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.
no code implementations • 20 Jul 2018 • Sharat Chidambaran, Amir Behjat, Souma Chowdhury
Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability.
no code implementations • 17 Mar 2019 • Amir Behjat, Sharat Chidambaran, Souma Chowdhury
Neuroevolution is a process of training neural networks (NN) through an evolutionary algorithm, usually to serve as a state-to-action mapping model in control or reinforcement learning-type problems.
no code implementations • 31 May 2019 • Amir Behjat, Krushang Gabani, Souma Chowdhury
Neuroevolution, which uses evolutionary algorithms to simultaneously optimize the topology and weights of neural networks, is used as the learning method -- which operates over a set of sample approach scenarios.
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 • 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 • 4 Mar 2020 • Ion Matei, Johan de Kleer, Alexander Feldman, Rahul Rai, Souma Chowdhury
In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models from high fidelity models.
no code implementations • 29 Jul 2020 • Amir Behjat, Manaswin Oddiraju, Mohammad Ali Attarzadeh, Mostafa Nouh, Souma Chowdhury
Further novel contribution occurs through the development of an inverse modeling approach that can instantaneously produce the 1D metamaterial design with minimum mass for a given desired non-resonant frequency range.
no code implementations • 8 Dec 2020 • Zhibo Zhang, Chen Zeng, Maulikkumar Dhameliya, Souma Chowdhury, Rahul Rai
The thermal data is processed through a thresholding and Kalman filter approach to detect and track the bounding box.
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 • 11 Jan 2022 • Chen Zeng, Grant Hecht, Prajit KrisshnaKumar, Raj K. Shah, Souma Chowdhury, Eleonora M. Botta
This tether-net system is subject to several sources of uncertainty in sensing and actuation that affect the performance of its net launch and closing control.
no code implementations • 6 Jul 2023 • Feng Liu, Achira Boonrath, Prajit KrisshnaKumar, Elenora M. Botta, Souma Chowdhury
The earth's orbit is becoming increasingly crowded with debris that poses significant safety risks to the operation of existing and new spacecraft and satellites.
no code implementations • 17 Aug 2023 • Prajit KrisshnaKumar, Jhoel Witter, Steve Paul, Hanvit Cho, Karthik Dantu, Souma Chowdhury
This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies.
no code implementations • 9 Jan 2024 • Steve Paul, Jhoel Witter, Souma Chowdhury
This paper develops a graph reinforcement learning approach to online planning of the schedule and destinations of electric aircraft that comprise an urban air mobility (UAM) fleet operating across multiple vertiports.
no code implementations • 11 Mar 2024 • Steve Paul, Nathan Maurer, Souma Chowdhury
Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods.
no code implementations • 11 Mar 2024 • Achira Boonrath, Feng Liu, Elenora M. Botta, Souma Chowdhury
System performance is assessed in terms of capture success and overall fuel consumption by the MUs.