Name
Abstract | ||
|---|---|---|
We consider dynamic route planning for a fleet of Autonomous Mobile Robots (AMRs) doing fetch and carry tasks on a shared factory floor. In this paper, we propose Stochastic Work Graphs (SWG) as a formalism for capturing the semantics of such distributed and uncertain planning problems. We encode SWGs in the form of a Euclidean Markov Decision Process (EMDP) in the tool Uppaal Stratego, which employs Q-Learning to synthesize near-optimal plans. Furthermore, we deploy the tool in an online and distributed fashion to facilitate scalable, rapid replanning. While executing their current plan, each AMR generates a new plan incorporating updated information about the other AMRs positions and plans. We propose a two-layer Model Predictive Controller-structure (waypoint and station planning), each individually solved by the Q-learning-based solver. We demonstrate our approach using ARGoS3 large-scale robot simulation, where we simulate the AMR movement and observe an up to 27.5% improvement in makespan over a greedy approach to planning. To do so, we have implemented the full software stack, translating observations into SWGs and solving those with our proposed method. In addition, we construct a benchmark platform for comparing planning techniques on a reasonably realistic physical simulation and provide this under the MIT open-source license. |
Year | Venue | Keywords |
|---|---|---|
2022 | International Conference on Automated Planning and Scheduling | Model-Predictive Control,Q-learning,Fleet Management,Uppaal Stratego,Simulation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Bøgh | 1 | 0 | 0.34 |
| Peter Gjøl Jensen | 2 | 32 | 9.38 |
| Martin Kristjansen | 3 | 0 | 0.34 |
| Kim G. Larsen | 4 | 3922 | 254.03 |
| Ulrik Nyman | 5 | 0 | 0.34 |