Abstract
Motivated by autonomous warehouse applications in the real world, we study a variant of Multi-Agent Path Finding (MAPF) problem where robots also need to pick and deliver some items on their way to their destination. We call this variant the Multi-Agent Pick and Delivery with Capacities (MAPDC) problem. In addition to the challenges of MAPF (i.e., finding collision-free plans for each robot from an initial location to a destination while minimizing the maximum makespan), MAPDC asks also for the allocation of the pick and deliver tasks among robots while taking into account their capacities (i.e., the maximum number of items one robot can carry at a time). We study this problem with two different approaches, action planning vs path finding, using Answer Set Programming with two different computation modes, single-shot vs multi-shot.
This work has been partially supported by Tubitak Grant 118E431.
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Notes
- 1.
The effects of an action appear at the same time step as the occurrence of the action, instead of the next time step, to comply with the multi-shot ASP formulation of MAPDC-P explained in the next section.
References
Bartholdi, J.J., III., Hackman, S.T.: Warehouse and distribution science. Supply Chain and Logistics Institute, Georgia Institute of Technology (2019)
Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming: an introduction to the special issue. AI Mag. 37(3), 5–6 (2016). https://doi.org/10.1609/aimag.v37i3.2669
Chen, Z., Alonso-Mora, J., Bai, X., Harabor, D.D., Stuckey, P.J.: Integrated task assignment and path planning for capacitated multi-agent pickup and delivery. IEEE RAL 6(3), 5816–5823 (2021). https://doi.org/10.1109/LRA.2021.3074883
Erdem, E., Kisa, D., Oztok, U., Schüller, P.: A general formal framework for pathfinding problems with multiple agents. In: Proceedings of AAAI (2013)
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Multi-shot ASP solving with clingo. TPLP 19(1), 27–82 (2019). https://doi.org/10.1017/S1471068418000054
Gebser, M., et al.: Experimenting with robotic intra-logistics domains. TPLP 18(3–4), 502–519 (2018). https://doi.org/10.1017/S1471068418000200
Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9, 365–385 (1991)
Grenouilleau, F., van Hoeve, W.J., Hooker, J.N.: A multi-label A* algorithm for multi-agent pathfinding. In: Proceedings of ICAPS, pp. 181–185 (2019)
Guthrie, C., Fosso-Wamba, S., Arnaud, J.B.: Online consumer resilience during a pandemic: an exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. JRCS 61, 102570 (2021). https://doi.org/10.1016/j.jretconser.2021.102570
Hönig, W., Kiesel, S., Tinka, A., Durham, J., Ayanian, N.: Conflict-based search with optimal task assignment. In: Proceedings of AAMAS (2018)
Lifschitz, V.: Answer set programming and plan generation. AIJ 138, 39–54 (2002). https://doi.org/10.1016/S0004-3702(02)00186-8
Liu, M., Ma, H., Li, J., Koenig, S.: Task and path planning for multi-agent pickup and delivery. In: Proceedings of AAMAS, pp. 1152–1160 (2019)
Ma, H., Koenig, S.: Optimal target assignment and path finding for teams of agents. In: Proceedings of AAMAS, pp. 1144–1152 (2016)
Ma, H., Li, J., Kumar, T.K.S., Koenig, S.: Lifelong multi-agent path finding for online pickup and delivery tasks. In: Proceedings of AAMAS, pp. 837–845 (2017)
Marek, V.W., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: Apt, K.R., Marek, V.W., Truszczynski, M., Warren, D.S. (eds.) The Logic Programming Paradigm. Artificial Intelligence. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-60085-2_17
Nguyen, V., Obermeier, P., Son, T.C., Schaub, T., Yeoh, W.: Generalized target assignment and path finding using answer set programming. In: Proceedings of IJCAI, pp. 1216–1223 (2017). https://doi.org/10.24963/ijcai.2017/169
Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. AMAI 25, 241–273 (1999)
Sharon, G., Stern, R., Felner, A., Sturtevant, N.R.: Conflict-based search for optimal multi-agent pathfinding. AIJ 219, 40–66 (2015). https://doi.org/10.1016/j.artint.2014.11.006
Surynek, P.: On propositional encodings of cooperative path-finding. In: Proceedings of ICTAI, pp. 524–531 (2012). https://doi.org/10.1109/ICTAI.2012.77
Vodrázka, J., Barták, R., Svancara, J.: On modelling multi-agent path finding as a classical planning problem. In: Proceedings of ICTAI, pp. 23–28 (2020). https://doi.org/10.1109/ICTAI50040.2020.00014
Yu, J., LaValle, S.M.: Optimal multirobot path planning on graphs: complete algorithms and effective heuristics. IEEE TRO 32(5), 1163–1177 (2016). https://doi.org/10.1109/TRO.2016.2593448
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Tajelipirbazari, N. et al. (2022). Multi-agent Pick and Delivery with Capacities: Action Planning Vs Path Finding. In: Cheney, J., Perri, S. (eds) Practical Aspects of Declarative Languages. PADL 2022. Lecture Notes in Computer Science(), vol 13165. Springer, Cham. https://doi.org/10.1007/978-3-030-94479-7_3
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