Predictive Collision Management for Time and Risk Dependent Path Planning

Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior in different simulation scenarios and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.Comment: Extended version of the SIGSPATIAL '20 pape

SLIM-A Scalable and Lightweight Indoor-Navigation MAV as Research and Education Platform

Indoor navigation with micro aerial vehicles (MAVs) is of growing importance nowadays. State of the art flight management controllers provide extensive interfaces for control and navigation, but most commonly aim for performing in outdoor navigation scenarios. Indoor navigation with MAVs is challenging, because of spatial constraints and lack of drift-free positioning systems like GPS. Instead, vision and/or inertial-based methods are used to localize the MAV against the environment. For educational purposes and moreover to test and develop such algorithms, since 2015 the so called droneSpace was established at the Institute of Computer Graphics and Vision at Graz University of Technology. It consists of a flight arena which is equipped with a highly accurate motion tracking system and further holds an extensive robotics framework for semi-autonomous MAV navigation. A core component of the droneSpace is a Scalable and Lightweight Indoor-navigation MAV design, which we call the SLIM (A detailed description of the SLIM and related projects can be found at our website: https://sites.google.com/view/w-a-isop/home/education/slim). It allows flexible vision-sensor setups and moreover provides interfaces to inject accurate pose measurements form external tracking sources to achieve stable indoor hover-flights. With this work we present capabilities of the framework and its flexibility, especially with regards to research and education at university level. We present use cases from research projects but also courses at the Graz University of Technology, whereas we discuss results and potential future work on the platform

A Reliable Open-Source System Architecture for the Fast Designing and Prototyping of Autonomous Multi-UAV Systems: Simulation and Experimentation

peer reviewedDuring the process of design and development of an autonomous Multi-UAV System, two main problems appear. The first one is the difficulty of designing all the modules and behaviors of the aerial multi-robot system. The second one is the difficulty of having an autonomous prototype of the system for the developers that allows to test the performance of each module even in an early stage of the project. These two problems motivate this paper. A multipurpose system architecture for autonomous multi-UAV platforms is presented. This versatile system architecture can be used by the system designers as a template when developing their own systems. The proposed system architecture is general enough to be used in a wide range of applications, as demonstrated in the paper. This system architecture aims to be a reference for all designers. Additionally, to allow for the fast prototyping of autonomous multi-aerial systems, an Open Source framework based on the previously defined system architecture is introduced. It allows developers to have a flight proven multi-aerial system ready to use, so that they can test their algorithms even in an early stage of the project. The implementation of this framework, introduced in the paper with the name of ``CVG Quadrotor Swarm'', which has also the advantages of being modular and compatible with different aerial platforms, can be found at \url{https://github.com/Vision4UAV/cvg_quadrotor_swarm} with a consistent catalog of available modules. The good performance of this framework is demonstrated in the paper by choosing a basic instance of it and carrying out simulation and experimental tests whose results are summarized and discussed in this paper

A Vision-based Quadrotor Multi-robot Solution for the Indoor Autonomy Challenge of the 2013 International Micro Air Vehicle Competition

peer reviewedThis paper presents a completely autonomous solution to participate in the 2013 International Micro Air Vehicle Indoor Flight Competition ({IMAV2013}). Our proposal is a modular multi-robot swarm architecture, based on the Robot Operating System (ROS) software framework, where the only information shared among swarm agents is each robot's position. Each swarm agent consists of an {AR Drone 2.0} quadrotor connected to a laptop which runs the software architecture. In order to present a completely visual-based solution the localization problem is simplified by the usage of ArUco visual markers. These visual markers are used to sense and map obstacles and to improve the pose estimation based on the IMU and optical data flow by means of an Extended Kalman Filter localization and mapping method. The presented solution and the performance of the CVG\_UPM team were awarded with the First Prize in the Indoors Autonomy Challenge of the {IMAV2013} competition