This work extends the principle of dual control for exploitation and exploration to robotic assembly tasks, where successful assembly requires both accurate state estimation and informed control actions to reduce uncertainty. The assembly task is formulated as an active inference problem in which the robot maintains a probabilistic belief of target location and executes actions that simultaneously reduce uncertainty (exploration) and drive the system towards alignment(exploitation). The framework is validated on an industrial UR10e robot for circular and square peg-in-hole insertions, representing distinct geometric contact conditions. To realize this, a physics based measurement model is derived that relates contact forces to relative peg-hole position to support Bayesian estimation of the target state. Performance is evaluated under two uncertainty regimes: (i) small initial error (low bias and uncertainty) and (ii) large initial error (high bias and uncertainty), and compared against a single step model predictive control and an entropy-based path planning strategy. Experimental results demonstrate that the proposed approach achieves robust and reliable insertions across a wide range of initial conditions, highlighting the potential of the proposed work for robotic assembly.

This study introduces a $k$ -step look-ahead active concurrent learning-based dual control of exploration and exploitation (KSLCL-DCEE) framework designed to address the challenges of auto-optimization in systems with unknown references and environments, inherently balancing parameter estimation and optimal reference tracking. The KSLCL-DCEE algorithm incorporates two loops that employ future gradients of the cost function to generate the subsequent control command by looking ahead $k$ -steps: the inner loop generates $k$ -step look-ahead gradients (i.e., estimated reference trajectory), while the outer loop utilizes the gradient at the $k$ th step to generate the dual control commands which act on a general linear system. Active concurrent learning with a modified learning rate in the initial period is introduced to relax the reliance on the condition of persistent excitation and achieve faster convergence. A comprehensive stability analysis of KSLCL-DCEE is provided. The effectiveness and performance of KSLCL-DCEE are demonstrated through numerical studies and applications on photovoltaic (PV) arrays.

Safe and reliable control provides a critical guarantee for the stable operation of quadrotor unmanned aerial vehicles (UAVs) in low-altitude scenarios. This article proposed a safety-critical control strategy for a quadrotor UAV through the integration of output constrained tracking and fault-tolerant mechanisms. First, a barrier Lyapunov function is incorporated into a backstepping control framework to rigorously enforce time-varying output constraints. Subsequently, a composite estimation module comprised of a disturbance observer and radial basis function neural networks is constructed to jointly compensate the influence of external disturbances and actuator faults. Furthermore, to address the noise amplification problem, a command filter is employed to derive a virtual control law of the proposed system, while an auxiliary system is designed to alleviate the impact of filter error. Besides, semi-globally bounded stability is proved by the Lyapunov direct method. Finally, the proposed safe and reliable control scheme is implemented on a quadrotor platform, where its effectiveness is verified by both simulations and experiments.

Unmanned Aerial Vehicles (UAVs) are gaining attention for inspections due to their improved safety, efficiency, and accuracy, alongside reduced costs and environmental risks. Visual servoing is crucial for autonomous UAV flight in GPS-degraded environments, guiding the UAV by minimizing errors between observed and desired visual features. This study focuses on Image-Based Visual Servoing (IBVS) control for quadrotor UAVs under complex dynamics and environmental disturbances. A nonlinear model predictive control (MPC) framework is first integrated with visual servoing to handle dynamics nonlinearity, control optimality, and constraints. To address uncertainties and disturbances, a Generalized Extended State Observer (GESO) is incorporated into the MPC, forming the Disturbance-Resilient (DR-) MPC. The GESO estimates the lumped disturbance to improve model predictions within the MPC horizon. The proposed algorithm is validated in a realistic Gazebo environment for UAV pipeline inspection in 3D scenarios, showing better control accuracy and reduced inspection time compared to three baseline methods: IBVS, IBVS-MPC(K) with kinematics, and IBVS-MPC(D) with dynamics.1

