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Armin Hornung, Sebastian Boettcher, Christian Dornhege, Andreas Hertle, Jonas Schlagenhauf and Maren Bennewitz.
Mobile Manipulation in Cluttered Environments with Humanoids: Itegrated Perception, Task Planning, and Action Execution.
In
Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS).
2014.
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To autonomously carry out complex mobile manipulation tasks, a robot control system has to integrate several components for perception, world modeling, action planning and replanning, navigation, and manipulation. In this paper, we present a modular framework that is based on the Temporal Fast Downward Planner and supports external modules to control the robot. This allows to tightly integrate individual sub-systems with the high-level symbolic planner and enables a humanoid robot to solve challenging mobile manipulation tasks. In the work presented here, we address mobile manipulation with humanoids in cluttered environments, particularly the task of collecting objects and delivering them to designated places in a home-like environment while clearing obstacles out of the way. We implemented our system for a Nao humanoid tidying up a room, i.e., the robot has to collect items scattered on the floor, move obstacles out of its way, and deliver the objects to designated target locations. Despite the limited sensing and motion capabilities of the low-cost platform, the experiments show that our approach results in reliable task execution by applying monitoring actions to verify object and robot states.
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Nicolas Riesterer, Christian Becker-Asano, Julien Hué, Christian Dornhege and Bernhard Nebel.
The Hybrid Agent MARCO.
In
Proceedings of the 16th International Conference on Multimodal Interaction, pp. 80-81.
2014.
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We present MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and a virtual agent’s face displaying emotions. MARCO was built to investigate the hypothesis that hybrid agents capable of displaying emotions make playing chess more personal and enjoyable. In addition, we aim to explore means of achieving emotional contagion between man and machine.
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Christian Becker-Asano, Eduardo Meneses, Nicolas Riesterer, Julien Hué, Christian Dornhege and Bernhard Nebel.
The Hybrid Agent MARCO: A Multimodal Autonomous Robotic Chess Opponent.
In
Proceedings of the 2nd International Confernce on Human-Agent Interaction, pp. 173-176.
2014.
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We present MARCO, a hybrid, chess playing agent equipped with a custom-built robotic arm and a virtual agent’s face displaying emotions. MARCO was built to investigate the hypothesis that hybrid agents capable of displaying emo- tions make playing chess more personal and enjoyable. In addition, we aim to explore means of achieving emotional contagion between man and machine.
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Andreas Hertle, Christian Dornhege, Thomas Keller, Robert Mattmüller, Manuela Ortlieb and Bernhard Nebel.
An Experimental Comparison of Classical, FOND and Probabilistic Planning.
In
Proceedings of the 37th German Conference on Artificial Intelligence (KI 2014), pp. 297-308.
Springer 2014.
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Domain-independent planning in general is broadly applicable to a wide range of tasks.
Many formalisms exist that allow to describe different aspects of realistic problems.
Which one is best is not clear as usually more expressiveness comes with a cost in
planning time. Under the assumption that hard guarantees are not required, users are
faced with a decision between multiple approaches. In this work we study the effect of
nondeterministic action outcomes. As a generic model we use a probabilistic description
in the form of Markov Decision Processes (MDPs). We define abstracting translations
into a classical planning formalism and fully observable nondeterministic (FOND)
planning. Our goal is to give insight into how state-of-the-art systems perform on
different MDP planning domains. We evaluate those MDPs by running state-of-the-art
planning systems on the abstracted formalisms.
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Christian Dornhege, Andreas Hertle and Bernhard Nebel.
Lazy Evaluation and Subsumption Caching for Search-Based Integrated Task and Motion Planning.
In
Proceedings of the IROS workshop on AI-based robotics.
2013.
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State of the art classical planning systems can efficiently solve large symbolic problem instances. Applying classical planning techniques to robotics is possible by in- tegrating geometric reasoning in the planning process. The problems that are solvable in this way are significantly smaller than purely logical formulations as many costly geometric calculations are requested by a planner. Therefore we aim to avoid those calculations while preserving correctness.
