Lukas Goormann

An Unmanned Helicopter for Autonomous Flights in Urban Terrain

This work summarizes a multi-disciplinary research project, focusing on key enabling techniques towards true autonomous flight of small, low flying VTOL UAVs. Research activities cover the flying testbed, a simulation and testing environment, as well as integrated components for onboard navigation, perception, planning and control. Promising results and feasibility demonstrations in flight tests underline the successful domain specific enhancements of approaches based on aeronautical engineering, computer science and mobile robotics. The current approaches pave the way towards further research in improved flight control performance and more system autonomy when a-priori mission uncertainties increase.

Autonomous Vision-Based Helicopter Flights Through Obstacle Gates

The challenge for unmanned aerial vehicles to sense and avoid obstacles becomes even harder if narrow passages have to be crossed. An approach to solve a mission scenario that tackles the problem of such narrow passages is presented here. The task is to fly an unmanned helicopter autonomously through a course with gates that are only slightly larger than the vehicle itself. A camera is installed on the vehicle to detect the gates. Using vehicle localization data from a navigation solution, camera alignment and global gate positions are estimated simultaneously. The presented algorithm calculates the desired target waypoints to fly through the gates. Furthermore, the paper presents a mission execution plan that instructs the vehicle to search for a gate, to fly through it after successful detection, and to search for a proceeding one. All algorithms are designed to run onboard the vehicle so that no interaction with the ground control station is necessary, making the vehicle completely autonomous. To develop and optimize algorithms, and to prove the correctness and accuracy of vision-based gate detection under real operational conditions, gate positions are searched in images taken from manual helicopter flights. Afterwards, the integration of visual sensing and mission control is proven. The paper presents results from full autonomous flight where the helicopter searches and flies through a gate without operator actions.

Mapping and path planning in complex environments: An obstacle avoidance approach for an unmanned helicopter

This paper presents an obstacle avoidance method that is performed with an unmanned helicopter. The approach begins with a mapping step where information from sensor data about previously unknown dangers is extracted into an occupancy grid and eventually converted into a polygonal 3D world model. This continuously updating map is used by a path planner that generates and updates a 3D trajectory guiding the vehicle through safe passages around the detected objects. The algorithms are generic but optimized for unmanned aircraft and a stereo camera as the environmental sensor. Computation is fully executed on board so that a ground control station is only needed for supervision. With successful obstacle detection and avoidance flight tests, the paper shows the qualification of the presented method under real operational conditions.

Certification and Software Verification Considerations for Autonomous Unmanned Aircraft

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 betwee...

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.

Combined grid and feature-based occupancy map building in large outdoor environments

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.

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.

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.