Dual Control for Exploitation and Exploration (DCEE) shows promising performance by realizing optimal trade-off between exploitation and exploration under an unknown environment. However, it is computationally intensive and lacks rigorously established properties such as stability and convergence. This paper addresses these two issues by developing the Nesterov Accelerated Gradient Descent (NAGD) based DCEE, i.e. DCEE-NAGD, where the NAGD is applied to both the source term estimation and the path planning in the DCEE framework. It shows that DCEE-NAGD significantly reduces the search time by driving the search agent moving towards the estimated airborne source location (exploitation) and actively searching new data to reduce the current estimation uncertainty (exploration) with the help of NAGD. The convergence of both the source term estimation and the path planning of the DCEE-NAGD algorithm is rigorously established by applying the mean value theorem and mathematical transformation. More specifically, the convergence boundaries and the convergence rates of the source term estimation and the whole DCEE-NAGD algorithm are rigorously established. Both theoretic analysis and simulations confirm the proposed DCEE-NAGD algorithm significantly improves the performance so reduces the autonomous search time.

The quest for optimal operation in environments with unknowns and uncertainties is highly desirable but critically challenging across numerous fields. This paper develops a dual control framework for exploration and exploitation (DCEE) to solve an auto-optimization problem in such complex settings. In general, there is a fundamental conflict between tracking an unknown optimal operational condition and parameter identification. The DCEE framework stands out by eliminating the need for additional perturbation signals, a common requirement in existing adaptive control methods. Instead, it inherently incorporates an exploration mechanism, actively probing the uncertain environment to diminish belief uncertainty. An ensemble based multi-estimator approach is developed to learn the environmental parameters and in the meanwhile quantify the estimation uncertainty in real time. The control action is devised with dual effects, which not only minimizes the tracking error between the current state and the believed unknown optimal operational condition but also reduces belief uncertainty by proactively exploring the environment. Formal properties of the proposed DCEE framework like convergence are established. A numerical example is used to validate the effectiveness of the proposed DCEE. Simulation results for maximum power point tracking are provided to further demonstrate the potential of this new framework in real world applications.Note to Practitioners—In numerous engineering applications, it is highly desirable to operate a system to improve the efficiency, enhance performance or save energy. However, attaining this optimal control is a challenging task, due to the presence of unknown system and/or environment parameters. We develop a principled approach to balance between exploration and exploitation, involving active learning to estimate unknown parameters and tracking the optimal operational condition based on current estimation. This paper provides a unified framework to solve general auto-optimization control problems. The simulation results demonstrate that the proposed method outperforms existing methods in terms of efficiency and optimality for maximum power point tracking problem, and it can be readily implemented for many other engineering problems. Future research include generalizing the proposed method to nonlinear systems, as well as exploring novel applications to facilitate the widespread adoption of our method.

This article addresses challenges of autonomous decisions making influenced by discrete system states, underlying continuous dynamics, and evolving operational environments. A comprehensive framework is proposed, encompassing new modeling, problem formulation, control design, and stability analysis. The framework integrates continuous system dynamics, used for low-level control, with discrete Markov decision processes (MDP) for high-level decision making. To capture the interactions between these domains, the decision-making system is modeled as a hybrid system consisting of a controlled MDP and autonomous (uncontrolled) continuous dynamics, collectively referred to as the hybrid Markov decision process (HMDP). The design focuses on ensuring safety and optimality by accounting for both discrete and continuous state variables across different levels. With the help of the model predictive control (MPC) concept, a decision-making scheme is developed for the hybrid model, with guarantees for recursive feasibility and stability. The proposed framework is applied to the autonomous lane changing system for intelligent vehicles, and simulation shows its capability to handle diverse behaviors in dynamic and complex environments.

