To ensure the operational integrity of military ground robots in hostile environments, we present a cutting-edge cybersecurity framework that leverages deep learning via a convolutional neural network (CNN). While primarily designed to enhance the security of military ground vehicles, the framework also improves the cybersecurity of the Robot Operating System (ROS), a widely adopted middleware across civilian and military robotic platforms. The GVR-BOT, developed by the United States Army Combat Capabilities Development Command (CCDC) Ground Vehicle Systems Center, serves as the experimental platform. To evaluate the robustness of our system, we conduct real-time penetration testing to identify vulnerabilities within ROS onboard the ground vehicle. Our approach involves training the model on two datasets of normalized network traffic, represented as RGB or grayscale images, to capture vehicle behavior under adversarial conditions. The theoretical foundation is supported by a formal convergence lemma, which guarantees that the CNN model minimizes classification loss under standard smoothness and learning rate assumptions. Experimental results demonstrate the efficacy of our framework, achieving over 98% detection accuracy and under 1% false positive rate, while maintaining minimal detection latency. We also compare different windowing techniques to assess performance trade-offs.

Recent advances in robotics have increased the reliance on networked middleware, such as Robot Operating System 2 (ROS 2), for orchestrating Unmanned Ground Vehicles (UGVs) in dynamic and safety-critical settings. However, this distributed architecture exposes platforms like the Turtlebot4 to Denial-of-Service (DoS) vulnerabilities, which can incapacitate critical sensor and control messages. This paper presents a deep learning-based framework for near-real-time detection of DoS attacks in a ROS 2-enabled UGV. A novel adversarial script, generated using a Large Language Model (LLM), systematically floods targeted ROS 2 topics to simulate genuine network-based intrusions. The resulting dataset, comprising both benign and adversarial conditions, undergoes rigorous cleaning, feature engineering, and one-second binning to capture key metrics such as burstiness and topic entropy. Four neural architectures are evaluated: a Multilayer Perceptron (MLP), a Long Short-Term Memory (LSTM), a 1D Convolutional Neural Network (CNN), and an unsupervised Variational Autoencoder (VAE). Experimental findings show that the MLP, leveraging the engineered features, achieves 97% accuracy and an AUC of 0.998 in classifying DoS traffic, outperforming sequence-based models and the VAE. Integrated Gradients analysis further reveals that metrics reflecting abrupt message spikes and imbalanced topic usage are highly discriminative of malicious behavior. These results underscore the impact of domain-specific feature design in safeguarding ROS 2-based robotic platforms against emerging cyber threats.

A significant threat to GPS users, including Unmanned Aerial Vehicles (UAVs), is the location spoofing attack, which can mislead systems with false GPS signals, jeopardising their operations and safety. To address this challenge, this study presents a method for verifying GPS spoofing attacks on UAV systems. The proposed solution develops a robust methodology by analysing the reported position of the UAV, along with various features of the received signal, such as the signal-to-noise ratio (SNR), azimuth, and pitch angles, at multiple base stations. Additionally, we consider Nakagami fading channels to model the properties of the received signal, which are relevant to real-world scenarios. We developed a smart verification algorithm using the Recurrent Neural Network (RNN) to authenticate the position reported by UAVs based on SNR, azimuth, and pitch angle data at various base station antennas. We have utilised both simple recurrent neural network (SRNN) and long short-term memory (LSTM) algorithms to evaluate and compare the performance of the model by varying the number of base stations. The performance of the algorithm was evaluated using confusion metrics, including accuracy, precision, and the F1 score. In addition, we have compared the performance of the proposed model with the models proposed in the previous study, which were built based on the received signal strength (RSS). The results show that the effectiveness of the models improves as the number of base stations increases.

The ability to recognize motions is an important feature in cutting-edge robotics or autonomous systems, such as self-driving cars, humanoid robots, and human-robot interactions, resulting in improved safety and efficiency. Addressing this critical issue, we introduce a simple framework leveraging the benefits of the normalized real-time network traffic data of the middleware ROS platform formulated in the form of RGB or grayscale images to train the Convolutional Neural Network (CNN) system in order to learn the motion pattern of the robot. For our experimental platform, we employ the GVR-BOT Unmanned Ground Vehicle (UGV), developed by the U.S. Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center (GVSC). We rigorously study the performance of our motion recognition system under several different lengths of data (epochs). In addition, we compare the relative merits of our proposed system with respect to the performance of the well-known ‘Bag-of-Features’ (BoFs) detection algorithm widely implemented in computer vision. Our research indicates the efficacy of the proposed motion recognition system as we can achieve a reasonably high detection accuracy ≥ 0.97 within a minimum detection time of two epochs highlighting its real-time benefits. Overall, our recognition system can also achieve superior detection performance compared to the efficacy of the BoFs algorithm.

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in various fields. A major concern with UAV operation is their limited power capacity which impacts mission planning, operational efficiency, and battery management, presenting significant research and engineering challenges. This paper evaluates the applications of multiple AI algorithms in predicting the energy consumption of low-cost quadcopter drones. One of the primary contributions involves developing four prediction models, including random forest, regression tree, support vector machine, artificial neural network, and adaptive Neuro-Fuzzy Inference System (ANFIS) on an open-source dataset of small quadcopter flights. This paper also performs a comparative study on the performance of the aforementioned algorithms in predicting the energy consumption of a UAV. This research enhances the field not only by leveraging established machine learning techniques but also by adopting and examining ANFIS, which has received limited prior research attention. By introducing and applying ANFIS, this study not only expands the existing knowledge but also offers a unique perspective, potentially paving the way for further research, especially in addressing uncertainty like weather conditions. According to our study, the power consumption of the UAV is notably influenced by the aircraft’s altitude, wind speed, and velocity. The Random Forest model demonstrates superior accuracy in forecasting UAV power consumption compared to other models. We also provide an overview of the ongoing challenges and potential future endeavors.

The increased reliance on Unmanned Aerial Vehicles (UAVs) in various industries exalts the security requirements since it is critical to protect these systems from any cyber-attack. GPS spoofing presents an important challenge by deceiving UAVs through false GPS signals that would disrupt their operations, thereby endangering them. As a countermeasure, this study introduces a method of detecting GPS spoofing attacks that are aimed at UAV systems. This involves developing a robust methodology to detect the GPS spoofing attack based on the UAV’s current reported location and Received Signal Strength (RSS) data at several base stations. In this study, we developed a smart verification algorithm using the K-Nearest Neighbors (KNN) algorithm to authenticate the reported locations of UAVs, based on RSS from various base stations antenna. We evaluated the performance of the algorithm using metrics such as accuracy, precision, and F1-score. The results indicate that the algorithm’s effectiveness improves with an increase in the number of base stations used. Additionally, the paper will pinpoint the possible direction for UAV security and the adaptive countermeasures to improve the level of resilience against spoofing tactics, which are rapidly evolving.

Safe and secure operations of robotic systems are of paramount importance. Aiming for achieving the trusted operation of a military robotic vehicle under contested environments, we introduce a new cyber-physical system based on the concepts of deep learning convolutional neural networks (CNNs). The proposed algorithm is specifically designed to reduce the cyber vulnerability of the Robot Operating System (ROS), a well-known middleware platform widely used in both civilian and military robots. To demonstrate the efficacy of the proposed algorithm, we conduct penetration testing (real-time man-in-the-middle cyber attack) on the GVR-BOT ground vehicle, a military ground robot, developed by the United States Army Combat Capabilities Development Command (CCDC), Ground Vehicle Systems Center. The cyber attack also exploits the vulnerability of the Robot Operating System (ROS) employed in its onboard computer. We collect experimental data and train our CNN based on two different operating conditions, namely, legitimate and malicious conditions. We normalize and convert the network traffic data in the form of RGB or grayscale images. We introduce two different types of windowing techniques, namely, the independent and overlapping sliding epochs to efficiently feed the network traffic data to our CNN system. Our research indicates the efficacy of the proposed algorithm as our proposed cyber intrusion detection system can achieve reasonably high accuracy of$\geq 99$% and substantially small false-positive rates$\leq$2 % supported with minimum detection time. In addition, we also compare and demonstrate the relative merits of our proposed algorithm with respect to the performance of some well-known techniques, namely, ‘bag-of-features’ and Support Vector Machine (SVM) algorithms.

