arXiv subjects

Souma Chowdhury

Publications and source records attributed to Souma Chowdhury.

53 records · Page 3Linked to original sources

arXiv2022

Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field

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 authors, known as Opportunistic Physics-mining Transfer Mapping Architecture or OPTMA, which transfers the original inputs into latent features using a transfer neural network; the partial physics model then uses the latent features to generate the final output that is as close as possible to the high-fidelity output. While gradient-free solvers and back-propagation with supervised learning was earlier used to train OPTMA, that approach is computationally inefficient and challenging to generalize across different problems or popular ML implementations. This paper aims to alleviate these issues by infusing the partial physics model inside the neural network, as described via tensors in the popular ML framework, PyTorch. Such a description also naturally allows auto-differentiation (AD) of the partial physics model, thereby enabling the use of efficient back-propagation methods to train the transfer network. The benefits of the upgraded OPTMA architecture with AD (OPTMA-Net) is demonstrated by applying it to the problem of modeling the sound pressure field created by a hovering unmanned aerial vehicle (UAV). Ground truth data for this problem was obtained from an indoor UAV noise measurement setup. Here, the partial physics model is based on the interference of acoustic pressure waves generated by an arbitrary number of acoustic monopole sources. Case studies show that OPTMA-Net provides generalization performance close to, and extrapolation performance that is 4 times better than, those given by a pure data-driven model.

cs.CE↗

arXiv2022

Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System

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 control actions however remain challenging and computationally prohibitive to generalize over varying launch scenarios and target (debris) state relative to the chaser. To search for a general and reliable control policy, this paper presents a reinforcement learning framework that integrates a proximal policy optimization (PPO2) approach with net dynamics simulations. The latter allows evaluating the episodes of net-based target capture, and estimate the capture quality index that serves as the reward feedback to PPO2. Here, the learned policy is designed to model the timing of the net closing action based on the state of the moving net and the target, under any given launch scenario. A stochastic state transition model is considered in order to incorporate synthetic uncertainties in state estimation and launch actuation. Along with notable reward improvement during training, the trained policy demonstrates capture performance (over a wide range of launch/target scenarios) that is close to that obtained with reliability-based optimization run over an individual scenario.

cs.RO↗

arXiv2021

Using Physiological Information to Classify Task Difficulty in Human-Swarm Interaction

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 task difficulty and adaptively allocate the task to the human operator to maintain optimal performance. In this direction, we study the classification of task difficulty in a human-swarm interaction experiment performing a target search mission. The human may control platoons of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to search a partially observable environment during the target search mission. The mission complexity is increased by introducing adversarial teams that humans may only see when the environment is explored. While the human is completing the mission, their brain activity is recorded using an electroencephalogram (EEG), which is used to classify the task difficulty. We have used two different approaches for classification: A feature-based approach using coherence values as input and a deep learning-based approach using raw EEG as input. Both approaches can classify the task difficulty well above the chance. The results showed the importance of the occipital lobe (O1 and O2) coherence feature with the other brain regions. Moreover, we also study individual differences (expert vs. novice) in the classification results. The analysis revealed that the temporal lobe in experts (T4 and T3) is predominant for task difficulty classification compared with novices.

cs.HC↗

arXiv2021

Learning Robot Swarm Tactics over Complex Adversarial Environments

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 experts. Recent work in automated approaches to learning swarm behavior has been limited to individual primitives with sparse work on learning complete missions. This paper presents a systematic approach to learn tactical mission-specific policies that compose primitives in a swarm to accomplish the mission efficiently using neural networks with special input and output encoding. To learn swarm tactics in an adversarial environment, we employ a combination of 1) map-to-graph abstraction, 2) input/output encoding via Pareto filtering of points of interest and clustering of robots, and 3) learning via neuroevolution and policy gradient approaches. We illustrate this combination as critical to providing tractable learning, especially given the computational cost of simulating swarm missions of this scale and complexity. Successful mission completion outcomes are demonstrated with up to 60 robots. In addition, a close match in the performance statistics in training and testing scenarios shows the potential generalizability of the proposed framework.

cs.MA↗

arXiv2021

Efficient Inverse Design of 2D Elastic Metamaterial Systems Using Invertible Neural Networks

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 specifications within a given global frequency range. This paper develops a computationally efficient framework for (fast) inverse design of LREM, by integrating a new type of machine learning models called invertible neural networks or INN. An INN can be trained to predict the bandgap bounds as a function of the unit cell design, and interestingly at the same time it learns to predict the unit cell design given a bandgap, when executed in reverse. In our case the unit cells are represented in terms of the width's of the outer matrix and middle soft filler layer of the unit cell. Training data on the frequency response of the unit cell is provided by Bloch dispersion analyses. The trained INN is used to instantaneously retrieve feasible (or near feasible) inverse designs given a specified bandgap constraint, which is then used to initialize a forward constrained optimization (based on sequential quadratic programming) to find the bandgap satisfying unit cell with minimum mass. Case studies show favorable performance of this approach, in terms of the bandgap characteristics and minimized mass, when compared to the median scenario over ten randomly initialized optimizations for the same specified bandgaps. Further analysis using FEA verify the bandgap performance of a finite structure comprised of $8\times 8$ arrangement of the unit cells obtained with INN-accelerated inverse design.