The high level of autonomy within autonomous systems demands new control strategies to achieve more complex objectives while ensuring both safety and robustness, rather than relying solely on a given reference. To this end, this article addresses the problem of temporal logic disturbance rejection control (TLDRC) for a class of nonlinear systems subject to disturbances. Signal temporal logic (STL) specifications are introduced for the representation of complex tasks. A control barrier function (CBF), composed of a monotonic function characterizing the temporal behavior of the system and a predicate function, is constructed to encode the STL specifications. To guarantee robustness against disturbances, generalized proportional integral observers (GPIOs) are introduced for higher-accuracy disturbance estimation. It is shown that by fully exploiting the constructed CBF and the disturbance estimate, the developed TLDRC strategy is able to ensure the STL specifications and compensate undesirable effects caused by unknown disturbances, even if they are fast-time-varying. A numerical example is presented to illustrate the effectiveness of the proposed strategy.

In this article, a multi-estimator based computationally efficient algorithm is developed for autonomous search in an unknown environment with an unknown source. Different from the existing approaches that require massive computational power to support nonlinear Bayesian estimation and complex decision-making process, an efficient cooperative active-learning-based dual control for exploration and exploitation (COAL-DCEE) is developed for source estimation and path planning. Multiple cooperative estimators are deployed for environment learning process, which is helpful to improving the search performance and robustness against noisy measurements. The number of estimators used in COAL-DCEE is much smaller than that of the particles required for Bayesian estimation in information-theoretic approaches. Consequently, the computational load is significantly reduced. As an important feature of this study, the convergence and performance of COAL-DCEE are established in relation to the characteristics of sensor noises and turbulence disturbances. Numerical and experimental studies have been carried out to verify the effectiveness of the proposed framework. Compared with the existing approaches, COAL-DCEE not only provides convergence guarantee but also yields comparable search performance using much less computational power.

Source term estimation of a hazardous release remains a topic of significant interest in the robotics and state estimation communities, with application to many safety critical scenarios including gas or nuclear release, locating suspicious smells or response to emergency incidents. Limited sensing resources and time constraints mean that deciding on how to act in order to improve efficiency of estimation is also of significant interest. This paper has two main focuses: a sequential Monte Carlo technique for performing source term estimation from gas concentration measurements taken on a mobile sensor platform and a Monte Carlo tree search (MCTS) framework to perform sensor motion planning to maximise Kullback-Leibler divergence (KLD). Both algorithms are implemented in the open source tracking and estimation framework: Stone Soup, creating several key contributions to this Python based toolkit. The presented algorithm demonstrates superior performance when compared to a greedy myopic alternative when considering source position estimation error, release rate error and successful rate performance measures.

This paper proposes a new 3D gas distribution mapping technique based on Gaussian belief propagation, which is capable of resolving in real time, the concentration estimates in 3D space whilst accounting for the obstacle information within the scenario, the first of its kind in the literature. The gas mapping problem is formulated as a 3D factor graph of Gaussian potentials, the connections of which are conditioned on local occupancy values. The Gaussian belief propagation framework is introduced as the solver and a new hybrid message scheduler is introduced to increase the rate of convergence. The factor graph problem is then redesigned as a dynamically expanding inference task, coupling the information of consecutive gas measurements with local spatial structure obtained by the robot. The proposed algorithm is compared to the state of the art methods in 2D and 3D simulations and is found to resolve distribution maps orders of magnitude quicker than typical direct solvers. The proposed framework is then deployed onboard a ground robot in a 3D mapping and exploration task. The system is shown to be able to resolve multiple sensor inputs and output high resolution 3D gas distribution maps in a GPS denied cluttered scenario in real time. This online inference of complicated gas dispersion provides a new layer of contextual information over its 2D counterparts and enables autonomous systems to take advantage of real time estimates to inform potential next best sampling locations.Note to Practitioners—The motivation of this work arises from the need to develop the robotic gas distribution mapping capability that can provide real-time situational awareness of the scenarios. The output distribution maps can be used to inform human first responders as to what areas of the environment contain a hazard, but looking towards autonomous robots, they can also be used by the robot itself to inform where should be measured next to gather more information about the environment. When performing these mapping tasks in unknown indoor environments, it is very important that the sensing robot can build up the knowledge of its physical surroundings together with how the obstacles in the environment affect the 3D gas distribution. The Gaussian belief propagation algorithm allows us to achieve all of this in real-time onboard the sensing robot, something that is yet to be achieved in the literature.

