Distributed and Time-Delayed k-Winner-Take-All Network for Competitive Coordination of Multiple Robots

Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control

Robustness is a significant research direction in manipulator control owing to their complicated and uncertain external environment, abrasion, and other factors. The ability to implement multitasking is also necessary for manipulator control because of the physical limitations and complex requirements. However, the existing research has mainly focused on the control of a single task and robustness analysis of single-task control. Although some research on multi-task control has been conducted recently, its robustness has not yet been studied. Because of the excellent performance of the integrated-enhanced zeroing neural network in terms of robustness for time-varying problem solving, it was employed in this study to solve robust multi-task control. First, the multi-task control was formulated as a two-layered time-varying problem, including nonlinear and hybrid linear equations describing the tracking task and additional tasks, respectively. Second, an integrated-enhanced zeroing neural network was employed for the multilayered time-varying problem solving and a robust multi-task control algorithm was obtained, which can suppress different types of noises. Theoretical analyses demonstrated its effectiveness in multitasking and superior robustness compared with conventional algorithms. Finally, simulation results verified the theoretical results.

UAV path planning method for data collection of fixed-point equipment in complex forest environment

In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach.