360 omnidirectional imagery enables comprehensive scene understanding, making it highly valuable for applications such as autonomous surveillance, robotic navigation, and immersive virtual environments. Unlike conventional imagery, 360 equirectangular videos introduce unique challenges, including viewpoint variations, geometric distortions, illumination shifts, scale changes, wraparound effects, and high computational demands. This review provides the first integrated survey to jointly analyse object detection, tracking, projection strategies, and datasets in omnidirectional imagery, highlighting challenges, limitations, and future research directions to enhance accuracy, efficiency, and robustness. This review synthesises current methodologies, examining key algorithms and frameworks for panoramic detection and tracking, while also discussing projection techniques that transform omnidirectional inputs into tractable representations. Relevant datasets for training and benchmarking are reviewed to contextualise practical evaluation and reproducibility. Diverse approaches, from deep learning architectures to geometric transformations, are evaluated with attention to their strengths, limitations, and applicability to real-world scenarios. Finally, the review outlines open challenges and emerging research opportunities, providing a roadmap for advancing panoramic object detection and tracking, serving as a comprehensive reference for researchers in the field.

Algorithms using swarming collective motion can solve coverage problems in unknown environments by reacting to unknown obstacles in real-time when they are encountered. However, these algorithms face two key challenges when deployed on real robots. First, hand-tuning efficient collective motion parameters is both time-consuming and difficult. Second, predicting the time required for a swarm to solve a particular problem is not straightforward. This paper introduces a novel evolutionary framework to address both problems by proposing a methodology that autonomously tunes collective motion parameters for coverage problems while predicting the time required for real robots to complete the task. Our approach utilizes a simulation–optimization framework that employs a genetic algorithm to optimize the parameters of a frontier-led swarming algorithm. Results indicate that the optimized parameters are transferable to real robots, achieving 100% coverage while maintaining 84% connectivity between them. Compared to state-of-the-art swarm methods, our system reduced turnaround time by 50% and 57% in different environments while maintaining collective motion. It also achieved a 55% reduction in turnaround time on average across five scenarios compared to budget-constrained path planning, with a 10% increase in coverage. Furthermore, our framework outperformed both hand-tuned and learned collective motion approaches, reducing turnaround time by 73% in non-collective motion scenarios and by 63% while maintaining 85% connectivity in collective motion scenarios. This approach effectively combines the adaptability of swarm behavior with the predictive reliability of planning methods. • Proposed a framework for optimizing frontier-led swarming parameters. • Utilized a genetic algorithm to enhance swarm robot performance in coverage tasks. • Developed a dynamic swarm simulator for a seamless transition to physical robots. • Evaluated swarm performance metrics balancing connectivity and coverage time. • Results highlight the effectiveness of optimized parameters in real-world scenarios.

With the arrival of deep learning and advanced sensor technologies, the autonomous vehicle domain has gained increased research interest. In particular, deep learning networks developed based on 3D LiDAR sensing data for perception and planning in autonomous vehicles demonstrate remarkable performance. However, recent research reveals vulnerabilities in LiDAR-based perception tasks, such as 3D object detection and segmentation, to intentionally crafted adversarial perturbations. Yet, the adversarial robustness of LiDAR-based regression tasks like scene flow estimation, remains largely unexplored. Therefore, this study introduces a novel point perturbation attack named FlowCraft, based on two loss functions, along with a critical analysis of selecting the adversarial objective against scene flow estimation. In particular, evaluations are conducted on trainable, runtime optimization, supervised, and self-supervised, scene flow estimation methods using the Argoverse 2 and Waymo datasets in both black-box and white-box settings. Experimental results on the Argoverse 2 benchmark dataset and the DeFlow network show that FlowCraft achieves a relative endpoint error increment of 2.9, while demonstrating a higher endpoint error increase of 5.5 per unit change in Chamfer Distance compared to PGD and CosPGD attacks. Furthermore, our results demonstrate that the performance of point perturbation attacks against runtime optimization methods involves a trade-off between their success rate and overall imperceptibility. • Explain why adding perturbations to the point cloud at t=t+1 is more effective for evaluations. • Introduce the FlowCraft attack against scene flow estimation, which is based on dual loss functions. • The white-box version of the FlowCraft attack against the DeFlow network on the Argoverse 2 dataset achieves a relative endpoint error increment of 2.9, and a relative endpoint error increment of 1.7 on the Waymo dataset. • Evaluate the black-box transferability of FlowCraft against supervised, self-supervised methods, trainable and run-time optimization-based methods, using the Argoverse 2 and Waymo datasets. • FlowCraft outperforms PGD and CosPGD attacks in terms of attack effectiveness and imperceptibility under trainable approaches.

