no code implementations • 1 Jan 2021 • Steve Paul, Payam Ghassemi, Souma Chowdhury
This paper presents a novel graph (reinforcement) learning method to solve an important class of multi-robot task allocation (MRTA) problems that involve tasks with deadlines, and robots with ferry range and payload constraints (thus requiring multiple tours per robot).
no code implementations • 9 Jul 2019 • Payam Ghassemi, David DePauw, Souma Chowdhury
Multiple robotic systems, working together, can provide important solutions to different real-world applications (e. g., disaster response), among which task allocation problems feature prominently.
no code implementations • 9 Jul 2019 • Payam Ghassemi, Souma Chowdhury
Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance.
no code implementations • 31 May 2019 • Payam Ghassemi, Sumeet Sanjay Lulekar, Souma Chowdhury
This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR).
no code implementations • 20 Jul 2018 • Payam Ghassemi, Souma Chowdhury
The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake.