Souma Chowdhury

University at Buffalo, The State University of New York | SUNY Buffalo · Department of Mechanical and Aerospace Engineering

For a wide variety of envisioned humanitarian and commercial applications that involve a human user commanding a swarm of robotic systems, developing human-swarm interaction (HSI) principles and interfaces calls for systematic virtual environments to study such HSI implementations. Specifically, such studies are fundamental to achieving HSI that is...

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model t...

Self-healing "smart grids" are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mit...

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircra...

We introduce a novel approach to automatically synthesize a mathematical representation of the control algorithms implemented in industrial cyber-physical systems (CPS), given the embedded system binary. The output model can be used by subject matter experts to assess the system's compliance with the expected behavior and for a variety of forensic...

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. The active tether-net system, which consists of a flexible net with maneuverable corner nodes launched from a small autonomous spacecraft, is a promising solution for capturing and dispos...

Complex optimal design and control processes often require repeated evaluations of expensive objective functions and consist of large design spaces. Data-driven surrogates such as neural networks and Gaussian processes provide an attractive alternative to simulations and are utilized frequently to represent these objective functions in optimization...

To succeed in real-world swarm robotics missions, it is essential to integrate multiple swarm-robot behaviors, such as target search, task allocation, and path planning. Most previous studies focus on the success of single behavior or are limited by the swarm size or the complexity of mission environment. This work aims to develop a neural network-...

Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. This paper tackles a particular class of these problems that we call MRTA-collective transport or MRTA-CT -- here tasks present varying workloads and deadlines, and robots are subject...

Majority of aircraft under the Urban Air Mobility (UAM) concept are expected to be of the electric vertical takeoff and landing (eVTOL) vehicle type, which will operate out of vertiports. While this is akin to the relationship between general aviation aircraft and airports, the conceived location of vertiports within dense urban environments presen...

Opportunistic Physics-mining Transfer Mapping Architecture (OPTMA) is a hybrid architecture that combines fast simplified physics models with neural networks in order to provide significantly improved generalizability and explainability compared to pure data-driven machine learning (ML) models. However, training OPTMA remains computationally ineffi...

This work presents a framework aimed at mitigating adverse effects of high-amplitude drone noise ranging from hearing loss to reduced productivity in human–robot collaborative environments by infusing acoustic awareness in a path planning algorithm without imposing any additional design layers or hardware to an operational drone. Following a detail...

Auxetics refer to a class of engineered structures which exhibit an overall negative Poisson’s ratio. These structures open up various potential opportunities in impact resistance, high energy absorption, and flexible robotics, among others. Interestingly, auxetic structures could also be tailored to provide passive adaptation to changes in environ...

This paper introduces a new graph neural network architecture for learning solutions of Capacitated Vehicle Routing Problems (CVRP) as policies over graphs. CVRP serves as an important benchmark for a wide range of combinatorial planning problems, which can be adapted to manufacturing, robotics and fleet planning applications. Here, the specific ai...

Auxetics refer to a class of engineered structures which exhibit an overall negative Poisson's ratio. These structures open up various potential opportunities in impact resistance, high energy absorption, and flexible robotics, among others. Interestingly, auxetic structures could also be tailored to provide passive adaptation to changes in environ...

The collective operation of robots, such as unmanned aerial vehicles (UAVs) operating as a team or swarm, is affected by their individual capabilities, which in turn is dependent on their physical design, aka morphology. However, with the exception of a few (albeit ad hoc) evolutionary robotics methods, there has been very little work on understand...

Swarm-robotic approaches to search and target localization, where target sources emit a spatially varying signal, promise unparalleled time efficiency and robustness. With most existing swarm search methods, it remains challenging to simultaneously preserve search efficiency and mathematical insight along with scalability and computational tractabi...

View Video Presentation: https://doi.org/10.2514/6.2022-3718.vid Locally resonant elastic metamaterials (LREM) usually constitute a periodic arrangement of unitcells, which can be tuned to potentially damp out vibration in selected frequency ranges, thus yielding desired bandgaps. However, being limited to the parameters of just one unitcell can so...

View Video Presentation: https://doi.org/10.2514/6.2022-3911.vid Optimal scheduling of the fleet of aircraft comprising an urban air mobility (UAM) network is key to economically viable and sustainable integration of UAM networks within our existing urban and suburban transportation ecosystems. To this end, this paper firstly formulates the UAM fle...

