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The purpose of this paper is to present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties.

The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem.

The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed.

The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.

Mooring chains used to stabilise offshore floating platforms are often subjected to harsh environmental conditions on a daily basis, i.e. high tidal waves, storms, etc. Therefore, the integrity assessment of chain links is vital, and regular inspection is mandatory for offshore structures. The development of chain climbing robots is still in its infancy due to the complicated climbing structure presented by mooring chains. The purpose of this paper is to establish an automated climbing technique for mooring chain inspection.

This paper presents a Cartesian legged tracked-wheel crawler robot developed for mooring chain inspection. The proposed robot addresses the misalignment condition of the mooring chains which is commonly evident in in situ conditions.

The mooring chain link misalignment is investigated mathematically and used as a design parameter for the proposed robot. The robot is validated with laboratory-based climbing experiments.

Chain breaking can lead to vessel drift and serious damage such as riser rupture, production shutdown and hydrocarbon release. Currently, structural health monitoring of chain links is conducted using either remotely operated vehicles which come at a high cost or by manual means which increase the danger to human operators. The robot can be used as a platform to convey equipment, i.e. tools for non-destructive testing/evaluation applications.

This study has upgraded a previously designed magnetic adhesion tracked-wheel mooring chain climbing robot to address the misalignment issues of operational mooring chains. As a result of this study, the idea of an orthogonally placed Cartesian legged-magnetic adhesion tracked wheel robotic platform which can eliminate concerns related to the misaligned mooring chain climbing has been established.

The successful application of the group of unmanned aerial vehicles (UAVs) in the tasks of monitoring large areas is becoming a promising direction in modern robotics. This paper aims to study the tasks related to the control of the UAV group while performing a common mission.

This paper discusses the main tasks solved in the process of developing an autonomous UAV group. During the survey, five key tasks of group robotics were investigated, namely, UAV group control, path planning, reconfiguration, task assignment and conflict resolution. Effective methods for solving each problem are presented, and an analysis and comparison of these methods are carried out. Several specifics of various types of UAVs are also described.

The analysis of a number of modern and effective methods showed that decentralized methods have clear advantages over centralized ones, since decentralized methods effectively perform the assigned mission regardless of on the amount of resources used. As for the method of planning the group movement of UAVs, it is worth choosing methods that combine the algorithms of global and local planning. This combination eliminates the possibility of collisions not only with static and dynamic obstacles, but also with other agents of the group.

The results of scientific research progress in the tasks of UAV group control have been summed up.