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While the need to set targets has been widely discussed in the TQM, benchmarking and re‐engineering literature, guidance aimed at helping management report and interpret performance against targets has been fragmented. Very little literature has appeared that brings together our current state of knowledge on performance targets and suitable methodologies. This paper attempts to compensate for this shortfall by first explaining the three major forms that targets assume and then presenting four methods that are useful when assessing performance in each case. The three forms are first, the target is a single lower limit; second, the target is a single upper limit; and third, the target is a zone between an upper and a lower limit. The four methods to calculate the level of improvement needed to reach target are the counting, distance, histogram and capability index methods. While individually each method cannot be claimed to be new, the contribution of this paper lies in their integration, which has not been presented in such a comprehensive way before.

This study aims to investigate the rule‐based decentralised control framework for a swarm of UAVs carrying out a cooperative ground target engagement mission scenario.

This study is to investigate the rule‐based decentralised control framework for missions which require high‐level cooperation between team members. The design of the authors’ control strategy is based on agent‐level interactions. Different to a centralized task assignment algorithm, the cooperation of the agents is entirely implicit. The behaviour of the UAVs is governed by rule sets which ultimately lead to cooperation at a system level. The information theoretic measures are adopted to estimate the value of possible future actions. The prediction model is further considered to enhance the team performance in the scenario where there are tight coupled task constraints.

The simulation study evaluates the performance of the decentralised controller and compares it with a centralised controller quantitatively. The results show that the proposed approach leads to a highly cooperative performance of the group without the need for a centralised control authority. The performance of the decentralised control depends on the complexity of the coupled task constraints. It can be improved by using a prediction model to provide information such as the intentions of the neighbours that is not available locally.

The achievable performance of the decentralised control was considered to be low due to the absence of communication and little global coordinating information. This study demonstrated that the decentralised control can achieve highly cooperative performance. The achievable performance is related to the complexity of the coupled constraints and the accuracy of the prediction model.

The purpose of this paper is to study the optimization of a pulsed-excitation gradiometric inductive sensing system.

The authors applied numerical finite-element modeling for the simulation of the step responses of different target materials to identify the particular contribution of the magnetic permeability and the electric conductivity. Four materials of technical importance (aluminum, copper, constructional steel and stainless steel) and four fictive test materials were modeled for the comparison of different materials possessing a wide range of combinations of material parameters. A microcontroller-based measurement setup was implemented for the qualitative validation of the simulation results. A simple signal processing chain was also applied for the time-domain conversion of the direct step response signals to increase the time scale of the signals to be processed by common mixed-signal components.

The step response signals contain relevant information of the target material quality and the sensor-to-target distance. The target materials can be distinguished and the sensor-target distance can be determined by the evaluation of the step response signals with an appropriate algorithm based on the measurement of the time and voltage of an extreme of the time dependent measurement signals. Both direct and time-domain converted signals can be used for material independent proximity sensing.

In order to design an inductive proximity switch, an evaluation method of the response signals has been proposed by using an analog RLC circuit. With the presented method, a target material invariant inductive proximity switch can be realized.

Multi-robots simultaneously coverage and tracking (SCAT) is the problem of simultaneously covering area and tracking targets, which is essential for many applications, such as delivery service, environment monitor, traffic surveillance, crime monitor, anti-terrorist mission and so on. The purpose of this paper is to improve the performance of detected target quantity, coverage rate and less deadweight loss by designing a self-organized method for multi-robots SCAT.

A self-organized reciprocal control method is proposed, coupling task assignment, tracking and covering, equipped with collision-avoiding ability naturally. First, SCAT problem is directly modeled as optimal reciprocal coverage velocity (ORCV) in velocity space. Second, the preferred velocity is generated by calculating the best velocity to the center of some robot detected targets. ORCV is given by adjusting the velocity relative to neighbor robots’ toward in optimal coverage velocity (OCV); it is proven that OCV is collision-free assembly. Third, some corresponding algorithms are designed for finding optimal velocity under two situations, such as no detected targets and empty ORCV.

The simulation results of two cases for security robots show that the proposed method has detected more targets with less deadweight loss and decision time and no collisions anytime.

In this paper, a self-organized reciprocal control method is proposed for multi-robots SCAT problem, which is modeled in velocity space directly, different to the traditional method modeling in configuration space. What is more, this method considers the reciprocal of robots that contributes to the better accomplishment of SCAT cooperatively.

Because of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.

The authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

The experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.

A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

Indoor robotic tasks frequently specify objects. For these applications, this paper aims to propose an object-based attention method using task-relevant feature for target selection. The task-relevant feature(s) are deduced from the learned object representation in semantic memory (SM), and low dimensional bias feature templates are obtained using Gaussian mixture model (GMM) to get an efficient attention process. This method can be used to select target in a scene which forms a task-specific representation of the environment and improves the scene understanding by driving the robot to a position in which the objects of interest can be detected with a smaller error probability.

Task definition and object representation in SM are proposed, and bias feature templates are obtained using GMM deduction for features from high dimension to low dimension. Mean shift method is used to segment the visual scene into discrete proto-objects. Given a task-specific object, the top-down bias attention uses obtained statistical knowledge of the visual features of the desired target to impact proto-objects and generate the saliency map by combining with the bottom-up saliency-based attention so as to maximize target detection speed.

Experimental results show that the proposed GMM-based attention model provides an effective and efficient method for task-specific target selection under different conditions. The promising results show that the method may provide good approximation to how humans combine target cues to optimize target selection.

The present method has been successfully applied in plenty of natural scenes of indoor robotic tasks. The proposed method has a wide range of applications and is using for an intelligent homecare robot cognitive control project. Due to the computational cost, the current implementation of this method has some limitations in real-time application.

The novel attention model which uses GMM to get the bias feature templates is proposed for attention competition. It provides a solution for object-based attention, and it is effective and efficient to improve search speed due to the autonomous deduction of features. The proposed model is adaptive without requiring predefined distinct types of features for task-specific objects.

In the present work, a new blast-/ballistic-impact mitigation concept is introduced and its efficacy analyzed using advanced computational methods and tools. The concept involves the use of a zeolite protective layer separated by air from the structure being protected and in contact with a water layer in front. The paper aims to discuss these issues.

To properly capture the attendant nano-fluidics phenomena, all the calculations carried out in the present work involved the use of all-atom molecular-level equilibrium and non-equilibrium molecular-dynamics simulations.

Under high-rate loading, water molecules (treated as a nano-fluidic material) are forced to infiltrate zeolite nanopores wherein, due to complex interactions between the hydrophobic nanopore walls and the hydrogen bonds of the water molecules, water undergoes an ordering-type phase transition and acquires high density, while a significant portion of the kinetic energy of the water molecules is converted to potential energy. Concomitantly, a considerable portion of this kinetic energy is dissipated in the form of heat. As a result of these energy conversion/dissipation processes, the (conserved) linear momentum is transferred to the target structure over a longer time period, while the peak loading experienced by the structure is substantially reduced.

To the authors’ knowledge, the present work constitutes the first reported attempt to utilize pure SiO₂ hydrophobic zeolites in blast-/ballistic-impact protection applications.

The purpose of this paper is to present an implementation of a soft‐computing (SC) based navigation approach on a bi‐steerable mobile robot, Robucar. This approach must provide Robucar with capability to acquire the obstacle avoidance, target localization, decision‐making and action behaviors after learning and adaptation. This approach uses three neural networks (NN) and fuzzy logic (FL) controller to achieve the desired task. The NNs corresponding to the obstacle avoidance and target localization are trained using the back‐propagation algorithm and the last one is based on the reinforcement learning paradigm while the FL controller uses the Mamdani search and match algorithm. Simulation and experimental results are presented, showing the effectiveness of the overall navigation control system.

In this paper, an interesting navigation approach is applied to a car‐like robot, Robucar, with addition of an action behavior to deal with the generation of smooth motions. Indeed, this approach is based on four basic behaviors; three of them are fused under a neural paradigm using Gradient Back‐Propagation (GBP) and reinforcement learning (RL) algorithms and the last behavior uses a FL controller. It uses a set of suggested rules to describe the control policy to achieve the action behavior.

In the implemented SC‐based navigation, the intelligent behaviors necessary to the navigation are acquired by learning using GBP algorithm and adaptation using FL. The proposed approach provides Robucar with more autonomy, intelligence and real‐time processing capabilities. Indeed, the proposed NNs and FLC are able to remedy problems of analytical approaches, missing or incorrect environment knowledge and uncertainties which can lead to undesirable effects as the rough velocity changes. The simulation and experimental results display the ability of the proposed SC‐based navigation approach to provide Robucar with capability to intelligently navigate in a priori unknown environment, illustrating the robustness and adaptation capabilities of the approach.

This work can be extended to consider mobile obstacles with a velocity higher than the velocity of the robot.

This paper presents a learning approach to navigating a bi‐steerable mobile robot in an unknown environment using GBP and RL paradigms.

This paper aims to introduce a new target tracking method based on risk theory in a 2-D discrete environment. After that, the related sensor scheduling method is proposed. This can make up the blank of target tracking and sensor management in the 2-D discrete environment.

The definition of risk is proposed based on risk decision theory firstly. Then the target tracking model in a two-dimensional discrete environment is built. The motion state updating and estimation method of target’s motion state based on Bayes theory is given. Thirdly, the method of computing sensor emission interception risk is provided. Afterwards, the optimization rule of obtaining the minimum risk is followed to model the sensor scheduling objective function. The lion algorithm is adjusted and improved combined with Chaos theory to generate the optimal sensor management projects.

The risk-based sensor target tracking method and sensor management method are both effective in a 2-D discrete environment.

To the best of the authors’ knowledge, this paper is the first to study the target tracking method and sensor scheduling method in a 2-D environment. Furthermore, the lion algorithm is improved combined with Chaos theory to show a better optimization performance.

The purpose of this paper is to report upon research into developing a biologically inspired target‐tracking system (TTS) capable of acquiring quality images of a known target type for a robotic inspection application.

The approach used in the design of the TTS hearkens back to the work on adaptive learning by Oliver Selfridge and Chris J.C.H. Watkins and the work on the classification of objects by Zdzislaw Pawlak during the 1980s in an approximation space‐based form of feedback during learning. Also, during the 1980s, it was Ewa Orlowska who called attention to the importance of approximation spaces as a formal counterpart of perception. This insight by Orlowska has been important in working toward a new form of adaptive learning useful in controlling the behaviour of machines to accomplish system goals. The adaptive learning algorithms presented in this paper are strictly temporal difference methods, including Q‐learning, sarsa, and the actor‐critic method. Learning itself is considered episodic. During each episode, the equivalent of a Tinbergen‐like ethogram is constructed. Such an ethogram provides a basis for the construction of an approximation space at the end of each episode. The combination of episodic ethograms and approximation spaces provides an extremely effective means of feedback useful in guiding learning during the lifetime of a robotic system such as the TTS reported in this paper.

It was discovered that even though the adaptive learning methods were computationally more expensive than the classical algorithm implementations, they proved to be more effective in a number of cases, especially in noisy environments.

The novelty associated with this work is the introduction of an approach to adaptive adaptive learning carried out within the framework of ethology‐based approximation spaces to provide performance feedback during the learning process.