Recent developments in cutting-edge robotics have been constantly faced with increased cyber-threats, not only in terms of the quantity or the frequency of attacks, but also when it comes to the quality and the severity of the intrusions. This paper provides a systematic overview and critical assessment of state-of-the-art scientific developments in the security aspects of robotics, autonomous systems, and critical infrastructures. Our review highlights open research questions addressing significant research gaps and/or new conceptual frameworks, considering recent advancements in artificial intelligence (AI) and machine learning. Thus the contributions of this paper can be summarised as follows. We first compare and contrast the benefits of multiple cutting-edge AI-based learning algorithms (e.g., fuzzy logic and neural networks) relative to traditional model-based systems (e.g. distributed control and filtering). Subsequently, we point out some specific benefits of AI algorithms to quickly learn and adapt the dynamics of non-linear systems in the absence of complex mathematical models. We also present some potential future research directions (open challenges) in the field. Lastly, this review also delivers an open message to encourage collaborations among experts from multiple disciplines. The implementation of multiple AI algorithms to tackle current security issues in robotics will transform and create novel, hybrid knowledge for intelligent cybersecurity at the application level.

Leveraging the benefits of deep-learning convolutional neural networks, we introduce a new data-driven cyber–physical system specifically designed to address the vulnerability of middleware software, namely, Robot Operating System (ROS), widely implemented in robotics in both civilian and military domains. As for our research platform, we employ the GVR-BOT unmanned ground vehicle, which is a replicate of the U.S. Army ground robot. We focus our study on the ability of the ground robot to operate under fault-tolerant conditions, making it challenging from the standpoint of cybersecurity to differentiate between legitimate and malicious operations. The GVR-BOT ground vehicle belongs to a class of differential drive ground robots and employs ROS in its onboard computer to interact with users. To facilitate deep learning, we develop a large database of images based on the network-traffic data of ROS, describing the dynamics of the GVR-BOT ground robot under legitimate and malicious operations. We use our image database to train and validate the performance of our deep-learning CNN system. Given a set of RGB/grayscale images describing the normalized time-series data representing the dynamics of the GVR-BOT ground robot, the objective of our proposed cybersecurity algorithm is to safeguard the legitimate operation of the ground robot under fault-tolerant conditions, such that any attempts to compromise its performance (e.g., malicious attacks) can be prevented within the minimum detection time. Our research indicates a promising result as our system is capable of detecting malicious attacks with high accuracy while recognizing its legitimate operations with reasonably small false-positive rates.

Recently, Type-2 fuzzy systems have become increasingly prominent as they have been applied to various nonlinear control applications. This article presents an adaptive fuzzy controller based on the sliding-mode control theory. The proposed self-adaptive interval Type-2 fuzzy controller (SAF2C) is based on the Takagi–Sugeno (TS) fuzzy model and it accommodates the “enhanced iterative algorithm with stop condition” type-reducer, which is more computationally efficient than the “Kernel–Mendel” type-reduction algorithm. We developed an integrated multi-input–multi-output (MIMO) SAF2C-controller to reduce the computation time so that we can expedite the learning process of our control algorithm by 80% compared to separate single-input–single-output (SISO) controllers. The stability of our controller is proven using the Lyapunov technique. To ensure the applicability of the presented control scheme, we implemented our controller on various nonlinear systems, including a hexacopter unmanned aerial vehicle (UAV). We also compare the accuracy of our controller with a conventional proportional–integral–derivative autopilot system. Our research indicates around 20% improvement in its transient response, in addition to achieving a better noise rejection capability with respect to a Type-1 fuzzy counterpart.

Formation control techniques have been widely implemented in networked multirobot systems. In this article, we present a novel framework for swarm multiagent systems based on the relative-position output feedback consensus supported with the new concept of adaptive strictly negative imaginary consensus controllers, leveraging the learning capability of artificial neural networks. For experimental validation, we consider the case of two quadcopters moving together while carrying a dynamic load. We employ Kharitonov's theorem to study the stability of the proposed adaptive control systems. Finally, a rigorous real-time experimental study is conducted to highlight the merits of the proposed formation control algorithms.

This paper presents a hybrid nonlinear control system, comprising of a conventional proportional-differential (PD) controller and a PD-type fuzzy logic autopilot for the trajectory tracking of a quadcopter drone. Given the inherent nature of traditional control, which is model-based, and the essence of fuzzy logic control, which is knowledge-based, the proposed hybrid controllers can provide a more robust solution in the face of uncertainties. Both controllers operate in a parallel incremental form to improve the transient performance and the robustness of the closed-loop control system. Through extensive computer simulations supported by real-time flight tests, this paper highlights the efficacy of the proposed hybrid control system in the presence of some parameter variations, nonlinear aerodynamic models, and some external disturbances (e.g., wind gusts). The Dryden and 1-cos turbulence models are employed to represent the effects of wind gusts under realistic flight environments. The stability analysis of the closed-loop control system is conducted using Lyapunov's indirect method.