We address this problem with efficient caching techniques. Subsumption caching avoids costly computations by caching geometric queries and beyond answering the same queries also considers less or more constrained ones. Additionally, we describe a lazy evaluation technique that pushes applicability checks for successor states performing geometric queries to a later point. As we are interested in the performance of our planner not as a standalone component, but as part of an intelligent robotic system, we evaluate those techniques embedded in an integrated system during real-world mobile manipulation experiments.
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Christian Dornhege, Alexander Kleiner and Andreas Kolling.
Coverage Search in 3D.
In
Proceedings of the Symposium on Safety Security and Rescue Robotics (SSRR).
2013.
(PDF)
(BIB)
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Andreas Hertle and Christian Dornhege.
Efficient Extensible Path Planning on 3D Terrain Using Behavior Modules.
In
Proceedings of the European Conference on Mobile Robotics (ECMR).
2013.
(PDF)
(BIB)
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Bernhard Nebel, Christian Dornhege and Andreas Hertle.
How Much Does a Household Robot Need To Know In Order To Tidy
Up Your Home?
In
AAAI Workshop on Intelligent Robotic Systems.
AAAI Press 2013.
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Although planning for the tasks a household robot has to perform appears to be easy, there exists the problem that the robot is usually uncertain about the state of the household when starting to plan. For example, when getting the order of tidying up the kitchen, the robot does not know what objects it will have to put away and whether there are actually any objects that need to be put away. Furthermore, while sensing operations can provide more information about the environment, things can go wrong when executing an action.
In this paper, we try to identify conditions under which classical planning can be used in a replanning loop in order to solve the planning problem in nondeterminis- tic partially observable open domains. In particular, we will define completeness and soundness of replanning with respect to nondeterministic planning and we will identify a PSPACE-checkable condition that guarantees soundness.
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Christian Dornhege and Andreas Hertle.
Integrated Symbolic Planning in the Tidyup-Robot Project.
In
AAAI Spring Symposium - Designing Intelligent Robots: Reintegrating AI II.
AAAI Press 2013.
(PDF)
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Christian Dornhege and Alexander Kleiner.
A Frontier-Void-Based Approach for Autonomous Exploration in 3D.
Advanced Robotics 27 (6). 2013.
(BIB)
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Kai M. Wurm, Christian Dornhege, Cyrill Stachniss, Bernhard Nebel and Wolfram Burgard.
Coordinating Heterogeneous Teams of Robots using Temporal Symbolic Planning.
Autonomous Robots. 2013.
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(Online; DOI)
The efficient coordination of a team of heterogeneous robots is an important requirement for exploration, rescue, and disaster recovery missions. In this paper, we present a novel approach to target assignment for heterogeneous teams of robots. It goes beyond existing target assignment algorithms in that it explicitly takes symbolic actions into account. Such actions include the deployment and retrieval of other robots or manipulation tasks. Our method integrates a temporal planning approach with a traditional cost-based planner. The proposed approach was implemented and evaluated in two distinct settings. First, we coordinated teams of marsupial robots. Such robots are able to deploy and pickup smaller robots. Second, we simulated a disaster scenario where the task is to clear blockades and reach certain critical locations in the environment. A similar setting was also investigated using a team of real robots. The results show that our approach outperforms ad-hoc extensions of state-of-the-art cost-based coordination methods and that the approach is able to efficiently coordinate teams of heterogeneous robots and to consider symbolic actions.
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Stefan Kohlbrecher, Karen Petersen, Gerald Steinbauer, Johannes Maurer, Peter Lepej, Suzana Uran, Rodrigo Ventura, Christian Dornhege, Andreas Hertle, Raymond Sheh and Johannes Pellenz.
Community-Driven Development of Standard Software Modules for Search and Rescue Robots.
In
Safety, Security and Rescue Robotics (SSRR).
2012.
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Andreas Hertle, Christian Dornhege, Thomas Keller and Bernhard Nebel.
Planning with Semantic Attachments: An Object-Oriented View.
In
Proceedings of the European Conference on Artificial Intelligence (ECAI).
2012.
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In recent years, domain-independent planning has been applied to
a rising number of real-world applications. Usually, the
description language of choice is PDDL. However, PDDL is not
suited to model all challenges imposed by real-world
applications. Dornhege et al. proposed semantic attachments to
allow the computation of Boolean fluents by external processes
called modules during planning. To acquire state information
from the planning system a module developer must perform manual
requests through a callback interface which is both
inefficient and error-prone.
