Select Publications
Preprints
2024, Active Sensing Strategy: Multi-Modal, Multi-Robot Source Localization and Mapping in Real-World Settings with Fixed One-Way Switching, http://arxiv.org/abs/2407.01308v1
,2023, Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles, http://dx.doi.org/10.48550/arxiv.2312.03493
,2023, Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios, http://dx.doi.org/10.20944/preprints202310.0738.v1
,2023, Coverage Path Planning with Budget Constraints for Multiple Unmanned Ground Vehicles, http://arxiv.org/abs/2306.04083v1
,2022, Lightweight Monocular Depth Estimation with an Edge Guided Network, http://dx.doi.org/10.48550/arxiv.2209.14829
,2022, Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data, http://arxiv.org/abs/2205.02375v2
,2022, Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems, http://arxiv.org/abs/2203.13959v1
,2021, Frontier-led Swarming: Robust Multi-Robot Coverage of Unknown Environments, http://arxiv.org/abs/2111.14295v2
,2021, MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation, http://dx.doi.org/10.48550/arxiv.2111.12334
,2021, Towards Real-Time Monocular Depth Estimation for Robotics: A Survey, http://dx.doi.org/10.48550/arxiv.2111.08600
,2020, Disturbances in Influence of a Shepherding Agent is More Impactful than Sensorial Noise During Swarm Guidance, http://dx.doi.org/10.48550/arxiv.2008.12708
,2020, Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding, http://dx.doi.org/10.48550/arxiv.2004.11543
,2020, Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers, http://dx.doi.org/10.48550/arxiv.2002.09854
,2019, A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach, http://dx.doi.org/10.48550/arxiv.1912.07796
,2019, Evolving Spiking Neural Networks for Nonlinear Control Problems, http://dx.doi.org/10.48550/arxiv.1903.01180
,2018, Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments, http://dx.doi.org/10.48550/arxiv.1811.06196
,2018, Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory, http://dx.doi.org/10.48550/arxiv.1811.06206
,2018, PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles, http://dx.doi.org/10.48550/arxiv.1811.03764
,2018, Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction, http://dx.doi.org/10.48550/arxiv.1810.04344
,2018, Development of a Sliding Mode Control Based Adaptive Fuzzy Controller for a Flapping Flight, http://dx.doi.org/10.48550/arxiv.1806.02945
,2018, Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems, http://dx.doi.org/10.48550/arxiv.1805.11236
,2018, PALM: An Incremental Construction of Hyperplanes for Data Stream Regression, http://dx.doi.org/10.48550/arxiv.1805.04258
,2018, A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System, http://dx.doi.org/10.48550/arxiv.1805.02508
,2018, Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle, http://dx.doi.org/10.48550/arxiv.1802.01262
,2018, Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-inspired Unmanned Aerial Vehicles, http://dx.doi.org/10.48550/arxiv.1802.00635
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