This paper presents a novel graph reinforcement learning (RL) architecture to solve multi-robot task allocation (MRTA) problems that involve tasks with deadlines and workload, and robot constraints such as work capacity. While drawing motivation from recent graph learning methods that learn to solve combinatorial optimization (CO) problems such as...

Physics-Infused Machine Learning (PIML) architectures aim at integrating machine learning with computationally-efficient, low-fidelity (partial) physics models, leading to improved generalizability, extrapolability, and robustness to noise, compared to pure data-driven approximation models. Recently a new PIML architecture was reported by the same...

Tether-net launched from a chaser spacecraft provides a promising method to capture and dispose of large space debris in orbit. 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. Earlier reliability-based optimization approaches to design co...

View Video Presentation: https://doi.org/10.2514/6.2022-0384.vid Physics-Infused Machine Learning (PIML) architectures aim at integrating machine learning with computationally-efficient, low-fidelity (partial) physics models, leading to improved generalizability, extrapolability, and robustness to noise, compared to pure data-driven approximation m...

Tether-net launched from a chaser spacecraft provides a promising method to capture and dispose off large space debris in orbit. 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. Earlier reliability based optimization approaches to design c...

Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to a...

This study presents car crash-induced neck injury trends in response to variations in three impact variables: velocity (10 − 45 mph; 16.1 − 72.4 km/h), location (front, rear, near side, and far side), and angle (-45° to 45°). By employing a combined finite element (FE)-mathematical surrogate modeling approach, the number of necessary FE crash simul...

This paper tackles a class of multi-robot task allocation (MRTA) problems called “Single-Task Robots and Single-Robot Tasks” or SR-ST problems, subject to the following additional characteristics: tasks with deadlines, tasks that are generated during the mission, and robots with range and payload constraints (thus requiring multiple tours per robot...

Human-swarm interaction has recently gained attention due to its plethora of new applications in disaster relief, surveillance, rescue, and exploration. However, if the task difficulty increases, the performance of the human operator decreases, thereby decreasing the overall efficacy of the human-swarm team. Thus, it is critical to identify the tas...

To accomplish complex swarm robotic missions in the real world, one needs to plan and execute a combination of single robot behaviors, group primitives such as task allocation, path planning, and formation control, and mission-specific objectives such as target search and group coverage. Most such missions are designed manually by teams of robotics...

Automated inverse design methods are critical to the development of metamaterial systems that exhibit special user-demanded properties. While machine learning approaches represent an emerging paradigm in the design of metamaterial structures, the ability to retrieve inverse designs on-demand remains lacking. Such an ability can be useful in acceler...

Unmanned aerial vehicles, specifically quadrotor drones, are increasingly commonplace in community and workplace settings and are often used for photography, cinematography, and small parcel transport. The presence of these flying robotic systems has a substantial impact on the surrounding environment. To better understand the ergonomic impacts of...

View Video Presentation: https://doi.org/10.2514/6.2021-3103.vid Tether-nets deployed from a chaser spacecraft have been proposed as a promising technical solution to capture space debris in recent years. The success of this (usually one-shot) process however depends on the ability to perform an optimal launch and closure of the tether-net, subject...

Locally resonant elastic metamaterials (LREM) can be designed, by optimizing the geometry of the constituent self-repeating unit cells, to potentially damp out vibration in selected frequency ranges, thus yielding desired bandgaps. However, it remains challenging to quickly arrive at unit cell designs that satisfy any requested bandgap specificatio...

A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is often used for crash analyses but requires time and co...

This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., no-fly zones) in a specified area of interest, and provides an op...

Bioinspired surface riblets have been known to improve drag performance by altering the near-wall flow structures, especially in the transitional flow regime. Unlike conventional riblet geometries (for example, sawtooth and scalloped shapes) overlaid on flat plates, the use of a new Gaussian-shaped abstraction overlaid on three-dimensional (3-D) ai...

In this paper, we address the problem of multiple quadcopter control, where the quadcopters maneuver in close proximity resulting in interference due to air-drafts. We use sparse experimental data to estimate the interference area between palm sized quadcopters and to derive physics-infused models that describe how the air-draft generated by two qu...

This paper proposes a multi-sensor based approach to detect, track, and localize a quadcopter unmanned aerial vehicle (UAV). Specifically, a pipeline is developed to process monocular RGB and thermal video (captured from a fixed platform) to detect and track the UAV in our FoV. Subsequently, a 2D planar lidar is used to allow conversion of pixel da...

