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• Albert-Ludwigs-Universität Freiburg
• Technische Fakultät
• Institut für Informatik

Johannes Löhr – Publikationen

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2016

• Johannes Aldinger und Johannes Löhr.
  The Jumpbot Domain for Numeric Planning.
  Technischer Bericht 279, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, 2016.
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  The Jumpbot domain for numeric planning with instantaneous actions models a walking robot that has to reach a target region by jumping over water ditches. The kinematic of the robot is modeled by its current position and velocity vector. The planner has to reason about the correct accelerations, rotations, velocities, jump positions and space for the deceleration. This domain description provides a set of benchmark instances for numeric planning in an area which is underrepresented by prevailing benchmarks: the use of numeric variables to model physical properties as opposed to their use for modeling resources.

2014

• Johannes Löhr, Martin Wehrle, Maria Fox und Bernhard Nebel.
  Symbolic Domain Predictive Control.
  In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), S. 2315-2321. AAAI Press 2014.
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  Planning-based methods to guide switched hybrid systems from an initial state into a desired goal region opens an interesting field for control. The idea of the Domain Predictive Control (DPC) approach is to generate input signals affecting both the numerical states and the modes of the system by stringing together atomic actions to a logically consistent plan. However, the existing DPC approach is restricted in the sense that a discrete and pre-defined input signal is required for each action. In this paper, we extend the approach to deal with symbolic states. This allows for the propagation of reachable regions of the state space emerging from actions with inputs that can be arbitrarily chosen within specified input bounds. This symbolic extension enables the applicability of DPC to systems with bounded inputs sets and increases its robustness due to the implicitly reduced search space. Moreover, precise numeric goal states instead of goal regions become reachable.

2013

• Johannes Löhr, Johannes Aldinger, Stefan Winkler und Georg Willich.
  Automated Planning for Earth Observation Spacecraft under Attitude Dynamical Constraints.
  In Jahrbuch der Deutschen Gesellschaft für Luft- und Raumfahrt (DGLR2013). 2013.
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  Agile Earth observation missions continuously require a large amount of planning during the spacecraft's observations. Beside priorities of the observation sites, especially the agility constraints of the satellite are important to be taken into account during the planning process. This is due to the body-fixed instrument's line of sight, requiring the whole satellite to point to the observation sites while scanning. Scanning a sequence of observation sites leads to complex slew maneuvers which must not exceed the satellite's actuator capacities, attitude constraints or maximum angular rates. Additionally, the regions of interest may change over time, making it necessary to adapt and optimize the observation sequence continuously. An automated process is required to efficiently handle this task. We present a planning algorithm to sequence an arbitrarily distributed set of observation patches to a feasible observation plan, considering priority criteria of the observation sites and agility constraints of the satellite.

• Johannes Aldinger und Johannes Löhr.
  Planning for Agile Earth Observation Satellites.
  In Proceedings of the ICAPS-2013 Workshop on Planning in Continuous Domains (PCD), S. 9-17. 2013.
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  Agile Earth observation satellites are satellites orbiting Earth with the purpose to gather information of the Earth's surface by slewing the satellite toward regions of interest. Constraints arise not only from dynamical and kinematic aspects of the satellite and its sensors. Regions of interest change over time and bad weather can conceal important observation targets. This results in a constant need to replan the satellite's tasks and raises the desire to automatize this planning process. We consider the Earth observation problem with the help of the module extension of the numerical planning system Temporal Fast Downward. Complex satellite slew maneuvers are calculated within modules, while the planner selects and schedules the regions to be scanned. First results encourage deeper research in this area so that forthcoming satellite space missions can draw on automated planning to improve the performance of agile Earth observation tasks.

• Johannes Löhr, Patrick Eyerich, Stefan Winkler und Bernhard Nebel.
  Domain Predictive Control Under Uncertain Numerical State Information.
  In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS13). 2013.
  (Abstract einblenden) (Abstract ausblenden) (PDF) (BIB)
  

  In planning, hybrid system states consisting of logical and numerical variables are usually assumed to be completely known. In particular, for numerical state variables full knowledge of their exact values is assumed. However, in real world applications states are results of noisy measurements and imperfect actuators. Therefore, a planned sequence of state transitions might fail to lead a hybrid system to the desired goal. We show how to propagate and reason about uncertain state information directly in the planning process, enabling hybrid systems to find plans that satisfy numerical goals with predefined confidence.

2012

• Johannes Löhr, Bernhard Nebel und Stefan Winkler.
  Planning Based Autonomous Lander Control.
  In Proceedings of the Astrodynamics Specialist Conference (AIAA/AAS 2012). 2012.
  (Abstract einblenden) (Abstract ausblenden) (Online; DOI)
  

  Safe landing of spacecraft on extraterrestrial surfaces implies a number of challenges. The main issue is to precisely initiate coasting, braking and landing maneuvers to safely land at a desired landing zone. Meanwhile, the increasing information level about the landing environment has to be processed and the landing trajectory eventually adapted in order to avoid hazardous situations. In this paper these time critical tasks are performed by Domain Predictive Control. It has been developed to guide dynamic systems into desired goal states by flexibly reordering atomic actions using planning algorithms from artificial intelligence. Here, the method is applied to autonomously adapt control commands and associated landing trajectories with respect to the changing environmental knowledge. Simulation results show the feasibility of this new approach and reveal issues which should be subject to future research.

• Johannes Löhr, Patrick Eyerich, Thomas Keller und Bernhard Nebel.
  A Planning Based Framework for Controlling Hybrid Systems.
  In Proceedings of the 22nd International Conference on Automated Planning and Scheduling (ICAPS 2012). 2012.
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  The control of dynamic systems, which aims to minimize the deviation of state variables from reference values in a contin- uous state space, is a central domain of cybernetics and con- trol theory. The objective of action planning is to find feasible state trajectories in a discrete state space from an initial state to a state satisfying the goal conditions, which in principle ad- dresses the same issue on a more abstract level. We combine these approaches to switch between dynamic system charac- teristics on the fly, and to generate control input sequences that affect both discrete and continuous state variables. Our approach (called Domain Predictive Control) is applicable to hybrid systems with linear dynamics and discretizable inputs.

2011

• Johannes Löhr und Stefan Winkler.
  Comparison of Periodic System Lifting Techniques for Robust Stability Analysis of Magnetic Spacecraft Attitude Control Systems.
  In Proceedings of the Guidance Navigation and Control Conference (AIAA/GNC 2011). 2011.
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  Magnetic attitude control is a common method for low Earth orbit spacecraft. Verification of nominal (yes/no), and even more important, robust attitude stability of such systems is of significant importance for any real mission. Considering nadir-oriented operation, the linearized closed-loop attitude dynamics equations lead to a linear time periodic system. While nominal stability can be obtained from Floquet-Theory, robust stability analysis via standard μ-Analysis requires lifting procedures to convert the linear time periodic into a linear time invariant system. Two of those lifting procedures are compared in this paper: (1) "Fast Discretization", which is based on a "sample and hold" view on the periodic state space matrices, and (2) "Frequency Lifting" based on Fourier series expansion. Both methods are applied to a theoretic linear time periodic example from literature and magnetic spacecraft attitude control. The comparison focus' on applicability for real satellite missions.

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