LiDAR-based 3D perception is a focal point in autonomous vehicle research due to its efficacy in real-world environments and falling costs. However, recent research reveals challenges with LiDAR sensing under corruptions that occur due to adverse weather conditions and sensor-level errors, known as common corruptions. In particular, the majority of these corruptions lead to sparsity or noise in LiDAR point clouds, degrading the performance of downstream perception tasks. To address this, we propose a blind inpainting method named 3DR-DIFF, utilizing diffusion networks to reconstruct and segment corrupted point clouds. 3DR-DIFF comprises two key components: a corrupted region prediction network, acting as a binary mask predictor, and a conditional diffusion network. The evaluation results demonstrate that the 3DR-DIFF is able to reconstruct the LiDAR samples with a depth error of less than 0.56 mean absolute error (MAE) and an intensity error of 0.02 MAE, along with an average segmentation performance of 0.43 mean intersection over union. Furthermore, benchmarking results highlight that 3DR-DIFF outperforms state-of-the-art methods in reconstructing LiDAR beam-missing scenarios, exhibiting an approximately 9.2% lower error for a degradation of 1 MAE.

Mission Planning for Multi-Uncrewed Ground Vehicle (multi-UGV) missions is a key functional module for achieving effective autonomy and coordination within a fleet of vehicles. However, the complexity of mission planning is compounded by the interconnected sub-problems involved and the challenging environments encountered by UGVs. Aiming to devise efficient and effective techniques to tackle the intricacies of mission planning in complex and cluttered environments, this paper presents an algorithmic architecture tailored for hierarchical multi-UGV mission planning systems. Specifically, this paper designs a Modified Cost Approximation Method integrated with two-layer environmental modelling for fast estimation of the travelling cost graphs of target points. A Hybrid Clustering Method that merges k-means clustering with a marginal cost-based assignment is proposed to streamline task decomposition and task assignment. Furthermore, a three-layer path planner is developed by integrating A*, post-processing steps, and Multi-operator Continuous Ant Colony optimisation, aiming to find paths with reduced cost for UGVs in challenging terrains. To evaluate the proposed techniques, a benchmark set for multi-UGV mission planning problems is designed using the robotic simulation platform CoppeliaSim. Simulation results demonstrate the superior performance of the proposed planning techniques.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

At present the perception system of autonomous vehicles is grounded on 3D vision technologies along with deep learning to process depth information. Although deep learning models for 3D perception give promising results, recent research demonstrates that they are also vulnerable to adversarial attacks similar to deep learning models trained on 2D images. As a result, it is essential to further explore the vulnerabilities of 3D perception models in autonomous vehicles and find methods to cope with the risks associated with these adversarial vulnerabilities, in order to improve the social acceptance of commercial autonomous vehicles. This study aims to provide an in-depth overview of the recent adversarial attacks and countermeasures against 3D perception models on autonomous vehicles. Further, challenges associated with the research domain and future research directions are highlighted to make autonomous vehicles robust against adversarial attacks.

Recognition of swarm behavior is important for two reasons. First, it permits the early detection of adversarial collective motion behaviours such that counter-collective motion can be activated. Second, it permits the monitoring and assessment of own swarms to enable detecting any disruptions to the required behavior. Existing work in this area requires feature-based data provided by an external observer that has access to all the swarm states that could not be available all the time. However, the need for pre-processing swarm data to calculate its features can lead to inefficient behavior recognition. This paper addresses this limitation by using raw video data for swarm behavior recognition. This paper proposes a new framework to autonomously recognize structured collective behavior in swarm robots from camera data First, we present a dataset of both collective motion and random behaviors of Pioneer 3DX robots. Then we formulate the recognition problem as a spatiotemporal learning problem. A recurrent neural network is used as the main building block to evaluate each video representation—Our experimental results showed that this video-based recognition without any data pre-processing results in high accuracy that is comparative to feature-based recognition techniques. The proposed model is able to efficiently recognise robots’ behaviour with 99.8% accuracy. Also, our methodology can distinguish behaviours of real robots with 79% accuracy even with different numbers of agents.

Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to their versatility and simple design. However, the tuning of gains for quadrotor flight controllers can be laborious, and accurately stable control of trajectories can be difficult to maintain under exogenous disturbances and uncertain system parameters. This article introduces a novel robust adaptive control synthesis methodology for a quadrotor robot's attitude and altitude stabilization. The proposed method is based on the fuzzy reinforcement learning and strictly negative imaginary (SNI) property. The first stage of our control approach is to transform a nonlinear quadrotor system into an equivalent negative-imaginary (NI) linear model by means of the feedback linearization (FL) technique. The second phase is to design a control scheme that adapts online the SNI controller gains via fuzzy Q -learning. The performance of the designed controller is compared with that of a fixed-gain SNI controller, a fuzzy-SNI controller, and a conventional PID controller in a series of numerical simulations. Furthermore, the proofs for the stability of the proposed controller and the adaptive laws are provided using the NI theorem.

This paper proposes an innovative approach to coverage path planning and obstacle avoidance for multiple Unmanned Ground Vehicles (UGVs) in a changing environment, taking into account constraints on the time, path length, number of UGVs and obstacles. Our approach leverages deformable virtual leader-follower formations to enable UGVs to adapt their formation based on both planned and real-time sensor data. A hierarchical block algorithm is employed to identify areas in the environment where UGV formations can spread out to meet time and budget constraints. Additionally, we introduce a novel control scheme that allows each UGV to generate a local steering force to dodge any static and mobile obstacles based on the closest safe angle. Results from simulations and real UGV experiments demonstrate that our approach achieves a higher coverage percentage than rule-based and reactive swarming approaches without planning. Our approach offers a promising solution for efficient coverage path planning and obstacle avoidance in complex environments with multiple UGVs.

Monocular depth estimation is an important task that can be applied to many robotic applications. Existing methods focus on improving depth estimation accuracy via training increasingly deeper and wider networks, however these suffer from large computational complexity. Recent studies found that edge information are important cues for convolutional neural networks (CNNs) to estimate depth. Inspired by the above observations, we present a novel lightweight Edge Guided Depth Estimation Network (EGD-Net) in this study. In particular, we start out with a lightweight encoder-decoder architecture and embed an edge guidance branch which takes as input image gradients and multi-scale feature maps from the backbone to learn the edge attention features. In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module (TRFA). TRFA captures the long-range dependencies between the context information and edge attention features through cross-attention mechanism. We perform extensive experiments on the NYU depth v2 dataset. Experimental results show that the proposed method runs about 96 fps on a Nvidia GTX 1080 GPU whilst achieving the state-of-the-art performance in terms of accuracy.

As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of our knowledge, however, there is not a comprehensive survey of MDE. This paper aims to bridge this gap by reviewing 197 relevant articles published between 1970 and 2021. In particular, we provide a comprehensive survey of MDE covering various methods, introduce the popular performance evaluation metrics and summarize publically available datasets. We also summarize available open-source implementations of some representative methods and compare their performances. Furthermore, we review the application of MDE in some important robotic tasks. Finally, we conclude this paper by presenting some promising directions for future research. This survey is expected to assist readers to navigate this research field.

Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU.

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.

Considerable progress has been made in improving the estimation accuracy of cognitive workload using various sensor technologies. However, the overall performance of different algorithms and methods remain suboptimal in real-world applications. Some studies in the literature demonstrate that a single modality is sufficient to estimate cognitive workload. These studies are limited to controlled settings, a scenario that is significantly different from the real world where data gets corrupted, interrupted, and delayed. In such situations, the use of multiple modalities is needed. Multimodal fusion approaches have been successful in other domains, such as wireless-sensor networks, in addressing single-sensor weaknesses and improving information quality/accuracy. These approaches are inherently more reliable when a data source is lost. In the cognitive workload literature, sensors, such as electroencephalography (EEG), electrocardiography (ECG), and eye tracking, have shown success in estimating the aspects of cognitive workload. Multimodal approaches that combine data from several sensors together can be more robust for real-time measurement of cognitive workload. In this article, we review the published studies related to multimodal data fusion to estimate the cognitive workload and synthesize their main findings. We identify the opportunities for designing better multimodal fusion systems for cognitive workload modeling.

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 three-layered approach for the mission route planning problems involving a team of autonomous vehicles where they have to collectively navigate to a number of target locations in an environment with both static and dynamic obstacles. The first layer computes the maximum distance that need to be traveled to complete a mission by a team of vehicles. We have developed a nearest-neighbor-search based approach to assign closely located tasks to each vehicle in the team. We developed a stochastic optimization based path planning algorithm that can compute the collision-free (with both static and dynamic obstacles) trajectory for a vehicle to navigate from start to the target location. By combining task assignment with path planning algorithm, we can estimate the maximum traveled distance for a mission with a team of vehicles. The second layer determines the optimal number of vehicles required for a mission based on any user defined constraint by casting it as a multiobjective optimization problem with two competing objectives, i.e. time vs cost. The methods derived in layer one are utilized to evaluate the objective functions in layer two. Finally, we have proposed a data driven neural network-based prediction model that will forecast the mission completion time with a reasonable accuracy which will utilize the historical information of the previous missions. The forecasting model is intended to facilitate the effective planning of parallel and subsequent missions. We have demonstrated the effectiveness of our approach with numerical simulation results for every layer mentioned above.

Finding synergy between a variety of control and estimation approaches can lead to effective solutions for controlling nonlinear dynamic systems in an efficient and systematic manner. In this paper, a novel controller design consisting of generalized regression neural networks (GRNNs) and sliding mode control (SMC) is proposed to control nonlinear multi-input and multi-output (MIMO) dynamic systems. The proposed design transforms GRNN from an offline regression model to an online adaptive controller. The suggested controller does not require any pretraining and it learns quickly from scratch. It uses a low computational complexity algorithm to provide accurate and stable performance. The proposed controller (GRNNSMC) performance is verified with a generic MIMO nonlinear dynamic system and a hexacopter model with a variable center of gravity. The results are compared with the standard PID controller. In addition, the stability of the GRNNSMC controller is verified using the Lyapunov stability method.

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.

A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant errors may arise when the well-designed controller is being deployed onto the targeted platform. Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross the reality gap via online adaptation. Through a set of experiments we show that the evolved plastic spiking controller can maintain its functionality by self-adapting to model changes that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart.

This paper describes a study on the perceived risk and trust of members of the general public regarding artificial intelligence applications. It assesses whether there is a difference in the perceptions of risk and trust in artificial intelligence expressed by the general public compared with those studying computer science in higher education. We define the general public as people having no specific level or specialist knowledge of AI yet with a high stake as potential users of AI systems on a regular basis with or without their knowledge. In the study, participants engaged in an AI debate on topical news articles at a public national science museum event and a University in the UK and completed a questionnaire with two sections: their assessment of trust and risk of an AI application based on a topical news story, and a set of general opinion questions on AI. Results indicate that in specific applications there is a significant difference of opinion between the two groups with regards to risk. Both groups strongly agreed that education in how AI works was significant in building trust.

Swarm guidance, such as the case of guiding a group of sheep away from a field, is a challenging task. As the swarm size increases, it becomes necessary that multiple control points, or sheepdogs, are needed to guide the swarm. In this paper, a swarm of unmanned aerial vehicles (UAVs) acts as a moving safety network (aka a formation) that not only guides the sheep swarm, but also prevents them from dispersing or reversing to the other side of the field. We investigate two types of formations. The first type acts as a baseline, maintains fixed distances from the sheep swarm, and relies on fixed predefined angular structure relative to the sheep's global centre of mass (GCM). The second type is dynamic, where the force vector to control the UAV and the individual distance of each UAV from the sheep's GCM are controlled by a Perceptron, with the weights optimized by a particle swarm optimization algorithm. We evolve five Perceptrons to specialize in relative positions in the formation, which fixes the space cost for the optimization algorithm, while allowing the size of the swarm of UAVs to scale up. We demonstrate that the use of Perceptron-networks for dynamic control scheme reduces the total distance travelled by the UAVs, is transparent when interpreted with Hinton diagrams, and transferable to a larger number of UAVs.

In recent times, with the incremental demand for fully autonomous systems, research interests are observed in learning machine-based intelligent, self-organizing, and evolving controllers. In this paper, a new evolving and self-organizing controller, namely generic-controller (G-controller), is proposed. The G-controller works in a fully online mode with minor expert domain knowledge. It is developed by incorporating the sliding mode control (SMC) theory with an advanced incremental learning machine, namely generic evolving neuro-fuzzy inference system. The controller starts operating from scratch with an empty set of fuzzy rule, and therefore, no offline training is required. To cope with the changing dynamic characteristics of the plant, the controller can add or prune the rules on demand. Control law and adaptation laws for the consequent parameters are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of the G-controller is guaranteed by using the Lyapunov function. The uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. In addition, the implementation of the multivariate Gaussian function helps the controller to handle the nonaxis parallel data from the plant and consequently, enhances the robustness against uncertainties and environmental perturbations. Finally, the controller's performance has been evaluated by observing the tracking performance in controlling simulated plants of unmanned aerial vehicle, namely bio-inspired flapping wing micro air vehicle and hexacopter for a variety of trajectories.

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.

In this article, a novel self-evolving general regression neural network (SEGRNN) is designed for tracking control of a class of discrete-time dynamic systems with unknown dynamics and unknown external disturbances. The proposed controller starts from scratch and automatically adjusts its structure and parameters online to solve the tracking control problem. The proposed controller can add, prune, and replace nodes online according to the control task, external disturbance, and the design specifications. A robustifying control term is also added to SEGRNN's output to mitigate the effects of the external disturbance. The concept of a data reservoir is proposed where a record of the deleted nodes is stored for any future recall, if they are seen to be significant again. Unlike most of the previously proposed self-evolving systems, our controller offers user-friendly design parameters to suit a variety of real-world systems. Lyapunov stability analysis is utilized to study the stability of the suggested controller and to determine an appropriate learning rate for the SEGRNN weights. A continuous stirred-tank reactor simulation example is employed to verify the performance of the proposed controller. The performance of the proposed controller is also compared with a variety of controllers, including adaptive radial basis functional networks, adaptive feed-forward neural networks, adaptive fuzzy logic system, proportional integral derivative controller, sliding-mode controller, and iterative learning controller. Finally, a dc motor platform is used to experimentally validate the controller performance.

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.

