IET Cybersystems and Robotics最新文献

To ensure that the robot can follow the planned trajectory, smooth switching between swinging legs and a smooth transition of motion process is realised. The previous motion planning work is analysed, and a method for improving the optimisation objective function and constraint conditions is proposed to eliminate the sudden change of acceleration and reduce the peak value of acceleration change. This method eliminates the impact phenomenon in the motor drive process and reduces the motor drive energy consumption, thus ensuring the smooth and consistent movement of the robot. The results show that the improved optimisation method has a better motion effect than the previous approach in terms of centre of mass motion speed, trajectory fitting and body posture change, and realises more robust motion of quadruped robots in a senseless state.

Modern agricultural tractors are complex systems, in which multiple physical (and technological) domains interact to reach a wide set of competing goals, including work operational performance and energy efficiency. This complexity translates to the dynamic, multi-domain simulation models implemented to serve as digital twins, for rapid prototyping and effective pre-tuning, prior to bench and on-field testing. Consequently, a suitable simulation framework should have the capability to focus both on the vehicle as a whole and on individual subsystems. For each of the latter, multiple options should be available, with different levels of detail, to properly address the relevant phenomena, depending on the specific focus, for an optimal balance between accuracy and computation time. The methodology proposed here by the authors is based on the lumped parameter approach and integrates the models for the following subsystems in a modular context: internal combustion engine, hydromechanical transmission, vehicle body, and tyre–soil interaction. The model is completed by a load cycle module that generates stimulus time histories to reproduce the work load under real operating conditions. Traction capability is affected by vertical load on the wheels, which is even more relevant if the vehicle is travelling on an uncompacted soil and subject to a variable drawbar pull force as it is when ploughing. The vertical load is, in turn, heavily affected by vehicle dynamics, which can be accurately modelled via a full multibody implementation. The presented lumped parameter model is intended as a powerful simulation tool to evaluate tractor performance, both in terms of fuel consumption and traction dynamics, by considering the cascade phenomena from the wheel–ground interaction to the engine, passing through the dynamics of vehicle bodies and their mass transfer. Its capabilities and numerical results are presented for the simulation of a realistic ploughing operation.

Considering the wheeled mobile robot (WMR) tracking problem with velocity saturation, we developed a data-driven iterative learning double loop control method with constraints. First, the authors designed an outer loop controller to provide virtual velocity for the inner loop according to the position and pose tracking error of the WMR kinematic model. Second, the authors employed dynamic linearisation to transform the dynamic model into an online data-driven model along the iterative domain. Based on the measured input and output data of the dynamic model, the authors identified the parameters of the inner loop controller. The authors considered the velocity saturation constraints; we adjusted the output velocity of the WMR online, providing effective solutions to the problem of velocity saltation and the saturation constraint in the tracking process. Notably, the inner loop controller only uses the output data and input of the dynamic model, which not only enables the reliable control of WMR trajectory tracking, but also avoids the influence of inaccurate model identification processes on the tracking performance. The authors analysed the algorithm's convergence in theory, and the results show that the tracking errors of position, angle and velocity can converge to zero in the iterative domain. Finally, the authors used a simulation to demonstrate the effectiveness of the algorithm.

Robotic fish actuated by smart materials has attracted extensive attention and has been widely used in many applications. In this study, a robotic fish actuated by dielectric elastomer (DE) films is proposed. The tensile behaviours of DE film VHB4905 are studied, and the Ogden constitutive equation is employed to describe the stress-strain behaviour of the DE film. The fabrication processes of the robotic fish, including pre-stretching treatment of the DE films, electrode coating with carbon paste, and waterproof treatment, are illustrated in detail. The dynamic response of the fabricated DE actuators under different excitation voltages is tested based on the experimental setup. Experimental results show that the first-order natural frequencies of the obtained DE actuator in air is 4.05 Hz. Finally, the swimming performances of the proposed robotic fish at different driving levels are demonstrated, and it achieves an average swimming speed of 20.38 mm/s, with a driving voltage of 5kV at 0.8 Hz.

In this study, a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach. Therefore, accurate tracking control can be achieved in the presence of unknown time-varying model parameters and environmental disturbances. The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory. Firstly, the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target. Then, a second-order characteristic model for the heading and surge speed channel is developed. The parameters of the model are updated by a real-time parameter identification algorithm. Based on this model, an integrated adaptive control law is designed which consists of golden-section control, feed-forward control and integral control. Finally, the development processes of the vehicle platform and the control algorithms are described, and the results of simulation and field experiments are presented and discussed.

The problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi-robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi-robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots.

The task of few-shot object detection is to classify and locate objects through a few annotated samples. Although many studies have tried to solve this problem, the results are still not satisfactory. Recent studies have found that the class margin significantly impacts the classification and representation of the targets to be detected. Most methods use the loss function to balance the class margin, but the results show that the loss-based methods only have a tiny improvement on the few-shot object detection problem. In this study, the authors propose a class encoding method based on the transformer to balance the class margin, which can make the model pay more attention to the essential information of the features, thus increasing the recognition ability of the sample. Besides, the authors propose a multi-target decoding method to aggregate RoI vectors generated from multi-target images with multiple support vectors, which can significantly improve the detection ability of the detector for multi-target images. Experiments on Pascal visual object classes (VOC) and Microsoft Common Objects in Context datasets show that our proposed Few-Shot Object Detection via Class Encoding and Multi-Target Decoding significantly improves upon baseline detectors (average accuracy improvement is up to 10.8% on VOC and 2.1% on COCO), achieving competitive performance. In general, we propose a new way to regulate the class margin between support set vectors and a way of feature aggregation for images containing multiple objects and achieve remarkable results. Our method is implemented on mmfewshot, and the code will be available later.

In this study, a robust model predictive controller is designed for the trajectory tracking problem of non-holonomic constrained wheeled mobile robot based on an elliptic invariant set approach. The controller is based on a time-varying error model of robot kinematics and uses linear matrix inequalities to solve the robust tracking problem taking uncertainties into account. The uncertainties are modelled by linear fractional transform form to contain both parameter perturbations and external disturbances. The control strategy consists of a feedforward term that drives the centre of the ellipse to the reference point and a feedback term that converges the uncertain system state error to the equilibrium point. The strategy stabilises the nominal system and ensures that all states of the uncertain system remain within the ellipsoid at each step, thus achieving robust stability of the uncertain system. Finally, the robustness of the algorithm and its resistance to disturbances are verified by simulation and experiment.

In recent years, deep learning has been applied to a variety of scenarios in Industrial Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers. The authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT. A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks (SOPA-GA-CNN) is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks (CNNs) by genetic algorithms (GA) for the intrusion detection issue of IIoT. An efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters, including batch size, learning rate, weight optimiser and weight regularisation, and the architectures, such as the block-based network topology and the parameters of each CNN block. The experimental results on five intrusion detection datasets in IIoT, including secure water treatment, water distribution, Gas Pipeline, Botnet in Internet of Things and Power System Attack Dataset, have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy, precision, recall, F1-score, and the number of parameters of the deep learning models.