A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.

A dual-robot system is a robotic device composed of two robot arms. In order to eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators. Then, such two subschemes are reformulated as two general quadratic programs (QPs), which can be formulated as one unified QP. A recurrent neural network (RNN) is thus presented to solve effectively such an unified QP problem. At last, computer-simulation results based on a dual three-link planar manipulator further validate the feasibility and the efficacy of the proposed optimization scheme for coordinated path tracking using the recurrent neural network.

Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional to the recognition accuracy. To simultaneously enhance the speed and accuracy for recognizing dynamic objects in different environments, a novel SLAM system without prior semantics called DGFlow-SLAM is proposed in this paper. A novel grid segmentation method is used in the system to segment the scene flow, and then an adaptive threshold method is used to roughly detect the dynamic objects. Based on this, a deep mean clustering segmentation method is applied to find potential dynamic targets. Finally, the results of grid segmentation and depth mean clustering segmentation are jointly used to find moving objects accurately, and all the feature points of the moving objects are removed on the premise of retaining the static part of the moving object. The experimental results show that on the dynamic sequence dataset of TUM RGB-D, compared with the DynaSLAM system with the highest accuracy for detecting moderate and violent motion and the DS-SLAM with the highest accuracy for detecting slight motion, DGflow-SLAM obtains similar accuracy results and improves the accuracy by 7.5%. In addition, DGflow-SLAM is 10 times and 1.27 times faster than DynaSLAM and DS-SLAM, respectively.

In dynamic SLAM systems, the traditional multi-view geometry method faces two main challenges. The first issue to address is how to select an appropriate threshold in order to enhance the accuracy of determining the dynamic or static (D-S) property of matched points. Secondly, for the unmatched points between adjacent frames, it is difficult to determine their D-S property. In addressing the aforementioned issues, this paper first obtains the dynamic mask by using semantic segmentation model and removes the dynamic feature points located within these masks. For the remained matched feature points, the paper proposes an adaptive threshold-setting method for epipolar constraint by analyzing the distribution characteristics of the epipolar line distances. Furthermore, to handle unmatched points, the paper proposes a novel method called the KCP method that combines K-means clustering and epipolar constraint. This method effectively calculates the dynamic probability and eliminates feature points with high dynamic probabilities. Finally, this paper proposes a system named DSK-SLAM based on the orb-slam2 system, which integrates the aforementioned methods into a new dynamic removal module. Experiments demonstrate that compared with ORB-SLAM2, DS-SLAM, DynaSLAM and MOR-SLAM, DSK-SLAM acquire better results on the public dataset TUM RGB-D, which achieves an average accuracy improvement of 95.71%, 41.35%, 8.37% and 1.75% in highly dynamic sequences, and achieves an accuracy improvements of 24.69%, 6.15%, 7.58% and 15.28% in a low dynamic sequence, respectively.

This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.