This article investigates the prescribed-time formation control in the task space of multirobot systems (MRSs), which is subject to the uncertain nonlinear dynamics and the position requirements. The strategy constructs a cascade system consisting of control and reference layers by connections of coupled prescribed-time control units, which separately guarantee the convergence of coordination errors, accuracy of states, and synchronization between layers. Meanwhile, the transformation from task space to joint space is built based on the pseudo-inverse of the Jacobi matrix, which avoids the singular value problem brought by computing the inverse kinematics. The adaptive control method utilizes parameter estimation to eliminate the inaccuracy problem brought by the pseudo-inverse of Jacobi matrix transformation. Then, this article provides solutions to the formation control and the formation along the trajectory control. Correspondingly, the Lyapunov analysis process proves the system’s stability and the parameter estimation’s boundedness, which confirms the sufficient conditions for realizing the prescribed-time formation control of the MRSs. Finally, this article presents examples of time-varying formation and along-trajectories formation, thereby demonstrating the effect of the controller.

Intelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. However, how to improve the distribution efficiency with low operating costs, and mitigate environmental pollution with high service quality is still a significant challenge in the practical industry applications. To address the above challenge, in this paper, we take into account both the economic cost and environmental cost, and propose a joint distribution path planning model based on neural architecture search (NAS) for electric vehicles with double-decked drones. More specifically, in our design, the factors such as energy consumption and carbon emissions of vehicles and drones during different distribution stages are considered. Then, a mixed integer linear programming model is established under the constraints of customer time window, vehicle capacity and vehicle battery capacity. Based on this model, a hybrid genetic algorithm is proposed to solve the optimization problem, where the carbon emission cost is estimated by the convolution neural network model, which is optimized by the neural architecture search technique. We conduct extensive experiments to validate the effectiveness of the proposed method. The experimental results show that, compared with CPLEX, the proposed method excelled in both solution quality and speed, which verify the effectives of our hybrid algorithm in dealing with the 2E-VRPD problem, where delivery routes can be well optimized, the efficiency of vehicle-machine collaborative delivery can be improved, and delivery costs are reduced.

As a very popular positioning system, WLAN positioning attracts widely researches and investigations throughout the world. It implements the fingerprint technique to realize indoor navigation. The fingerprinting technique which employs the KNN algorithm has to make use of RSS (Received Signal Strength) from the Access Points (APs) without any classification. However, not all of the APs provide the contributions but interference, which will not only burden the positioning system but also results in poor positioning accuracy. To increase the positioning accuracy and decrease the computation cost of Wireless Local Area Network (WLAN), a novel algorithm is proposed by implementing the KNN algorithm and Information Gain Theory to bridge the gap between the Access Point selection and positioning accuracy. The experiment results indicates that, the positioning accuracy is improved by 4.76% within 2m, and meanwhile the time for positioning is decreased by 23.81%, which means the proposed algorithm successfully achieves higher positioning accuracy with less computational cost. Besides, it is also proved in this paper that contrary to the traditional concept, more APs do not always mean higher positioning accuracy; on the opposite, in our experimental environment, a relatively small scale of APs can achieve higher positioning accuracy than that of large scale of APs.