@INPROCEEDINGS{2008-Kropf-etal-MMULATOR,
  author = {Matthias Kropff and Christian Reinl and Kim Listmann and Karen Petersen and Katayon Radkhah and Faisal Karim Shaikh and Arthur Herzog and Armin Strobel and Daniel Jacobi and Oskar von Stryk},
  title = {MM-ulator: Towards a Common Evaluation Platform for Mixed Mode Environments},
  year = {2008},
  publisher = {Springer (ACCEPTED FOR PUBLICATION)},
  booktitle = {Proceedings of Int. Conference on Simulation, Modeling, and Programming (SIMPAR)},
  abstract = {We investigate the interaction of mobile robots, relying on information provided by heterogeneous sensor nodes, to accomplish a mission. Cooperative, adaptive and responsive monitoring in Mixed-Mode Environments (MMEs) raises the need for multi-disciplinary research initiatives. To date, such research initiatives are limited since each discipline focusses on its domain specific simulation or testbed environment. Existing evaluation environments do not respect the interdependencies occurring in MMEs. As a consequence, holistic validation for development, debugging, and performance analysis requires an evaluation tool incorporating multi-disciplinary demands. In the context of MMEs, we discuss existing solutions and highlight the synergetic benefits of a common evaluation tool. Based on this analysis we present the \emph{MM-ulator}: a novel architecture for an evaluation tool incorporating the necessary diversity for multi-agent hard-/software-in-the-loop simulation in a modular and scalable way.},
}

@INPROCEEDINGS{2008:ReinlRuhStolzenburgStryk,
  author = {C. Reinl and F. Ruh and F. Stolzenburg and O. von Stryk},
  title = {Multi-Robot Systems Optimization and Analysis Using MILP and CLP },
  year = {2008},
  month = {May 12-16},
  address = {Estoril, Portugal},
  booktitle = {AAMAS08-Workshop on Formal Models and Methods for Multi-Robot Systems},
  abstract = {Formal methods for multi-robot system analysis, specially logic-based methods, operate on discrete models. Optimization methods for simultaneous trajectory and task allocation, namely mixed integer dynamic optimization, operate on hybrid dynamical models which take into account a model of the motion dynamics of the physical robot. In this paper, ongoing work towards a coherent treatment of both approaches is described. A benchmark problem from robot soccer is introduced and used as an illustrative example.},
}

@INPROCEEDINGS{2007:ReinlVonStryk_RoboMat,
  author = {C. Reinl and O. von Stryk},
  title = {Optimal Control of Cooperative Multi-Robot Systems Using Mixed-Integer Linear Programming},
  year = {2007},
  pages = {145 - 151},
  month = {Sept. 17-19},
  editor = {Centro Internacional de Mathematica},
  address = {Coimbra, Portugal},
  booktitle = {Proc. RoboMat 2007},
  url = {http://labvis.isr.uc.pt/robomat/},
  abstract = {A new planning method for optimal control of multi-robot systems is discussed which accounts for the (continuous) physical locomotion dynamics of the robots and its tight coupling to the distribution and allocation of (discrete) subtasks to the robots to fulfill a joint mission. The point of departure is a nonlinear and nonconvex hybrid optimal control problem (HOCP) formulation which incorporates a detailed hybrid automaton model. Because of the many difficulties involved in solving this problem like large computational times and the lack of good or global convergence properties it is transcribed into a mixed- integer linear program (MILP). This can be solved much more efficiently using existing algorithms. The proposed approach is outlined for an example problem of cooperative soccer robots. The MILP solution itself may serve either as a good initial solution estimate for a method addressing the nonlinear HOCP or may later become the kernel of a model predictive control method for cooperative multi-robot systems. Despite the promising results obtained so far a variety of open questions yet remains to be answered including the ā€¯bestā€¯ way of transcribing HOCP to MILP with respect to both computational efficiency and good HOCP solution approximation. },
}

@INPROCEEDINGS{2007:ReinlVonStryk_ROBOCOMM,
  author = {Christian Reinl and Oskar von Stryk},
  title = {Optimal Control of Multi-Vehicle Systems Under Communication Constraints Using Mixed-Integer Linear Programming},
  year = {2007},
  month = {Oct. 15-17},
  address = {Athens, Greece},
  booktitle = {Proceedings of the. First International Conference on Robot Communication and Coordination (RoboComm)},
  organization = {ICST},
  keywords = {connectivity network, mobile communication network, linearized optimal control, mixed-integer linear optimal control, cooperative multi-vehicle system},
  abstract = {A new planning method for optimal cooperative control of heterogeneous multi-vehicle systems is investigated which enables to account for each vehicleā€™s nonlinear physical motion dynamics in a structured environment as well as for connectivity constraints of wireless communication. A general formulation as nonlinear hybrid optimal control problem (HOCP) is presented. A transformation technique is proposed to reduce the large computational efforts for solving HOCPs towards a future online application of this approach. Hereby the general problem is transcribed to a linearized mixed-integer linear programming problem (MILP) which can be solved much more efficiently. The proposed approach is successfully applied to the numerical solution of a representative, cooperative monitoring problem involving heterogeneous vehicles and conditions.},
}

@INPROCEEDINGS{2006:IFAC-MVS-GlockerReinlvonStryk,
  author = {M.Glocker and C. Reinl and O. von Stryk},
  title = {Optimal task allocation and dynamic trajectory planning for multi-vehicle systems using nonlinear hybrid optimal control},
  year = {2006},
  pages = {38-43},
  month = {October 2-3},
  address = {Salvador, Brazil},
  booktitle = {Proc. 1st IFAC-Symposium on Multivehicle Systems},
  abstract = {Based on a nonlinear hybrid dynamical systems model a new planning method for optimal coordination and control of multiple unmanned vehicles is investigated. The time dependent hybrid state of the overall system consists of discrete (roles, actions) and continuous (e.g. position, orientation, velocity) state variables of the vehicles involved. The evolution in time of the systemā€™s hybrid state is described by a hybrid state automaton. The presented approach enables a tight and formal coupling of discrete and continuous state dynamics, i.e. of dynamic role and action assignment and sequencing as well as of the physical motion dynamics of a single vehicle modeled by nonlinear differential equations. The planning problem of determining optimal hybrid state trajectories that minimize a cost function as time or energy for optimal multi-vehicle cooperation subject to constraints including the vehicleā€™s motion dynamics is transformed to a mixed-binary dynamic optimization problem being solved numerically. The numerical method consists of an inner iteration where multiphase optimal control problems are solved using a direct collocation method and an outer iteration based on a branch-and-bound search of the discrete solution space. The approach presented in this paper is applied to the scenarios of optimal simultaneous waypoint or target sequencing and dynamic trajectory planning for a team of unmanned aerial vehicles in a plane and to optimal role assignment and physics-based trajectories in robot soccer.},
}