arXiv subjects

Yaser Mike Banad

Publications and source records attributed to Yaser Mike Banad.

26 records · Page 2Linked to original sources

arXiv2025

Novel Pigeon-inspired 3D Obstacle Detection and Avoidance Maneuver for Multi-UAV Systems

Recent advances in multi-agent systems manipulation have demonstrated a rising demand for the implementation of multi-UAV systems in urban areas, which are always subjected to the presence of static and dynamic obstacles. Inspired by the collective behavior of tilapia fish and pigeons, the focus of the presented research is on the introduction of a nature-inspired collision-free formation control for a multi-UAV system, considering the obstacle avoidance maneuvers. The developed framework in this study utilizes a semi-distributed control approach, in which, based on a probabilistic Lloyd's algorithm, a centralized guidance algorithm works for optimal positioning of the UAVs, while a distributed control approach has been used for the intervehicle collision and obstacle avoidance. Further, the presented framework has been extended to the 3D space with a novel definition of 3D maneuvers. Finally, the presented framework has been applied to multi-UAV systems in 2D and 3D scenarios, and the obtained results demonstrated the validity of the presented method in dynamic environments with stationary and moving obstacles.

cs.RO↗

arXiv2025

Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems Deployment

Presented study introduces a novel distributed cloud-edge framework for autonomous multi-UAV systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual Spiking Neural Network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions. By integrating spike coding with nature-inspired control principles inspired by Tilapia fish territorial behavior, our system achieves sophisticated formation control and obstacle avoidance in complex urban environments. The distributed architecture leverages cloud computing for complex calculations while maintaining local autonomy through edge-based SNNs, significantly reducing energy consumption and computational overhead compared to traditional centralized approaches. Our framework addresses critical limitations of conventional methods, including the dependency on pre-modeled environments, computational intensity of traditional methods, and local minima issues in potential field approaches. Simulation results demonstrate the system's effectiveness across two different scenarios. First, the indoor deployment of a multi-UAV system made-up of 15 UAVs. Then the collision-free formation control of a moving UAV flock including 6 UAVs considering the obstacle avoidance. Owing to the sparsity of spiking patterns, and the event-based nature of SNNs in average for the whole group of UAVs, the framework achieves almost 90% reduction in computational burden compared to traditional von Neumann architectures implementing traditional artificial neural networks.

cs.NE↗

arXiv2024

Neuromorphic Robust Estimation of Nonlinear Dynamical Systems Applied to Satellite Rendezvous

State estimation of nonlinear dynamical systems has long aimed to balance accuracy, computational efficiency, robustness, and reliability. The rapid evolution of various industries has amplified the demand for estimation frameworks that satisfy all these factors. This study introduces a neuromorphic approach for robust filtering of nonlinear dynamical systems: SNN-EMSIF (spiking neural network-extended modified sliding innovation filter). SNN-EMSIF combines the computational efficiency and scalability of SNNs with the robustness of EMSIF, an estimation framework designed for nonlinear systems with zero-mean Gaussian noise. Notably, the weight matrices are designed according to the system model, eliminating the need for a learning process. The framework's efficacy is evaluated through comprehensive Monte Carlo simulations, comparing SNN-EMSIF with EKF and EMSIF. Additionally, it is compared with SNN-EKF in the presence of modeling uncertainties and neuron loss, using RMSEs as a metric. The results demonstrate the superior accuracy and robustness of SNN-EMSIF. Further analysis of runtimes and spiking patterns reveals an impressive reduction of 85% in emitted spikes compared to possible spikes, highlighting the computational efficiency of SNN-EMSIF. This framework offers a promising solution for robust estimation in nonlinear dynamical systems, opening new avenues for efficient and reliable estimation in various industries that can benefit from neuromorphic computing.

eess.SY↗

arXiv2024

Design of Tunable Perfect Absorbers in the Mid-IR Spectrum Using Graphene-Based Multilayer Structures: Emerging Applications in Atmospheric Window Matching

This paper introduces tunable and switchable Perfect Absorbers (PAs) operating within the mid-infrared spectrum, specifically targeting the 3 to 5 um range at 0.25 um intervals. This spectrum is engineered for minimal atmospheric absorption and unique transmission characteristics. Our approach uses graphene-based nanophotonic aperiodic multilayer structures, optimized through micro-genetic algorithms within an inverse design framework. This combination broadens the design space, enabling highly accurate absorption control. Using the Transfer-Matrix-Method for simulations, we tailor absorption characteristics while keeping the structures under 2 um thick. Our results demonstrate PA tunability and switchability by adjusting graphene layers' chemical potentials. For instance, a 4 um peak can shift to 4.22 um by changing the graphene potential from 0 eV to 1 eV, without reducing efficiency. Our research also reveals PA adaptability to incident angles, maintaining 90% absorption up to 52 degrees, demonstrating versatility for applications like thermal photovoltaics, sensors, and stealth technology. This research deepens our understanding of nanophotonic materials and advances optical devices for the mid-IR range.