In this paper, we present the Object-oriented Planning Language
OPL, which incorporates the structure and advantages of modern
object-oriented programming languages. We demonstrate how a
domain-specific module interface that allows to directly access
the planner state using object member functions is automatically
gen- erated from an OPL planning task. The generated
domain-specific interface allows for a safe and less error-prone
implementation of modules. We show experimentally that this
interface is more efficient than the PDDL-based module interface
of TFD/M.
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Christian Dornhege and Alexander Kleiner.
A Frontier-Void-Based Approach for Autonomous Exploration in 3D.
In
Proceedings of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR).
2011.
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We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform equipped with a manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.
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Matthias Westphal, Christian Dornhege, Stefan Wölfl, Marc Gissler and Bernhard Nebel.
Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning.
Spatial Cognition & Computation: An Interdisciplinary Journal 11 (1), pp. 75-102. 2011.
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Manipulation planning is a complex task for robots with a manipulator arm that need to grasp objects in the environment, specifically under narrow spatial conditions restricting the workspace of the robot. A popular approach for generating motion plans is probabilistic roadmap planning. However, the sampling strategy of such planners is usually unguided, and hence may lead to motion plans that seem counterintuitive for a human observer. In this article we present an approach that generates heuristics for the probabilistic sampling strategy from spatial plans that abstract from concrete metric data. These spatial plans describe a free trajectory in the workspace of the robot on a purely qualitative level, i.e., by employing spatial relations from formalisms considered in the domain of Qualitative Spatial and Tem- poral Reasoning. We discuss how such formalisms and constraint-based reasoning methods can be applied to approximate geometrically feasible motions. The paper is completed by an evaluation of a hybrid planning system in different spatial settings showing that run-times are notably improved when an abstract plan is considered as a guidance heuristic.
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R. Kümmerle, B. Steder, Christian Dornhege, Alexander Kleiner, G. Grisetti and W. Burgard.
Large Scale Graph-based SLAM using Aerial Images as Prior Information.
Autonomous Robots 30, pp. 25-39. 2011.
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The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.
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Marc Gissler, Christian Dornhege, Bernhard Nebel and Matthias Teschner.
Deformable Proximity Queries and their Application in Mobile Manipulation Planning.
In
Symposium on Visual Computing (ISVC 2009), pp. 79-88.
AAAI Press 2009.
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We describe a proximity query algorithm for the exact minimum distance computation between arbitrarily shaped objects. Special characteristics of the Gilbert-Johnson-Keerthi (GJK) algorithm are employed in various stages of the algorithm. In the first stage, they are used to search for sub-mesh pairs whose convex hulls do not intersect. In the case of an intersection, they guide a recursive decomposition. Finally, they are used to derive lower and upper distance bounds in non-intersecting cases. These bounds are utilized in a spatial subdivision scheme to achieve a twofold culling of the domain. The algorithm does not depend on spatial or temporal coherence and is, thus, specifically suited to be applied to deformable objects. Furthermore, we describe its embedding into the geometrical part of a mobile manipulation planning system. Experiments show its usability in dynamic scenarios with deformable objects as well as in complex manipulation planning scenarios.
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Alexander Kleiner and Christian Dornhege.
Operator-Assistive Mapping in Harsh Environments.
In
IEEE International Workshop on Safety, Security and Rescue Robotics
(SSRR 2009), pp. 1-6.
2009.
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Teleoperation is a difficult task, particularly when
controlling robots from an isolated operator station.
In general, the operator has to solve nearly blindly the problems of mission
planning, target identification, robot navigation, and robot control at the same time.
The goal of the proposed system is to support teleoperated navigation
with real-time mapping.
We present a novel scan matching technique that re-considers data
associations during the search, enabling robust pose estimation even under
varying roll and pitch angle of the robot enabling mapping
on rough terrain.
The approach has been implemented as an embedded system and extensively tested
on robot platforms designed for teleoperation in critical situations, such as bomb
disposal.
Furthermore,
the system has been evaluated in a test maze by first responders during
the Disaster City event in Texas 2008.