The public debate and discussion about trust in Computational Intelligence (CI) systems is not new, but a topic that has seen a recent rise. This is mainly due to the explosion of technological innovations that have been brought to the attention of the public, from lab to reality usually through media reporting. This growth in the public attention was further compounded by the 2018 GDPR legislation and new laws regarding the right to explainable systems, such as the use of "accurate data", "clear logic" and the "use of appropriate mathematical and statistical procedures for profiling". Therefore, trust is not just a topic for debate - it must be addressed from the onset, through the selection of fundamental machine learning processes that are used to create models embedded within autonomous decision-making systems, to the selection of training, validation and testing data. This paper presents current work on trust in the field of Computational Intelligence systems and discusses the legal framework we should ascribe to trust in CI systems. A case study examining current public perceptions of recent CI inspired technologies which took part at a national science festival is presented with some surprising results. Finally, we look at current research underway that is aiming to increase trust in Computational Intelligent systems and we identify a clear educational gap.

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.

Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach. In the realm of the fuzzy system community, data stream is handled by algorithmic development of self-adaptive neuro-fuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property that enables effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of SANFSs lies in its design principle, which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of the type-2 fuzzy system. In this paper, a novel SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, which significantly reduces the number of network parameters because it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of which characterize a fully dynamic rule-based system. That is, it is capable of automatically generating, merging, and tuning the hyperplane-based fuzzy rule in the single-pass manner. Moreover, an extension of PALM, namely recurrent PALM, is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of PALM has been evaluated through numerical study with six real-world and synthetic data streams from public database and our own real-world project of autonomous vehicles. The proposed model showcases significant improvements in terms of computational complexity and number of required parameters against several renowned SANFSs, while attaining comparable and often better predictive accuracy.

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.

The General Data Protection Regulation (GDPR) will become a legal requirement for all organizations in Europe from 25th May 2018 which collect and process data. One of the major changes detailed in Article 22 of the GDPR includes the rights of an individual not to be subject to automated decisionmaking, which includes profiling, unless explicit consent is given. Individuals who are subject to such decision-making have the right to ask for an explanation on how the decision is reached and organizations must utilize appropriate mathematics and statistical procedures. All data collected, including research projects require a privacy by design approach as well as the data controller to complete a Data Protection Impact Assessment in addition to gaining ethical approval. This paper discusses the impact of the GDPR on research projects which contain elements of computational intelligence undertaken within a University or with an Academic Partner.

Apprenticeship learning is a learning scheme based on the direct imitation of humans. Inverse reinforcement learning is used to learn a reward function from human data. Coupling Inverse reinforcement learning with reinforcement learning has demonstrated production of human-competitive policies. However, obtaining human subjects with the right level of skills for complex tasks can be a challenge. We propose a new learning scheme called Apprenticeship Bootstrapping to learn a composite task using human demonstrations on sub-tasks. The scenario is a ground-air interaction task with an Unmanned Aerial Vehicle that needs to maintain 3 autonomous Unmanned Ground Vehicles within range of an imaging sensor. For validation, we show that the bootstrapped policy performs as good as a policy learnt from a human performing the composite task. The method offers a clear advantage when skilled humans are available for simpler tasks that form the building blocks for a more complex task, where availability of experts is limited.

A formation is a geometric shape that a group of agents spatially organizes themselves into and maintains over time. Swarm Q-Learning (SQL) is a tabular multi-agent reinforcement learning algorithm designed to solve formation control problems. We modify SQL by allowing agents to exchange knowledge they have learnt within the same environment and introduce the Swarm Q-Learning with knowledge Sharing within an Environment (SQL-SIE). The algorithm is tested on a task where a swarm of robots, initially scattered in one side of the environment, needs to navigate through obstacles until they reach their initial positions in the formation within a region of interest. Experimental results show that the proposed SQL-SIE is more efficient than SQL as measured by the time taken by the swarm to complete this part of the mission. Moreover, SQL-SIE scales better than SQL as the number of agents increases.

Nowadays, the application of fully autonomous system like rotary wing unamnned air vehicles (UAVs) are increasing sharply. Due to the complex nonlinear dynamics a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (Gcontroller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent paramters are derived from the sliding model control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain an uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of an UAV namely hexacopter for various trajectories.

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.

This paper describes path replanning techniques and obstacles avoidance for autonomous vehicles, in complex environments. The Vectorized Particle Swarm optimization (VPSO) method is used for searching for an optimal path. A simultaneous replanning concept is incorporated with path planning to avoid static and dynamic obstacles. This proposed simultaneous replanning vectorized particle swarm optimization (SRVPSO) algorithm reduces the computational time of the path planning. Numerical results show that the SRVPSO algorithm provides better replanning time in a dynamic environment.

This paper presents a hierarchical inner-outer loop-based scheme for flight control of a small unmanned helicopter in the presence of input time-delay. The controller is designed based on a two-time-scale separation architecture which includes a fast inner loop and a slow outer loop. The inner-loop (attitude controller) employs an inverse optimal control strategy, which circumvents the tedious task of numerically solving an online Hamilton-Jacobi-Bellman (HJB) equation to obtain the optimal controller. The designed controller is optimal with respect to a meaningful objective function which considers penalties for control input, angular position and angular velocity errors. The outer loop (position) controller uses the backstepping technique to control the position and keep the helicopter on track. Finally, computer simulations are conducted to validate the theoretical results and illustrate the tracking performance of the proposed control method.

When employing autonomous vehicles, it is desirable to use controllers which can be rigorously shown to always ensure safety is maintained. In this manuscript we compare two approaches for the problem of navigation through environments containing obstacles. The first uses boundary following to maintain an avoidance distance to obstacles, and the second uses a MPC-type algorithm to plan short range trajectories around detected obstacles, while ensuring the vehicle can be brought to a halt within the sensor radius. The controllers are subjected to analysis for robustness, and simulations are carried out with both a simple second order linear model and a realistic helicopter model for verification. The controller that planned ahead was found to give significantly better trajectories.

This paper discusses a common approach to systems integration and automation for two different sizes of rotary wing UAVs. A linear model has been identified using time domain methods for both platforms. The identified models will help to evaluate parameters for designing nonlinear control systems for automatic landing of UAVs on moving platforms. In this paper, the innovation is the enhancement of experimental platforms to better suit experimental research in the design of controllers

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. A tether is used for landing and securing a helicopter to the deck of the ship in rough weather. A detailed nonlinear dynamic model for the helicopter is used. This model is underactuated, where the rotational motion couples into the translation. This property is used to design controllers which separate the time scales of rotation and translation. It is shown that the tether tension can be used to couple the translation of the helicopter to the rotation. Two controllers are proposed in this paper. In the first, the rotation time scale is chosen much shorter than the translation, and the rotation reference signals are created to achieve a desired controlled behavior of the translation. In the second, due to coupling of the translation of the helicopter to the rotation through the tether, the translation reference rates are created to achieve a desired controlled behavior of the attitude and altitude. Controller A is proposed for use when the helicopter is far away from the goal, while Controller B is for the case when the helicopter is close to the ship. The proposed control schemes are proved to be robust to the tracking error of its internal loop and results in local exponential stability. The performance of the control system is demonstrated by computer simulations. Currently, work is in progress to implement the algorithm using an instrumented model of a helicopter with a tether.

In this paper, we address the design of an autopilot for autonomous landing of a helicopter on a rocking ship, due to rough sea. The deck is modeled to have a sinusoidal motion. The goal of the helicopter is to land on it during motion. In this work, we use a tether to help in target tracking. Based on the measurement of the angle between the cable and the helicopter/ship, a novel hierarchical two time-scale controller has been proposed to ensure landing of the helicopter on the ship. The system is demonstrated by computer simulation. Currently, work is under progress to implement the algorithm using an instrumented model of a helicopter using a tether.