Search Results for author: Payam Ghassemi

Found 5 papers, 0 papers with code

Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture

no code implementations1 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).

Combinatorial Optimization Graph Learning

Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response

no code implementations9 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.

Decision Making Disaster Response

Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search

no code implementations9 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.

Bayesian Optimization

Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring

no code implementations31 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).

Bayesian Optimization

Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented with Fuzzy Clustering

no code implementations20 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.

Clustering Disaster Response +3

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