We introduce a new configuration of a robust and adaptive autopilot system for a model-scale flapping-wing aircraft. The system is specifically designed to achieve high performance flapping angle tracking in the face of large uncertainties. To describe the dynamics of the system, we leverage the benefits of both first principle modeling and data-driven approach (system identification technique). We introduce a high-performance robust and adaptive nonlinear control system by means of a feedback linearization (FL) technique, supported with an interval Type-2 fuzzy system due to its ability to accommodate the footprint-of-uncertainties (FoUs). While the first stage of our nonlinear control system is to cancel some predictable nonlinearities using an FL technique, the second phase of control is to accommodate the existing uncertainties in the system by way of an interval Type-2 fuzzy control technique, e.g., due to imperfect cancelation and modeling errors. This way, the stability and the robustness of the closed-loop control system can be guaranteed. We quantify the relative merit of our hybrid control system with respect to an FL technique, supported with a fixed gain state feedback controller and a Type-1 fuzzy system. Lastly, we also conduct stability analysis of the overall closed-loop control system.

We introduce a new nonlinear system identification technique, leveraging the benefits of the Type-2 Evolutionary Takagi-Sugeno (T2-ETS) fuzzy system. The major advantage of our proposed system identification technique is mainly due to its ability to learn-from-scratch while accommodating the footprint-of-uncertainties (FoUs). To support its mission to achieve a reasonably high prediction accuracy for uncertain nonlinear dynamic systems, we also introduce a new type reduction method to convert Type-2 fuzzy systems into their Type-1 counterparts. As a part of its efficient pruning strategy, the proposed system incorporates the concept of information entropy to avoid over fitting, which is a highly undesirable issue in modeling. We demonstrate the effectiveness of our system identification technique in achieving a delicate balance between minimizing the complexity of the acquired fuzzy model and maximizing the prediction accuracy. To highlight the efficacy of our algorithm, we employ a set of challenging pH neutralization data, known for its substantial nonlinearity, in addition to the dynamics of a nonlinear mechanical system. We conclude our research by conducting a rigorous comparative study to quantify the relative merits of our proposed technique with respect to the previous ETS algorithm (as its predecessor), the well-known KM-type reduction technique, and the higher-order discrete transfer functions, widely implemented in most conventional mathematical modeling techniques.

Robustness in the face of uncertainties is an important aspect in designing high performance control systems. This paper addresses the problem of accurate trajectory tracking of a small quadcopter unmanned aerial vehicle in the face of uncertainties. Accommodating the worst-case scenario, we propose a hybrid feedback and feedforward autopilot that has the capability to eliminate the cross-coupling disturbance between the lateral and the longitudinal loops with respect to the vertical loop as well as external disturbances (e.g., wind gusts). The proposed control system leverages on the technical benefits of both the nonlinear model predictive control and the fuzzy feedforward compensator. We highlight the efficacy of our hybrid autopilot system with respect to the performance of the conventional PD control systems through rigorous comparative studies. We also present stability analysis of our hybrid control system.

Interval Type-2 fuzzy controllers have become increasingly popular, and have been applied in many engineering applications over the past few decades. In this paper, a knowledge-based interval Type-2 fuzzy controller is proposed to control an inverted pendulum on a cart system in the presence of disturbance, random noise and parameter variations. The proposed controller utilizes the Takagi-Sugeno fuzzy inference system, supported by the Nie-Tan (NT) type-reduction method for the input-output mapping. The adaptation laws for the Type-2 fuzzy consequent parameters are derived based on the sliding mode control (SMC) theory. A comparison study of the proposed interval Type-2 fuzzy controller with a conventional PID controller is investigated in the presence of disturbance, external noise and parameter variations. Simulation results show the efficacy of the proposed controller with respect to a conventional PID controller as indicated by lower RMSE values.