Finally, experiments conducted within different environments show that
the system yields comparably accurate maps in real-time when
compared to higher sophisticated offline methods, such as Rao-Blackwellized SLAM.
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Christian Dornhege, Marc Gissler, Matthias Teschner and Bernhard Nebel.
Integrating Symbolic and Geometric Planning for Mobile Manipulation.
In
IEEE International Workshop on Safety, Security and Rescue Robotics
(SSRR 2009).
2009.
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Mobile manipulation requires to solve multiple subproblems.
One is planning in high-dimensional configuration spaces, that we approach in this work.
We decompose the manipulation problem into a symbolic and a geometric part.
The symbolic part is implemented as a classical symbolic planner that
tightly integrates a geometric planner enabling us to efficiently generate correct
plans.
A probabilistic roadmap planner constitutes the geometric part.
During the computation of the roadmap we utilize proximity queries to determine non-colliding configurations and to verify collision-free paths between configurations accurately and efficiently.
We demonstrate experiments in two scenarios, one of these being the manipulator dexterity test scenario that was
used in NIST's response robot evaluation in Disaster City.
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Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner and Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 114-121.
AAAI Press 2009.
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Solving real-world problems using symbolic planning often
requires a simplified formulation of the original problem,
since certain subproblems cannot be represented at all or only
in a way leading to inefficiency. For example, manipulation
planning may appear as a subproblem in a robotic planning
context or a packing problem can be part of a logistics
task. In this paper we propose an extension of PDDL for
specifying semantic attachments. This allows the evaluation of
grounded predicates as well as the change of fluents by
externally specified functions. Furthermore, we describe a
general schema of integrating semantic attachments into a
forward-chaining planner and report on our experience of
adding this extension to the planners FF and Temporal Fast
Downward. Finally, we present some preliminary experiments
using semantic attachments.
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Moritz Göbelbecker and Christian Dornhege.
Realistic Cities in Simulated Environments - An Open Street Map to Robocup Rescue Converter.
In
Online-Proceedings of the Fourth International Workshop on Synthetic Simulation
and Robotics to Mitigate Earthquake Disaster (SRMED 2009).
2009.
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A general problem when developing large scale disaster simulation environments is to acquire GIS data.
In this work, we tackle the problem of map generation from public sources.
Usually the major problem is not only the data conversion itself, but to get access to the data at all.
We solve this problem by using the website OpenStreetMap.org, that provides mapping data for the whole world in a wiki-style concept, as our source of data,
thus being able to generate maps for almost any city.
The data is converted to the format required by the Robocup Rescue Simulation System, enabling simulations
on various real-world scenarios.
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Rainer Kümmerle, Bastian Steder, Christian Dornhege, Michael Ruhnke, Giorgio Grisetti, Cyrill Stachniss and Alexander Kleiner.
On Measuring the Accuracy of SLAM Algorithms.
Autonomous Robots 27 (4), pp. 387-407. 2009.
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In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.
We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.
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Wolfram Burgard, Cyrill Stachniss, Giorgio Grisetti, Bastian Steder, Rainer Kümmerle, Christian Dornhege, Michael Ruhnke, Alexander Kleiner and Juan D. Tardos.
A Comparison of SLAM Algorithms Based on a Graph of Relations.
In
Proceedings of the 2009 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2009), pp. 2089-2095.
IEEE 2009.
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In this paper, we address the problem of creating
an objective benchmark for comparing SLAM approaches.
We propose a framework for analyzing the results of SLAM
approaches based on a metric for measuring the error of the
corrected trajectory. The metric uses only relative relations
between poses and does not rely on a global reference frame.
The idea is related to graph-based SLAM approaches in
the sense that it considers the energy needed to deform the
trajectory estimated by a SLAM approach to the ground
truth trajectory. Our method enables us to compare SLAM
approaches that use different estimation techniques or different
sensor modalities since all computations are made based on the
corrected trajectory of the robot. We provide sets of relative
relations needed to compute our metric for an extensive set
of datasets frequently used in the SLAM community. The
relations have been obtained by manually matching laser-range
observations. We believe that our benchmarking framework
allows the user an easy analysis and objective comparisons
between different SLAM approaches.