physics.app-ph↗

arXiv2024

Modeling Melt Pool Geometry in Metal Additive Manufacturing Using Goldak's Semi-Ellipsoidal Heat Source: A Data-driven Computational Approach

This analytical solution, based on Goldak's Semi-Ellipsoidal Heat Source model, captures the dynamic temperature evolution from a semi-ellipsoidal power density moving heat source within a semi-infinite body. It tackles the convection-diffusion heat transfer equation by integrating an instantaneous point heat source across the volume of the ellipsoidal shape. The model's precision is validated by the excellent match between the predicted transient temperatures and empirical data from bead-on-plate specimens, enhancing its capability to accurately predict in-process temperature profiles in laser-based metal additive manufacturing (AM) operations. Developed in Python, the model offers customized calculations from setup to boundary conditions, adapting to variations in material properties under intense heat gradients. It considers the temperature dependency of thermal material properties and AM-process parameters, accounting for significant temperature gradients and changes in heat transfer mechanisms. The model also includes phase changes of melting or solidification with adjusted heat capacity to accurately reflect these transformations. Additionally, it considers the effects of variable laser power, scanning speed, and timing across each scanning pattern segment, acknowledging the thermal interactions between successive layers and their impact on heat transfer. This comprehensive analytical model is ready for applications including thermal stress analysis, microstructure modeling, and simulation of AM processes, predicting residual stresses and distortions.

math.NA↗

arXiv2024

A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules

This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalability, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 111 nJ (0.3% of conventional computing requirements). The results demonstrate the system's adjustment to changing work environments and its efficient use of computational and energy resources, with a moderate increase in energy consumption of 27.2% and 37% for static and dynamic obstacles, respectively, compared to non-obstacle scenarios.

eess.SY↗

arXiv2023

Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks

Concurrent estimation and control of robotic systems remains an ongoing challenge, where controllers rely on data extracted from states/parameters riddled with uncertainties and noises. Framework suitability hinges on task complexity and computational constraints, demanding a balance between computational efficiency and mission-critical accuracy. This study leverages recent advancements in neuromorphic computing, particularly spiking neural networks (SNNs), for estimation and control applications. Our presented framework employs a recurrent network of leaky integrate-and-fire (LIF) neurons, mimicking a linear quadratic regulator (LQR) through a robust filtering strategy, a modified sliding innovation filter (MSIF). Benefiting from both the robustness of MSIF and the computational efficiency of SNN, our framework customizes SNN weight matrices to match the desired system model without requiring training. Additionally, the network employs a biologically plausible firing rule similar to predictive coding. In the presence of uncertainties, we compare the SNN-LQR-MSIF with non-spiking LQR-MSIF and the optimal linear quadratic Gaussian (LQG) strategy. Evaluation across a workbench linear problem and a satellite rendezvous maneuver, implementing the Clohessy-Wiltshire (CW) model in space robotics, demonstrates that the SNN-LQR-MSIF achieves acceptable performance in computational efficiency, robustness, and accuracy. This positions it as a promising solution for addressing dynamic systems' concurrent estimation and control challenges in dynamic systems.

cs.RO↗

arXiv2023

Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach

Energy efficiency and reliability have long been crucial factors for ensuring cost-effective and safe missions in autonomous systems computers. With the rapid evolution of industries such as space robotics and advanced air mobility, the demand for these low size, weight, and power (SWaP) computers has grown significantly. This study focuses on introducing an estimation framework based on spike coding theories and spiking neural networks (SNN), leveraging the efficiency and scalability of neuromorphic computers. Therefore, we propose an SNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategy for well-defined linear systems. Furthermore, based on the modified sliding innovation filter (MSIF) we present a robust strategy called SNN-MSIF. Notably, the weight matrices of the networks are designed according to the system model, eliminating the need for learning. To evaluate the effectiveness of the proposed strategies, we compare them to their algorithmic counterparts, namely the KF and the MSIF, using Monte Carlo simulations. Additionally, we assess the robustness of SNN-MSIF by comparing it to SNN-KF in the presence of modeling uncertainties and neuron loss. Our results demonstrate the applicability of the proposed methods and highlight the superior performance of SNN-MSIF in terms of accuracy and robustness. Furthermore, the spiking pattern observed from the networks serves as evidence of the energy efficiency achieved by the proposed methods, as they exhibited an impressive reduction of approximately 97 percent in emitted spikes compared to possible spikes.

cs.LG↗