Inspired by the concept of recently proposed dual control for exploration and exploitation, this article presents a multistep dual control for exploration and exploitation with guaranteed convergence in search for autonomous sources. To deal with an unknown source position and environment, the proposed dual control algorithm faces significant challenges in demonstrating its recursive feasibility and convergence. With the help of the properties of Bayesian estimators, we redesign a multistep dual control for exploitation and exploration algorithm with necessary terminal ingredients and show that the recursive feasibility and the convergence of the modified dual control algorithm are guaranteed. Two simulation scenarios are conducted, which demonstrate that the proposed algorithm outperforms the stochastic model-predictive control approach and the informative path planning approach in terms of searching successful rates and efficiency.

Automating sensing processes is of high interest to both the target tracking and the control community. Active sensing is focused on solving this task, usually with information based or task driven selection of optimal sensing actions. This paper presents an active sensing formulation that combines task based, in the form of standoff tracking, and information based active sensing by implementing the dual control for exploitation and exploration (DCEE) concept to control a mobile sensor platform with a limited field-of-view. The DCEE based cost function is integrated into the Monte Carlo tree search (MCTS) framework for non-myopic decision making. Using the Bernoulli particle filter for single target tracking with bearing-only measurements, the DCEE observer control method is benchmarked against the popular Rényi divergence information metric with two different parameterisations. Whilst the Rényi divergence performs marginally better when considering existence estimation, spatial results clearly demonstrate that our formulation is able to outperform the benchmark algorithm with improved target localisation performance resulting from outmanoeuvring of the target.

This paper proposes an active information-directed reinforcement learning (AID-RL) framework for autonomous source seeking and estimation problem. Source seeking requires the search agent to move towards the true source, and source estimation demands the agent to maintain and update its knowledge regarding the source properties such as release rate and source position. These two objectives give rise to the newly developed framework, namely, dual control for exploration and exploitation. In this paper, the greedy RL forms an exploitation search strategy that navigates the agent to the source position, while the information-directed search commands the agent to explore most informative positions to reduce belief uncertainty. Extensive results are presented using a high-fidelity dataset for autonomous search, which validates the effectiveness of the proposed AID-RL and highlights the importance of active exploration in improving sampling efficiency and search performance.

This article addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed, which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed, where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

This paper advocates the Gaussian belief propagation solver for factor graphs in the case of gas distribution mapping to support an olfactory sensing robot. The local message passing of belief propagation moves away from the standard Cholesky decomposition technique, which avoids solving the entire factor graph at once and allows for only areas of interest to be updated more effectively. Implementing a local solver means that iterative updates to the distribution map can be achieved orders of magnitude quicker than conventional direct solvers which scale computationally to the size of the map. After defining the belief propagation algorithm for gas mapping, several state of the art message scheduling algorithms are tested in simulation against the standard Cholesky solver for their ability to converge to the exact solution. Testing shows that under the wildfire scheduling method for a large urban scenario, that distribution maps can be iterated at least 10 times faster whilst still maintaining exact solutions. This move to an efficient local framework allows future works to consider 3D mapping, predictive utility and multi-robot distributed mapping.

In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game-theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero-sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.

This paper overviews and discusses the relationship between Reinforcement Learning (RL) and the recently developed Dual Control for Exploitation and Exploration (DCEE). It is argued that there are two related but quite distinctive approaches, namely, control and machine learning, in tackling intractability arising in optimal decision making/control problems. In the control approach, the original problems (of an infinite horizon) are approximated by finite horizon problems and solved online by taking advantage of the availability of computing power. In the machine learning approach, the optimal solutions are approximated through iterations, or (offline) training through trials when models are not available. When dealing with unknown environments, DCEE as a technique developed from the control approach could potentially solve similar problems as RL while offering a number of advantages, most notably, coping with uncertainty in environment/tasks, high efficiency in learning through balancing exploitation and exploration, and potential in establishing its formal properties like stability. The links between DCEE and other relevant methods like dual control, Model Predictive Control and particularly Active Inference in neuroscience are discussed. The latter provides a strong biological endorsement for DCEE. The methods and discussions are illustrated by autonomous source search using a robot. It is concluded that DCEE provides a promising, complementary approach to RL, and more research is required to develop it as a generic theory and fully realise its potential. The relationships revealed in this paper provide insights into these relevant methods and facilitate cross fertilisation between control, machine learning and neuroscience for developing autonomous control under uncertain environments.

Deep learning on 3D point clouds has drawn much attention, due to its large variety of applications in intelligent perception for automated and robotic systems. Unlike structured 2D images, it is challenging to extract features and implement convolutional networks over these unordered points. Although a number of previous works achieved high accuracies for point cloud recognition, they tend to process local point information in such a way that semantic information is not fully encoded. In this paper, we propose a deep neural network for 3D point cloud processing that utilizes effective feature aggregation methods emphasizing both generalizability and relevance. In particular, our method uses fixed-radius grouping for pooling layers and spherical kernel convolution for semantics mining. To address the issue of gradient degradation and memory consumption of a deep network, a parallel feature feed-forward mechanism and bottleneck layers are implemented to reduce the number of parameters. Experiments show that our algorithm achieves state-of-the-art results and competitive accuracy in both classification and part segmentation while maintaining an efficient architecture.

Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This article exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms, including unmanned aerial vehicle sensing, multispectral imaging, vegetation segmentation, and deep learning U-Net. A field experiment is designed by infecting winter wheat with yellow rust inoculum, on top of which multispectral aerial images are captured by DJI Matrice 100 equipped with RedEdge camera. After image calibration and stitching, multispectral orthomosaic is labeled for system evaluation by inspecting high-resolution RGB images taken by Parrot Anafi Drone. The merits of the developed framework drawing spectral-spatial information concurrently are demonstrated by showing improved performance over purely spectral-based classifier by the classical random forest algorithm. Moreover, various network input band combinations are tested, including three RGB bands and five selected spectral vegetation indices, by sequential forward selection strategy of wrapper algorithm.

Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) algorithms struggle in POMDP environments because the state of the system, essential in a RL framework, is not always visible. Additionally, hand-designed meta-RL architectures may not include suitable computational structures for specific learning problems. The evolution of online learning mechanisms, on the contrary, has the ability to incorporate learning strategies into an agent that can (i) evolve memory when required and (ii) optimize adaptation speed to specific online learning problems. In this paper, we exploit the highly adaptive nature of neuromodulated neural networks to evolve a controller that uses the latent space of an autoencoder in a POMDP. The analysis of the evolved networks reveals the ability of the proposed algorithm to acquire inborn knowledge in a variety of aspects such as the detection of cues that reveal implicit rewards, and the ability to evolve location neurons that help with navigation. The integration of inborn knowledge and online plasticity enabled fast adaptation and better performance in comparison to some non-evolutionary meta-reinforcement learning algorithms. The algorithm proved also to succeed in the 3D gaming environment Malmo Minecraft.

The lane change maneuver is one of the typical maneuvers in various driving situations. Therefore the automatic lane change function is one of the key functions for autonomous vehicles. Many researches have been conducted in this field. Most existing work focused on the solutions for the static environment and assume that the surrounding vehicles are running at constant speeds. However, in reality, if not all the vehicles on the road are fully autonomous, the situation could be much more complicated and the ego vehicle has to deal with the dynamic environment. This paper proposes a Model Predictive Control (MPC)-based method to achieve automatic lane change in a dynamic environment. A two-wheel dynamic bicycle model, which combines the longitudinal and lateral motion of the ego vehicle, together with a utility function, which helps to automatically determine the target lane have been used in the algorithm. The simulation results have demonstrated the capability of the proposed algorithm in a dynamic environment.

Mobile robotic gas distribution mapping (GDM) is a useful tool for hazardous scene assessment where a quick and accurate representation of gas concentration levels is required throughout a staging area. However, research in robotic path planning for GDM has primarily focused on mapping in open spaces or estimating the source term in dispersion models. Whilst this may be appropriate for environment monitoring in general, the vast majority of GDM applications involve obstacles, and path planning for autonomous robots must account for this. This paper aims to tackle this challenge by integrating a GDM function with an informative path planning framework. Several GDM methods are explored for their suitability in cluttered environments and the GMRF method is chosen due to its ability to account for obstacle interactions within the plume. Based on the outputs of the GMRF, several reward functions are proposed for the informative path planner. These functions are compared to a lawnmower sweep in a high fidelity simulation, where the RMSE of the modelled gas distribution is recorded over time. It is found that informing the robot with uncertainty, normalised concentration and time cost, significantly reduces the time required for a single robot to achieve an accurate map in a large-scale, urban environment. In the context of a hazardous gas release scenario, this time reduction could save lives as well as further gas ingress.

A method was previously developed by this author to optimise the flight path of a fixed wing UAV performing aerial surveys of complex concave agricultural fields. This relies heavily on a flight time in wind prediction model as its cost function. This paper aims to validate this model by comparing flight test results with the model prediction. There are a number of assumptions that this model relies on. The major assumption is that wind is steady and uniform over the small area and time scales involved in a survey. To show that this is reasonable, wind fields measurements will be taken from a multi rotor UAV with an ultrasonic windspeed sensor.

This paper presents experiments to assess the plume mapping performance of autonomous robots. The paper compares several mapping algorithms including Gaussian Process regression, Neural networks and polynomial and piecewise linear interpolation. The methods are compared in Monte Carlo simulations using a well known plume model and in indoor experiments using a ground robot. Unlike previous work on mapping using unmanned vehicles, the indoor experiments were performed in a controlled and repeatable manner where a steady state ground truth could be obtained in order to properly assess the various regression methods using data from a real dispersive source and sensor. The effect of sampling time during data collection was assessed with regards to the mapping accuracy, and the data collected during the experiments have been made available. Overall, the Gaussian Process method was found to perform the best among the regression algorithms, showing more robustness to the noisy measurements obtained from short sampling periods, enabling an accurate map to be produced in significantly less time. Finally, plume mapping results are presented in uncontrolled outdoor conditions, using an unmanned aerial vehicle, to demonstrate the system in a realistic uncontrolled environment.

Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit various driving characteristics impairing the performance of conventional algorithms using all drivers' data indiscriminatingly. This paper develops a personalized driver intention prediction system at unsignalized T intersections by seamlessly integrating clustering and classification. Polynomial regression mixture (PRM) clustering and Akaike's information criterion are applied to individual drivers trajectories for learning in-depth driving behaviors. Then, various classifiers are evaluated to link low-level vehicle states to high-level driving behaviors. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a real-world driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviors than manually defined maneuver due to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM clustering and CART classification provides promising intention prediction performance and is adaptive to different drivers.

In model-based fault detection and isolation (FDI) systems, fault indicating signals (FISs) such as residuals and fault estimates are corrupted by various noises, uncertainties and variations. It becomes challenging to verify whether an FDI system still works or not in real life applications. It is also challenging to select a threshold so that false alarm rate and missed detection rate are kept low depending on real operation conditions. This paper proposes solutions to the aforementioned problems by quantitatively analyzing the effect of uncertainties on FIS. The problems are formulated into reachability analysis problem for uncertain systems. The reachable sets of FIS are calculated under normal and selected faulty cases, respectively. From these reachable sets, the effectiveness of an FDI system can be qualitatively verified under described uncertainties. A dedicated threshold can be further chosen to be robust to all possible described uncertainties. As a by-product, the minimum detectable fault can also be quantitatively determined by checking the intersection of the computed reachable sets. The proposed approach is demonstrated by evaluating an FDI algorithm of a motor in the presence of parameter uncertainties, unknown load, and sensor noises, where a fault estimation-based approach is adopted to diagnose amplifier, velocity, and current sensor faults.

In this paper, a backpropagating constraints-based trajectory tracking control (BCTTC) scheme is addressed for trajectory tracking of a quadrotor with complex unknowns and cascade constraints arising from constrained actuator dynamics, including saturations and dead zones. The entire quadrotor system including actuator dynamics is decomposed into five cascade subsystems connected by intermediate saturated nonlinearities. By virtue of the cascade structure, backpropagating constraints (BCs) on intermediate signals are derived from constrained actuator dynamics suffering from nonreversible rotations and nonnegative squares of rotors, and decouple subsystems with saturated connections. Combining with sliding-mode errors, BC-based virtual controls are individually designed by addressing underactuation and cascade constraints. In order to remove smoothness requirements on intermediate controls, first-order filters are employed, and thereby contributing to backsteppinglike subcontrollers synthesizing in a recursive manner. Moreover, universal adaptive compensators are exclusively devised to dominate intermediate tracking residuals and complex unknowns. Eventually, the closed-loop BCTTC system stability can be ensured by the Lyapunov synthesis, and trajectory tracking errors can be made arbitrarily small. Simulation studies demonstrate the effectiveness and superiority of the proposed BCTTC scheme for a quadrotor with complex constrains and unknowns.

Awareness of driver workload (DW) plays a paramount role in enhancing driving safety and convenience for intelligent vehicles. The DW prediction systems proposed so far learn either from individual driver's data (termed personalized system) or existing drivers' data indiscriminately (termed average system). As a result, they either do not work or lead to a limited performance for new drivers without labeled data. To this end, we develop clustering-aided approaches exploiting group characteristics of the existing drivers' data. Two clustering aided predictors are proposed. The first is clustering-aided regression (CAR) model, where the regression model for the cluster with the highest likelihood is adopted. The second is clustering-aided multiple model regression model, where the concept of multiple models is further augmented to CAR. A recent dataset from real-world driving experiments is adopted to validate the algorithms. Comparative results against the conventional average system demonstrate that by incorporating clustering information, both the proposed approaches significantly improve workload prediction performance.

Adapting in-vehicle systems (e.g., advanced driver assistance systems and in-vehicle information systems) to individual drivers' workload can enhance both safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in an appropriate manner. Rather than developing an average model for all drivers, a personalized driver workload inference (PDWI) system considering individual drivers driving characteristics is developed using machine learning techniques via easily accessed vehicle related measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers workload is first automatically splitted into different categories according to its inherent data characteristics using fuzzy C-means (FCM) clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload type can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating FCM clustering and support vector machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score, and prediction time. The interindividual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness.

Ahstract- This paper presents a method to estimate the original location and the mass of an instantaneous release of hazardous material into the atmosphere. It is formulated as an inverse problem, where concentration observations from a mobile sensor are fused with meteorological information and a Gaussian puff dispersion model to characterise the source. Bayes' theorem is used to estimate the parameters of the release taking into account the uncertainty that exists in the dispersion parameters and meteorological variables. An information based reward is used to guide an unmanned aerial vehicle equipped with a chemical sensor to the expected most informative measurement locations. Simulation results compare the performance between a single mobile sensor with various amounts of static sensors.

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. In this paper, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e., VISSIM) and the corresponding traffic conflicts data generated by the surrogate safety assessment model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e., support vector machines, k-nearest neighbours, and random forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative of the real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e., 30 s, 1 min, 3 min, and 5 min) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of random forests with 5-min temporal aggregation in the classification results. However, attention should be given to the calibration and validation of the simulation software so as to acquire more realistic traffic data, resulting in more effective prediction of conflicts.

This paper proposes the use of a learning approach to predict air-to-ground (A2G) communication strength in support of the communication relay mission using UAVs in an urban environment. To plan an efficient relay trajectory, A2G communication link quality needs to be predicted between the UAV and ground nodes. However, due to frequent occlusions by buildings in the urban environment, modelling and predicting communication strength is a difficult task. Thus, a need for learning techniques such as Gaussian Process (GP) arises to learn about inaccuracies in a pre-defined communication model and the effect of line-of-sight obstruction. Two ways of combining GP with a relay trajectory planner are presented: i) scanning the area of interest with the UAV to collect communication strength data first and then using learned data in the trajectory planner and ii) collecting data and running the trajectory planner simultaneously. The performance of both approaches is compared with Monte Carlo simulations. It is shown that the first implementation results in slightly better predictions, however the second one benefits from being able to start the relay mission immediately.

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only.

As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs' autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then, we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. An indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically.

This paper presents a new standoff tracking framework of a moving ground target using UAVs with a limited sensing capability such as sensor field-of-view and motion constraints. To maintain persistent track of the target even in case of target loss (out of surveillance) for a certain period, this study predicts the target existence area using the particle filter, and produces control commands to ensure that all predicted particles can be covered by the field-of-view of the UAV sensor at all times. To improve target prediction/estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as nonlinear inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control methods are applied for the standoff tracking and phase angle control, and the advantages and disadvantages of them are compared using numerical simulation results.

Autonomous control of unmanned helicopters has drawn a great deal of interests in recent years. This paper investigates two aspects on this topic, namely the flight control and local path planning. The former is designed only using the low-level helicopter dynamics, but the latter take into account the high-level kinematics in an optimisation based framework. A guidance compensator is therefore developed to integrate them in an interacting way. The overall control principle is demonstrated in an indoor flight environment with promising results.

A robust nonlinear predictive controller with a disturbance observer for a permanent magnet synchronous motor (PMSM) is presented. As the disturbance relative degree is less than that of the input, it is quite challenging to adopt the existing disturbance observer-based predictive control techniques. In the proposed controller, robustness of the closed loop system with respect to mismatched parameters and unknown load torque is significantly improved. Stability of the closed-loop system is proved, and the controller is easy to implement. Validity of the proposed controller was experimentally tested on a dSPACE DS1104 board driving a 0.25 kW PMSM drive. Excellent results were obtained with respect to the speed trajectory tracking performance and robustness.

A decentralized feedback control scheme is proposed to synchronize linearly coupled identical neural networks with time-varying delay and parameter uncertainties. Sufficient condition for synchronization is developed by carefully investigating the uncertain nonlinear synchronization error dynamics in this article. A procedure for designing a decentralized synchronization controller is proposed using linear matrix inequality (LMI) technique. The designed controller can drive the synchronization error to zero and overcome disruption caused by system uncertainty and external disturbance.

The inability of airport capacity to meet the growing air traffic demand is a major cause of congestion and costly delays. Airport capacity management (ACM) in a dynamic environment is crucial for the optimal operation of an airport. This paper reports on a novel method to attack this dynamic problem by integrating the concept of receding horizon control (RHC) into a genetic algorithm (GA). A mathematical model is set up for the dynamic ACM problem in a multiairport system where flights can be redirected between airports. A GA is then designed from an RHC point of view. Special attention is paid on how to choose those parameters related to the receding horizon and terminal penalty. A simulation study shows that the new RHC-based GA proposed in this paper is effective and efficient to solve the ACM problem in a dynamic multiairport environment

Airports, especially busy hub airports, proved to be the bottleneck resources in the air traffic control system. How to carry out arrival scheduling and sequencing effectively and efficiently is one of main concerns to improve the safety, capacity, and efficiency of the airports. This paper introduces the concept of receding horizon control (RHC) to the problem of arrival scheduling and sequencing in a dynamic environment. The potential benefits RHC could bring in terms of airborne delay and computational burden are investigated by means of Monte Carlo simulations. It is pointed out that while achieving similar performance as existing schemes, the new arrival scheduling and sequencing scheme significantly reduces the computational burden and provides potential for developing new optimization algorithms for further reducing airborne delay.