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Rainer Kümmerle, Bastian Steder, Christian Dornhege, Alexander Kleiner, Giorgio Grisetti and Wolfram Burgard.
Large Scale Graph-based SLAM using Aerial Images as Prior Information.
In
Proceedings of 2009 Robotics: Science and Systems (RSS 2009).
2009.
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(PDF)
(BIB)
To effectively navigate in their environments and accurately
reach their target locations, mobile robots require a globally
consistent map of the environment. The problem of learning a map
with a mobile robot has been intensively studied in the past and
is usually referred to as the simultaneous localization and
mapping (SLAM) problem. However, existing solutions to the SLAM
problem typically rely on loop-closures to obtain global
consistency and do not exploit prior information even if it is
available. In this paper, we present a novel SLAM approach that
achieves global consistency by utilizing publicly accessible
aerial photographs as prior information. Our approach inserts
correspondences found between three-dimensional laser range
scans and the aerial image as constraints into a graph-based
formulation of the SLAM problem. We evaluate our algorithm based
on large real-world datasets acquired in a mixed in- and outdoor
environment by comparing the global accuracy with
state-of-the-art SLAM approaches and GPS. The experimental
results demonstrate that the maps acquired with our method show
increased global consistency.
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Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler and Bernhard Nebel.
Successful Search and Rescue in Simulated Disaster Areas.
In
Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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RoboCupRescue Simulation is a large-scale multi-agent simulation
of urban disasters where, in order to save lives and minimize damage, rescue
teams must effectively cooperate despite sensing and communication limitations.
This paper presents the comprehensive search and rescue approach of the ResQ
Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup
2004.
Specific contributions include the predictions of travel costs and civilian lifetime,
the efficient coordination of an active disaster space exploration, as well as
an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability
of extinguishing fires, freeing roads from debris, disaster space exploration, and
civilian rescue. The evaluation is carried out with information extracted from
simulation log files gathered during RoboCup 2004. Our results clearly explain
the success of our team, and also confirm the scientific approaches proposed in
this paper.
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Alexander Kleiner, Bastian Steder, Christian Dornhege, Daniel Hoefler, Daniel Meyer-Delius, Johann Prediger, Joerg Stueckler, Kolja Glogowski, Markus Thurner, Matthias Luber, Michael Schnell, Rainer Kuemmerle, Timothy Burk, Tobias Braeuer and Bernhard Nebel.
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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This paper describes the approach of the RescueRobots Freiburg team.
RescueRobots Freiburg is a team of students from the university of Freiburg, that
originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ
Freiburg team (RoboCupRescue Simulation).
Due to the high versatility of the RoboCupRescue competition we tackle the three
arenas by a a twofold approach: On the one hand we want to introduce robust
vehicles that can safely be teleoperated through rubble and building debris while
constructing three-dimensional maps of the environment. On the other hand we
want to introduce a team of autonomous robots that quickly explore a large terrain
while building a two-dimensional map. This two solutions are particularly wellsuited
for the red and yellow arena, respectively. Our solution for the orange arena
will finally be decided between these two, depending on the capabilities of both
approaches at the venue.
In this paper, we introduce some preliminary results that we achieved so far from
map building, localization, and autonomous victim identification. Furthermore
we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism
for the active distribution of RFID tags.
1 Introduction
RescueRobots Freiburg is a team of students from the university of Freiburg. The team
originates from the former CS Freiburg team[6], which won three times the RoboCup
world championship in the RoboCupSoccer F2000 league, and the ResQ Freiburg team[2],
which won the last RoboCup world championship in the RoboCupRescue Simulation
league. The team approach proposed in this paper is based on experiences gathered at
RoboCup during the last six years.
Due to the high versatility of the RoboCupRescue competition we tackle the three
arenas by a twofold approach: On the one hand we want to introduce a vehicle that
can safely be teleoperated through rubble and building debris while constructing threedimensional
maps of the environment. On the other hand we want to introduce an autonomous
team of robots that quickly explore a large terrain while building a twodimensional
map. This two solutions are particularly well-suited for the red and yellow
arena, respectively. Our solution for the orange arena will finally be decided between
these two, depending on the capabilities of both approaches at the venue.
