The Efficacy and Safety of Air Tamponade in the Repair of Rhegmatogenous Retinal Detachment: A Systematic Review and Meta-Analysis.

Background: Rhegmatogenous retinal detachment (RRD) repair by pars plana vitrectomy (PPV) combined air tamponade has many advantages compared with PPV combined gas tamponade. However, there are controversial outcomes in RRD cases involving the lower quadrants.

Objective: This study aimed to evaluate the efficacy and safety of PPV combined air tamponade in patients with RRD compared with PPV combined gas tamponade and whether it could be a safe alternative to PPV combined gas tamponade.

MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs.

The path planning and tracking problem of the multi-robot system (MRS) has always been a research hotspot and applied in various fields. In this article, a novel multi-robot path planning and tracking model (MPPTM) is proposed, which can carry out online path planning and tracking problem for multiple mobile robots. It considers many issues during this process, such as collision avoidance, and robot failure.

Enhanced Multifactorial Evolutionary Algorithm With Meme Helper-Tasks.

Evolutionary multitasking (EMT) is an emerging research direction in the field of evolutionary computation. EMT solves multiple optimization tasks simultaneously using evolutionary algorithms with the aim to improve the solution for each task via intertask knowledge transfer. The effectiveness of intertask knowledge transfer is the key to the success of EMT.

A Bio-Inspired Approach to Task Assignment of Swarm Robots in 3-D Dynamic Environments.

Intending to mimic the operating mechanism of biological neural systems, a self organizing map-based approach to task assignment of a swarm of robots in 3-D dynamic environments is proposed in this paper. This approach integrates the advantages and characteristics of biological neural systems. It is capable of dynamically planning the paths of a swarm of robots in 3-D environments under uncertain situations, such as when some robots are presented in or broken down or when more than one robot is needed for some special task locations.

A neural network approach to dynamic task assignment of multirobots.

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given.