Sjriek Alers

OctoSLAM: A 3D mapping approach to situational awareness of unmanned aerial vehicles

The focus of this paper is on situational awareness of airborne agents capable of 6D motion, in particular multi-rotor UAVs. We propose the fusion of 2D laser range finder, altitude, and attitude sensor data in order to perform simultaneous localization and mapping (SLAM) indoors. In contrast to other planar 2D laser range finder based SLAM approaches, we perform SLAM on a 3D instead of a 2D map. To represent the 3D environment an octree based map is used. Our scan registration algorithm is derived from Hector SLAM. We evaluate the performance of our system in simulation and on a real multirotor UAV equipped with a 2D laser range finder, inertial measurement unit, and altitude sensor. The results show significant improvement in the localization and representation accuracy over current 2D map SLAM methods. The system is implemented using Willow Garages robot operating system.

How to Win RoboCup@Work?

In this paper we summarize how the Swarmlab@Work team has won the 2013 world championship title in the RoboCup@Work league, which aims to facilitate the use of autonomous robots in industry. The various techniques that have been combined to win the competition come from different computer science domains, entailing learning, (simultaneous) localization and mapping, navigation, object recognition and object manipulation. While the RoboCup@Work league is not a standard platform league, all participants used a (customized) Kuka youBot. The youBot is a ground based platform, capable of omnidirectional movement and equipped with a five degree of freedom arm featuring a parallel gripper.

Evaluation of an Experimental Framework for Exploiting Vision in Swarm Robotics

Visual feature detection with limited resources of simple robots is an essential requirement for swarm robotic systems. Robots need to localize their position, to determine their orientation, and need to be able to acquire extra information from their surrounding environment using their sensors, while their computational and storage capabilities might be very limited. This paper evaluates the performance of an experimental framework, in which environmental elements such as landmarks and QR-codes are considered as key visual features. The performance is evaluated for environmental light disturbances and distance variations and feature detection speed is thoroughly examined. The applicability of the approach is shown in a real robot scenario by using epuck robots. Finally, the results of applying the approach to a completely different setting, i.e., simulation of pheromones using glowing trail detection, are presented. These results indicate the broad applicability range of the developed feature detection techniques.

Bio-inspired multi-robot systems

Abstract In this chapter, we discuss team coordination in multi-robot systems inspired by the behavior of social insets such as ants and honeybees. Specifically, we study the application instances of ant-inspired robot coverage and bee-inspired robot foraging and pheromone signaling mechanisms and introduce some of our bio-inspired algorithms to deal with these problems. By robot coverage, we refer to the problem of deploying a robotic swarm in the environment with the task of maximizing the sensor coverage of the environment, and by robot foraging we refer to the problem of exploring the environment in search of food or provisions.