cs.CE↗

arXiv2021

Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

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 optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user's input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.

cs.RO↗

arXiv2020

Deep Learning based Multi-Modal Sensing for Tracking and State Extraction of Small Quadcopters

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 data to actual distance measurements, and thereby enable localization of the UAV in global coordinates. The monocular data is processed through a deep learning-based object detection method that computes an initial bounding box for the UAV. The thermal data is processed through a thresholding and Kalman filter approach to detect and track the bounding box. Training and testing data are prepared by combining a set of original experiments conducted in a motion capture environment and publicly available UAV image data. The new pipeline compares favorably to existing methods and demonstrates promising tracking and localization capacity of sample experiments.

cs.CV↗

arXiv2020

Metamodel Based Forward and Inverse Design for Passive Vibration Suppression

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 approaches. Specialized analytical modeling along with metamodel based optimization can provide a more tractable alternative solution approach. To this end, this paper presents a design automation framework applied to a 1D metamaterial system, namely a drill string, where vibration suppression is of utmost importance. The drill string comprises a set of nonuniform rings attached to the outer surface of a longitudinal rod. As such, the resultant system can now be perceived as an aperiodic 1D metamaterial with each ring/gap representing a cell. Despite being a 1D system, the simultaneous consideration of multiple DoF (i.e., torsional, axial, and lateral motions) poses significant computational challenges. Therefore, a transfer matrix method (TMM) is employed to analytically determine the frequency response of the drill string. A suite of neural networks (ANN) is trained on TMM samples (which present minute-scale computing costs per evaluation), to model the frequency response. ANN-based optimization is then performed to minimize mass subject to constraints on the gap between consecutive resonance peaks in one case, and minimizing this gap in the second case, leading to crucial improvements over baselines. 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. This is accomplished by using invertible neural networks, and results show promising alignment with forward solutions.

cs.CE↗

arXiv2020

Decentralized Dynamic Task Allocation in Swarm Robotic Systems for Disaster Response

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 deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involves novel construction and matching of bipartite graphs. Its performance is tested on a multi-UAV flood response application.

cs.MA↗

arXiv2020

Hybrid modeling: Applications in real-time diagnosis

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 are using such models for real-time diagnosis applications. Specifically, we have developed machine learning inspired representations to generate reduced order component models that preserve, in part, the physical interpretation of the original high fidelity component models. To ensure the accuracy, scalability and numerical stability of the learning algorithms when training the reduced-order models we use optimization platforms featuring automatic differentiation. Training data is generated by simulating the high-fidelity model. We showcase our approach in the context of fault diagnosis of a rail switch system. Three new model abstractions whose complexities are two orders of magnitude smaller than the complexity of the high fidelity model, both in the number of equations and simulation time are shown. The numerical experiments and results demonstrate the efficacy of the proposed hybrid modeling approach.

eess.SP↗

arXiv2019

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

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 method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.

cs.MA↗

arXiv2019

Decentralized Informative Path Planning with Exploration-Exploitation Balance for Swarm Robotic Search

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 behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.

cs.MA↗

arXiv2019

Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution

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 coordination (thereby avoiding the computational burden and risk associated with it). This paper presents a learning based approach to train such reciprocal maneuvers. 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. Unlike most existing work (that minimize travel distance, energy or risk), the training process here focuses on the objective of minimizing the required detection range; this has important practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. A specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. In order to allow an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.

cs.NE↗

arXiv2019

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

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 process), it did not provide specific direction towards "where" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria ("q-EI"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where high-fidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).

math.OC↗

arXiv2019

Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents

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 advancements are made to the neuroevolution process to address premature stagnation and convergence issues, central among which is the incorporation of automated mechanisms to control the population diversity and average fitness improvement within the neuroevolution process. Insights into the performance and efficiency of the new algorithm is obtained by evaluating it on three benchmark problems from the Open AI platform and an Unmanned Aerial Vehicle (UAV) collision avoidance problem.

cs.NE↗

arXiv2018

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

Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions for coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. 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. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents' task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7-28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1-3 orders of magnitude faster and minimally sensitive to task-to-robot ratios, unlike the centralized algorithm.

cs.MA↗

arXiv2018

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

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. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios.

cs.NE↗