2 research outputs found
Cable Tree Wiring -- Benchmarking Solvers on a Real-World Scheduling Problem with a Variety of Precedence Constraints
Cable trees are used in industrial products to transmit energy and
information between different product parts. To this date, they are mostly
assembled by humans and only few automated manufacturing solutions exist using
complex robotic machines. For these machines, the wiring plan has to be
translated into a wiring sequence of cable plugging operations to be followed
by the machine. In this paper, we study and formalize the problem of deriving
the optimal wiring sequence for a given layout of a cable tree. We summarize
our investigations to model this cable tree wiring Problem (CTW) as a traveling
salesman problem with atomic, soft atomic, and disjunctive precedence
constraints as well as tour-dependent edge costs such that it can be solved by
state-of-the-art constraint programming (CP), Optimization Modulo Theories
(OMT), and mixed-integer programming (MIP) solvers. It is further shown, how
the CTW problem can be viewed as a soft version of the coupled tasks scheduling
problem. We discuss various modeling variants for the problem, prove its
NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark
set of 278 instances. The complete benchmark set with all models and instance
data is available on github and is accepted for inclusion in the MiniZinc
challenge 2020
Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots
This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.California Institute of Technology. Keck Institute for Space Studie