Combining Occupancy Grids with a Polygonal Obstacle World Model for Autonomous Flights

This chapter presents a mapping process that can be applied to autonomous systems for obstacle avoidance and trajectory planning. It is an improvement over commonly applied obstacle mapping techniques, such as occupancy grids. Problems encountered in large outdoor scenarios are tackled and a compressed map that can be sent on low-bandwidth networks is produced. The approach is real-time capable and works in full 3-D environments. The efficiency of the proposed approach is demonstrated under real operational conditions on an unmanned aerial vehicle using stereo vision for distance measurement

A Remote Test Pilot Control Station for Unmanned Research Aircraft

First-person-view ground control stations are an alternative to overcome the drawbacks of an external remote pilot with direct visual line of sight during flight-testing of unmanned aircraft systems. In this paper, a remote test pilot control station with first-person-view for advanced flight-testing is presented. The remote test pilot control station is developed for the German Aerospace Center's ALAADy (Automated Low Altitude Air Delivery) demonstrator aircraft, a gyroplane with a maximum take-off mass of 450 kg. The paper focusses on the system design of the remote test pilot control station, which has to overcome three major challenges: fault tolerance and reliability of the system, the pilot's situational and spatial awareness and latency. The remote test pilot control station is evaluated by pilot-in-the-loop simulations within a dedicated simulation environment. Objective performance criteria as well as subjective pilot ratings based on the Cooper-Harper rating scale are used to assess the control station for the ALAADy-demonstrator in direct mode and flight controller assisted mode. The simulation results show that pilots with experience in manned gyroplanes can consistently control the ALAADy demonstrator with the remote test pilot control station in ideal windless conditions. However, in more challenging crosswind conditions, pilot induced oscillations can be observed in direct mode

Objektorientierte Bildverarbeitungsalgorithmen zum relativen Hovern eines autonomen Helikopters

In Rahmen dieser Diplomarbeit werden Strategien und Bildverabeitungsalgo-rithmen zur Realisierung des „relativen Hoverns“ eines autonomen Kleinhelikopters untersucht. Unter relativem Hovern versteht man dabei das Positionhalten, relativ zu Objekten und Untergründen, die von einer Kamera aufgenommen werden. Insbesondere wurden Blob-Analyse-Algorithmen zu dieserProblemstellung entwickelt und geprüft, ob und in wie weit die Blob-Analyse zur Bestimmung der Eigenbewegung geeignet ist. Des Weiteren wurden ein objektorientiertes Framework, Konzepte und Applikationen zum Realisieren und Testen von Bildverarbeitungsalgorithmen auf verschiedenen Plattformen erstellt. Mit Hilfe dieses Frameworks wurden auch einige der Blob-Analyse-Algorithmen implementiert. Erste Tests haben ergeben, dass sich die Blob-Analyse zum „relativen Hovern“ unter „Echtzeit“-Bedingungen gut eignet, da sie sehr schnell und dabei ähnlich genau wie andere zur Zeit verwendete Algorithmen ist

Steps Towards Scalable and Modularized Flight Software for Unmanned Aircraft Systems

Unmanned aircraft (UA) applications impose a variety of computing tasks on the on-board computer system. From a research perspective, it is often more convenient to evaluate algorithms on bigger aircraft as they are capable of lifting heavier loads and thus more powerful computational units. On the other hand, smaller systems are often less expensive and operation is less restricted in many countries. This paper thus presents a conceptual design for flight software that can be evaluated on the UA of convenient size. The integration effort required to transfer the algorithm to different sized UA is significantly reduced. This scalability is achieved by using exchangeable payload modules and a flexible process distribution on different processing units. The presented approach is discussed using the example of the flight software of a 14 kg unmanned helicopter and an equivalent of 1.5 kg. The proof of concept is shown by means of flight performance in a hardware-in-the-loop simulation

A Fast and Small 3-D Obstacle Model for Autonomous Applications

This paper presents a mapping process that can be used for autonomous applications like obstacle avoidance and trajectory planning. The process is real-time capable, and works in full 3-D environments. The mapping starts with building an occupancy grid out of sensor data. Within this grid, single objects are recognized and their polygonal shapes are calculated. The model for object shape representation is rather rough and uses only right prisms and horizontal floor planes. This makes the shape calculation very fast. The extracted objects are not complex so that an external application will be fast as well. As a special application, the approach is tested on an aerial vehicle using a stereo camera system

Stereo-Based Obstacle Mapping from a Helicopter Platform

This paper presents a part of the sense and avoid system of an unmanned helicopter, that describes the process of creating three-dimensional obstacle maps used by a collision avoidance system. Classical robotic approaches are combined with aircraft-specific techniques and extended with new features to meet the requirements of flight scenarios. The maps are created and updated onboard and in real-time, and it is possible to integrate a-priori knowledge and the data of other sensors or vehicles. Furthermore, the map is adaptive so that the vehicle is not restricted at all, e.g. to a pre-defined area

Choosing death in cases of anorexia nervosa. Should we ever let people die from anorexia nervosa?

This paper presents an approach to create three-dimensional occupancy maps from an aerial vehicle with stereo vision. The main idea is to create an occupancy grid that moves along with the vehicle and extract features into a fixed global map. Vice versa, global features or a-priori knowledge can be inserted into the grid. The maps are calculated onboard to be used for autonomous behavior like path planning and obstacle avoidance. With the described method, maps are created and updated in real-time, and due to its flexibility, the vehicle is not restricted to a pre-defined area. The developed approach has been demonstrated in flights with a small unmanned helicopter

A System for Vision-Based Flights in Unknown Urban Environments

This paper presents a process to create threedimensional obstacle maps for collision avoidance, it is a part of the sense and avoid system of an unmanned helicopter. Classical robotic approaches like occupancy grid mapping and polygonal feature extraction are combined with aircraft-specific techniques and extended with new features to meet the requirements of flight scenarios. For example, the created map covers full 3-D scenarios, it is not restricted to a pre-defined area to be applicable to large outdoor environments, and the output is very compressed to be sent over network. The maps are created and updated onboard and in real-time, and it is possible to integrate a-priori knowledge and the data of other sensors or vehicles