concept of composite behaviors was also used in the presented system in order to extend the flexibility of the approach. The architecture of the mission manager is discussed in detail. Additionally, a Monte Carlo simulation illustrates the short computation time of the transition path planning. The execution of a mission plan is demonstrated using a software-in-the-loop simulation.

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An Unmanned Helicopter for Autonomous Flights in Urban Terrain

Cited by 36 publications

References 20 publications

Model-Based Local Path Planning for UAVs

Model-Based Local Path Planning for UAVs

Certification and Software Verification Considerations for Autonomous Unmanned Aircraft

Behavior-Based Onboard Mission Management for an Unmanned Fixed-Wing Aircraft

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