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@PHDTHESIS{Scheffe:1018409,
      author       = {Scheffe, Patrick},
      othercontributors = {Kowalewski, Stefan and Alrifaee, Bassam and Koenig, Sven},
      title        = {{P}rioritized motion planning for connected vehicles},
      volume       = {2025,01},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University, Department of Computer Science},
      reportid     = {RWTH-2025-07834},
      series       = {Aachener Informatik-Berichte},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2025},
      abstract     = {Networked control is concerned with the control of multiple
                      agents which interact. When formulating the control problem
                      as an optimization problem, the highest solution quality can
                      be achieved by centralized control which considers all
                      agents at once. However, the number of problem dimensions
                      then grows with the number of agents. When solving nonconvex
                      optimization problems, the computation time scales
                      non-polynomially with the number of problem dimensions.
                      Combining centralized multi-agent control with nonconvex
                      optimization, a time-constrained optimization is often
                      intractable. A relevant application is multi-agent motion
                      planning (MAMP) for connected and automated vehicles (CAVs)
                      with collision avoidance. We address the nonconvexity of the
                      optimization problem by modelling the system dynamics using
                      an automaton, enabling the use of graph-search algorithms
                      for motion planning. Our motion planning algorithm has a
                      limited horizon to decrease computational complexity and
                      obtains a constant computation time with a fixed number of
                      search operations. We achieve recursive feasibility through
                      the structure of the automaton modelling the system
                      dynamics. We address the growing number of problem
                      dimensions in a centralized formulation with prioritized
                      planning (PP). In PP, agents plan sequentially according to
                      their priorities, and lower-priority agents respect the
                      plans of higher-priority agents. This work addresses three
                      challenges in PP. First, the effect of the prioritization on
                      the solution quality and the computation time is often
                      unknown a priori. We present three prioritization
                      approaches: one using a heuristic to increase solution
                      quality, one to minimize the number of sequentially planning
                      agents and thus the computation time, and one to
                      simultaneously compute multiple prioritizations, which can
                      increase solution quality or reduce computation time.
                      Second, the computation time in PP grows approximately
                      linearly with the number of agents, which poses a challenge
                      in large-scale systems. We present an approach to bound the
                      computation time as desired, while improving the solution
                      quality compared to an approach in which all agents compute
                      in parallel. Third, an agent can fail to find a feasible
                      solution in PP. We present an approach to ensure recursive
                      feasibility for every agent. When testing algorithms for
                      networked control, it is difficult to achieve
                      reproducibility of the experiments. We present a software
                      architecture to address this challenge and implement it in
                      our Cyber-Physical Mobility Lab (CPM Lab). The CPM Lab is an
                      open-source testbed for CAVs. We evaluate our methods on
                      prioritization and bounded computation time for prioritized
                      motion planning in numerical and physical experiments in the
                      CPM Lab.},
      cin          = {122810 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)122810_20140620$ / $I:(DE-82)120000_20140620$},
      pnm          = {DFG project G:(GEPRIS)412371229 - GROKO-Plan:
                      GRaphenbasierte, Optimale und KOoperative
                      Trajektorienplanung für Interagierende Automobile
                      (412371229) / SPP 1835: Kooperativ interagierende
                      Automobile},
      pid          = {G:(GEPRIS)412371229 / G:(GEPRIS)255645177},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-07834},
      url          = {https://publications.rwth-aachen.de/record/1018409},
}