Small multi-rotor unmanned aerial vehicles (UAVs) are poised to revolutionize commercial and logistics sectors through their versatility, maneuverability, and rapidly increasing sophistication and decreasing costs. However, these robotic systems also produce a substantial and overpowering level of acoustic noise that can potentially distract or har...

Integrating simplified or partial physics models with data-driven machine learning models is an emerging concept targeted at facilitating generalizability and extrapolability of complex system behavior predictions. In this paper, we introduce a novel machine learning based fusion model MIDPhyNet that decomposes, memorizes, and integrates first prin...

In surrogate-based optimization (SBO), the deception issues associated with the low fidelity of the surrogate model can be dealt with in situ model refinement that uses infill points during optimization. However, there is a lack of model refinement methods that are both independent of the choice of surrogate model (neural networks, radial basis fun...

Aperiodic metamaterials represent a class of structural systems that are composed of different building blocks (cells), instead of a self-repeating chain of the same unit cells. Optimizing aperiodic cellular structural systems thus presents high-dimensional design problems, that become intractable to solve using purely high-fidelity structural anal...

Hybrid modeling architectures seek to combine a machine learning model with a computationally efficient (simplified or partial) physics model to predict the behavior of physical systems. Existing sequential or parallel approaches to hybrid modeling do not typically exploit the potential relationship between the input or latent features of the parti...

Aperiodic metamaterials represent a class of structural systems that are composed of different building blocks (cells), instead of a self-repeating chain of the same unit cells. Optimizing aperiodic cellular structural systems thus presents high-dimensional problems that are challenging to solve using purely high-fidelity structural optimization ap...

Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency and fault tolerance. To guide the behavior of such swarm systems, two broad...

Reduced-order models that accurately abstract high fidelity models and enable faster simulation is vital for real-time, model-based diagnosis applications. 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. We...

In this paper, a novel hybrid unmanned aerial vehicle (UAV) concept is developed. This UAV is capable of transitioning between VTOL, hover, and efficient (fixed-wing type) forward flight. The overall configuration comprises a blended-wing-body, with two rotor arms mounted at the two wing tips using span-wise shafts; the arms can rotate about the sp...

We propose a theoretical framework for joint system identification and control on a class of stochastic linear systems. We investigate optimization algorithms for inferring endogenous and environmental parameters from data, part of which are used for control purposes. A number of non-trivial interplays among stability and performance, as well as co...

We introduce a novel machine learning-based fusion model, termed as PI-LSTM (Physics-Infused Long Short-Term Memory Networks) that integrates first principle Physics-Based Models and Long Short-Term Memory (LSTM) network. Our architecture aims at combining equation-based models with data-driven machine learning models to enable accurate predictions...

The ability to avoid collisions with each other is one of the fundamental requirements for autonomous unmanned aerial vehicles (UAVs) to be safely integrated into the civilian airspace, and for the viability of multi-UAV operations. This paper introduces a new approach for online cooperative collision avoidance between quadcopters, involving recipr...

Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavi...

This paper focuses on the idea of energy efficient cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy they change their...

This paper presents the conceptual design and fabrication/assembly of an autonomous solar powered small unmanned ground vehicle (UGV) platform for operation in outdoor environments. The contribution lies in the ability of the proposed design to offer uninterrupted operation in terms of endurance, to facilitate educational and research applications...

Over the past several years, microgrids have been setup in remote villages in developing countries such as India, Kenya and China to boost the standards of living of the less privileged citizens, mostly by private companies. However, these systems succumb to increase in demand and maintenance issues over time. A method for scaling the capacity of s...

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, single radio technology can hardly deliver optimal performance across all critical quality of service (QoS) dimension...

Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search metho...

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. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task dead...

Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potential online cooperative approach is the concept of reciprocal actions, where both UAVs take pre-trained mutually coherent actions that do not require active online coordin...

This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to "when" to sample and "how many" samples to add (to preserve the credibility of the optimization search pr...

Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavi...

Over the past several years, microgrids have been setup in remote villages in developing countries such as India, Kenya and China to boost the standards of living of the less privileged citizens, mostly by private companies. However, these systems succumb to increase in demand and maintenance issues over time. This paper explores a method for scali...

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. This paper builds on the Neuro Evolution of Augmented Topologies (NEAT) formalism that allows designing topology and weight evolving NNs. Fundamental...

Multiple simple agents working together to achieve a common complex goal embodies the underlying theme of swarm concepts, with decentralized decision-making serving as the new frontier for tackling challenges associated with scalability, fault tolerance, and communication constraints. This paper builds on this emerging paradigm to develop a distrib...

We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron...