Driving and tilt-hovering help unmanned aerial vehicles extend their time of operation and the visible range of the equipped sensors. Standard quadrotors are underactuated, whose translational and rotational motions are strongly coupled. As a result, it is impossible to maintain the attitude invariably while changing position. Meanwhile, larger flying speed results in greater air resistance, which is also not desirable. A novel aerial platform with tiltable rotors is proposed. Compared with standard quadrotors, the aerial vehicle has three special capabilities: vector-flying, tilt-hovering, and driving. The platform consists of two tilting-axes to change the direction of thrusts with respect to the aircraft body. To guarantee flexible manoeuvres, a geometric tracking control scheme that adapts on the fly to the thrust vectoring is exploited. Manoeuvrability of the proposed vehicle in different manoeuvres is demonstrated, such as flight with vectoring thrust that can reduce the flight resistance, tilt-hovering expanding the reachable set of the equipped sensors. The prototype equipped with an electromechanical controller is constructed, and several associated preliminary experiments are performed. The feasibility of the mechanical design and the geometric control strategy are demonstrated.

Novelty detection is concerned with detecting features that do not belong to any known class or are not well represented by existing models. Ergo, in autonomous navigation novelty detection determines whether an input camera frame contains certain entities of high interest which do not correspond to a known category. One of the key requirements for the future space exploration missions is the reduction of the information to be transferred back to Earth. Thus, novelty detection techniques have been developed to select the subset of acquired images with significant measurements that justify utilisation of the limited bandwidth from the available information link. Such methods are based on the identification of salient regions, which are then evaluated against a set of trained classifiers. We explore a novelty detection approach, based on the reasoning properties of Neural Networks, which follow the same guidelines while also being trainable in an end-to-end manner. This characteristic allows for the intertwined optimisation of the individual components leading to a closer estimation of a global solution. Our experiments reveal that the proposed novelty detection system achieves better performance, as compared to hand-crafted techniques, when the learning and testing examples refer to similar environments.

This study addresses the problem of deploying a group of mobile robots over a non-convex region with obstacles. Assuming that the robots are equipped with omnidirectional range sensors of common radius, disjoint subsets of the sensed area are assigned to the robots. These proximity-based subsets are calculated using the visibility notion, where the cell of each robot is treated as an opaque obstacle for the other robots. Based on that, optimal spatially distributed coordination algorithms are derived for the area coverage problem and for the homing problem, where the swarm needs to move to specific locations. Experimental studies demonstrate the results.

Software validation aims to ensure that a particular software product fulfils its intended purpose, and needs to be performed against both software requirement as well as its implementation (i.e. product). However, for diagnostic software (i.e. a diagnoser) performing online diagnosis against certain fault models and reports diagnosis decision, the underlying fault models are usually not explicitly specified, neither by formal language nor by descriptive language. The lack of formal representation of fault models leaves the intended purpose of the diagnostic software vague, making its validation difficult. To address this issue, the authors propose various model-based techniques that can generate concrete examples of the diagnoser's key properties. Such examples are represented in an intuitive and possibly visualised way, facilitating the designers/users to approve or disapprove the conformance of the diagnoser to the intended purpose. The proposed techniques work for validation of both the requirement and implementation that can be modelled as finite state machine, and are illustrated through applications on vehicle on-board diagnostic requirement.

The authors analyse a Bayesian framework for posing and solving inverse reinforcement learning (IRL) problems that arise in decision-making and optimisation settings. The authors propose a non-parametric Bayesian model using Gaussian process (GP) and preference graphs, which offer an effective and computationally efficient method for ill-posed IRL problems in large or infinite state space. This approach only requires a finite number of demonstrations that is much less than the amount required for approximating the feature expectation or value functions in previous IRL methods. The proposed learning framework is expressive as it relies on a Bayesian structure in which assumptions are explicit and changeable. It is also robust in that it formalises on convex optimisation, which retains the promise of computationally manageable implementations for practical problems. To deal with more realistic IRL problems where the dynamics is also unknown, the GP model can be easily combined with the method to learn the dynamics at the same time. Experimental results prove the superiority of the authors method to current prevailing IRL algorithms with fewer numbers of demonstrations in both discrete and continuous state space.

This article focuses on H ∞ containment control and the communication network topologies that are driven by a semi-Markov chain. Moreover, the communication channels between agents exist time-varying delays and noise. Firstly, the authors extend the Markov switching topologies to semi-Markov switching topologies. Because the transition rate of the semi-Markov switching topology is time-varying and depends on the sojourn time, the analysis of containment control under semi-Markov switching topology becomes more challenging. Secondly, a control protocol with time-varying delays is adopted. The error function is derived by the property of graph theory, convex hull and communication noise. Hence, the problem of H ∞ containment control is transformed into the stability problem of the semi-Markov jump system. To avoid the zero initial condition in the traditional H ∞ control approach, a novel performance function is constructed with the initial condition considered. Finally, simulation experiments are provided to verify the effectiveness of the proposed algorithm.

Some systems, in spite of having multiple outputs, have only one control input, which makes their control a challenge. Two novel controllers are proposed that utilise an adaptive finite-time sliding mode control (AFSMC) scheme for a class of single-input multiple-output (SIMO) nonlinear systems in the presence of unknown mismatched uncertainties. To alleviate the inherent chattering phenomenon of sliding mode control, new forms of the two designed controllers are suggested by using new sliding surfaces. Not only can the proposed AFSMC scheme stabilise the system in a finite time, but also it can provide estimated data of the uncertainty upper bound in the controller. Lyapunov stability theory is used to obtain finite-time stability analysis of the closed-loop system. Finally, simulation results are carried out in Simulink/MATLAB for a four-dimensional autonomous hyper-chaotic system with mismatched uncertainties as an example of SIMO uncertain nonlinear systems to reveal the effectiveness of the proposed controllers.