In this work we present non-linear system identification for a class of the dragonfly-like flapping wing aircraft. We model the system in its vertical and all attitude loops (roll, pitch, and yaw) as well as its actuator dynamics. Based on a set of input-output data, obtained from first principle modelling; we perform the entropy fuzzy system identification to derive the open loop dynamics of the aircraft using the Mamdani Fuzzy inference method, which is more intuitive, despite being non-linear. This will make the proposed models well-suited to non-expert users (e.g. average drone operators). Our research indicates that the information entropy is very effective to maximize the system accuracy while avoiding overfitting problems. Through numerical simulation, we demonstrate the efficacy of the proposed fuzzy models as we can achieve reasonably good average modelling accuracy of around 90 % for all attitude loops.

We discuss state-of-the-art intelligent robotic aircraft with the special focus on evolutionary autopilots for small unmanned aerial vehicles (UAVs). Under the umbrella of adaptive autopilots, we highlight the pros and cons of the most widely implemented intelligent algorithms against the navigational and maneuvering capabilities of small UAVs. We present several cutting-edge applications of bioinspired flight control systems that have the capability of self-learning. We also highlight several research opportunities and challenges associated with each technique.

In this paper, we comprehensively discuss the current progress of visual-inertial (VI) navigation systems and sensor fusion research with a particular focus on small unmanned aerial vehicles, known as microaerial vehicles (MAVs). Such fusion has become very topical due to the complementary characteristics of the two sensing modalities. We discuss the pros and cons of the most widely implemented VI systems against the navigational and maneuvering capabilities of MAVs. Considering the issue of optimum data fusion from multiple heterogeneous sensors, we examine the potential of the most widely used advanced state estimation techniques (both linear and nonlinear as well as Bayesian and non-Bayesian) against various MAV design considerations. Finally, we highlight several research opportunities and potential challenges associated with each technique.

Secure physical regions (e.g., border areas, nuclear zones, or military facilities) are often patrolled by networked robotic vehicles that require the capability to rapidly verify the advertised location of a potential intruder based on received signal strengths. In this paper, we develop novel algorithms by which mobile robots can coordinate their motions in order to minimize the time required to verify the advertised location for given accuracy bounds. Our specific contributions on this paper are threefold. Firstly, we develop a framework that uses a combination of the particle filters (for position estimation) and the Cramér-Rao lower bounds (for threshold of validation) to drive the motion models for rapid verification of the reported position. We believe our approach is the first in the literature that is accurate, easy to compute, and feasible for practical implementation. Secondly, we propose a centralized coordinated motion algorithm that is optimal at each sampling time. This provides a lower bound on detection time that can be used as a benchmark for practical considerations. Thirdly, we present a practical heuristic approach that allows for distributed protocol based on the concept of the gradient vectors. Subsequently, we also advocate a sub-optimal approach, derived from our heuristic approach, which provides a good trade-off between performance and computational resources. Our results are important for the development of secure access control schemes to prevent unauthorized access of communication networks from malicious users.

This research aims to propose a novel decentralised coverage protocol known as square-grid blanket coverage control algorithm for self-deployments of autonomous robotic wireless sensor networks in the open corridors as a means to discourage any intrusions. The algorithm advocates distributed solution and is asymptotically optimal in the sense of uniformity. The motion coordination scheme employed is due to the nearest neighbour technique which is implemented for the purpose of clustering and coordination among mobile sensor nodes in local area (vicinity), that is, to achieve uniform and distributed solutions for both 1-barrier coverage as an initial thrust of this research as well as our square-grid blanket coverage. Having successfully achieved 1-barrier coverage, mobile sensor nodes are subsequently moved in the systematic zigzag (snake-like) pattern by utilising local information obtained from its neighbourhoods to achieve the desired square-grid lattice. Research points out that simple motion coordination schemes have resulted in powerful, efficient and intelligent control algorithm to achieve the desired coverage. To manifest the efficacy of the proposed algorithm, several computer simulations have been conducted accordingly.

In this work we develop a new tracking-based wireless intrusion detection algorithm that allows for the identification of malicious vehicle network users who are not at their appropriate locations. Based on a particle filter implementation and detection thresholds set by Cramer-Rao lower bounds we show how our tracking-verification algorithm is capable of verifying any reported positions within a reasonable time frame of order 30 seconds. We explicitly determine how the performance of the algorithm, as measured by detection and false positive rates, is influenced by the amount of tracking information collected. The results presented here are important for implementation of safe vehicular networks where only users at the expected locations can access and participate in the network communications.