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Aiming at the problems of low positioning accuracy, poor map readability and weak robustness when the mobile robot implements the SLAM technology due to the existence of dynamic objects in the mobile robot working environment, an SLAM technology algorithm for mobile robot dynamic environment positioning based on semantic information is proposed. Firstly, the front end of the ORB-SLAM2 framework will be used, combined with the YOLO v4 target detection algorithm, to extract the ORB features of the input image. Meanwhile, the YOLO v4 target detection algorithm is used to obtain the dynamic and static areas of the object containing semantic information in the scene image, to obtain the preliminary semantic dynamic and static areas, and to perform semantic segmentation in the image; Then, the dynamic object region is selected by using the image polar equation, and the ORB feature points distributed on the dynamic object are eliminated. Finally, the processed feature points and adjacent key frames are used for inter-frame matching to estimate the camera pose to build a static environment map. The experiment used the open TUM dataset to compare the proposed algorithm with the traditional ORB-SLAM2 test results show that the proposed algorithm improves the pose estimation accuracy by 75% compared with the ORB-SLAM2 in the dynamic environment, and the map construction effect is significantly enhanced. The experimental results show that this algorithm can eliminate the influence of dynamic objects on SLAM technology, improve the positioning accuracy of the system, and expand the application field of the system.

Evolutionary algorithms (EAs) can be used to find solutions in dynamic environments. In such cases, after a change in the environment, EAs can either be restarted or they can take advantage of previous knowledge to resume the evolutionary process. The second option tends to be faster and demands less computational effort. The preservation or growth of population diversity is one of the strategies used to advance the evolutionary process after modifications to the environment. We propose a new adaptive method to control population diversity based on a model-reference. The EA evolves the population whereas a control strategy, independently, handles the population diversity. Thus, the adaptive EA evolves a population that follows a diversity-reference model. The proposed model, called the Diversity-Reference Adaptive Control Evolutionary Algorithm (DRAC), aims to maintain or increase the population diversity, thus avoiding premature convergence, and assuring exploration of the solution space during the whole evolutionary process. We also propose a diversity models based on the dynamics of heterozygosity of the population, as models to be tracked by the diversity control. The performance of DRAC showed promising results when compared with the standard genetic algorithm and six other adaptive evolutionary algorithms in 14 different experiments with three different types of environments.

Many real-world problems involve measures of objectives that may be dynamically optimized. The application of evolutionary algorithms, such as genetic algorithms, in time dependent optimization is currently receiving growing interest as potential applications are numerous ranging from mobile robotics to real time process command. Moreover, constant evaluation functions skew results relative to natural evolution so that it has become a promising gap to combine effectiveness and diversity in a genetic algorithm. This paper features both theoretical and empirical analysis of the behavior of genetic algorithms in such an environment. We present a comparison between the effectivenss of traditional genetic algorithm and the dual genetic algorithm which has revealed to be a particularly adaptive tool for optimizing a lot of diversified classes of functions. This comparison has been performed on a model of dynamical environments which characteristics are analyzed in order to establish the basis of a testbed for further experiments. We also discuss fundamental properties that explain the effectiveness of the dual paradigm to manage dynamical environments.

Audretsch & Feldman (2004) argue that an agglomeration is a collection of localized firms with a common focus. As firms thrive, resources are attracted to the region. They state that, if entrepreneurship serves as a mechanism for knowledge spillovers, measures of entrepreneurial activity should be linked positively to regional growth performance. In Schumpeterian economics the engine of economic development is entrepreneurial innovation. Creative destruction makes way for new innovations and growth. In this study, we simultaneously examine the regional entrepreneurial activity and regional growth activity in Finland. A further aim of the study was to find out if entrepreneurial activity and growth activity also play a deagglomerating role. We find, first, that the indicators used are very well suited to measure the dynamic environment, especially in manufacturing, since the regions with the most dynamic environment were areas with high small-business activity. Furthermore, the study indicates that growth activity should be taken into account when examining regional development by means of the concept of the dynamic environment. Secondly, we find that entrepreneurial activity and growth activity decreases regional specialization, i.e., the regions with the highest regional specialization are characterized by the lowest levels of entrepreneurial activity and growth activity. Our study confirms with Finnish data the findings of Dumais et al. (2002) that new plant births play a deagglomerating role. The results of the study indicate also that growth activity tends to act to reduce regional specialization. As a whole, the results suggest that the regional specialization is the result of a dynamic process in which the combination of plant births and growth act together.

Unsteady locomotion and the dynamic environment are two problems that block humanoid robots to apply visual Simultaneous Localization and Mapping (SLAM) approaches. Humans are often considered as moving obstacles and targets in humanoid robots working space. Thus, in this paper, we propose a robust dense RGB-D SLAM approach for the humanoid robots working in the dynamic human environments. To deal with the dynamic human objects, a deep learning-based human detector is combined in the proposed method. After the removal of the dynamic object, we fast reconstruct the static environments through a dense RGB-D point clouds fusion framework. In addition to the humanoid robot falling problem, which usually results in visual sensing discontinuities, we propose a novel point clouds registration-based method to relocate the robot pose. Therefore, our robot can continue the self localization and mapping after the falling. Experimental results on both the public benchmarks and the real humanoid robot SLAM experiments indicated that the proposed approach outperformed state-of-the-art SLAM solutions in dynamic human environments.

There is an extensive literature regarding multi-robot simultaneous localization and mapping (MRSLAM). In most part of the research, the environment is assumed to be static, while the dynamic parts of the environment degrade the estimation quality of SLAM algorithms and lead to inherently fragile systems. To enhance the performance and robustness of the SLAM in dynamic environments (SLAMIDE), a novel cooperative approach named parallel-map (p-map) SLAM is introduced in this paper. The objective of the proposed method is to deal with the dynamics of the environment, by detecting dynamic parts and preventing the inclusion of them in SLAM estimations. In this approach, each robot builds a limited map in its own vicinity, while the global map is built through a hybrid centralized MRSLAM. The restricted size of the local maps, bounds computational complexity and resources needed to handle a large scale dynamic environment. Using a probabilistic index, the proposed method differentiates between stationary and moving landmarks, based on their relative positions with other parts of the environment. Stationary landmarks are then used to refine a consistent map. The proposed method is evaluated with different levels of dynamism and for each level, the performance is measured in terms of accuracy, robustness, and hardware resources needed to be implemented. The method is also evaluated with a publicly available real-world data-set. Experimental validation along with simulations indicate that the proposed method is able to perform consistent SLAM in a dynamic environment, suggesting its feasibility for MRSLAM applications.

This paper presents a new algorithm for the mobile robot path planning problem. The algorithm presented here applies an initialization heuristics and new operators to solve this problem and discusses how the operators are tuned to perform better. The algorithm adapts itself by tuning its operator probabilities according to the characteristics of the path generated so far. The fitness of the path is not only based on the length of the path but also determined on the basis of the smoothness of the path. The algorithm is tested for different environments with arbitrarily-shaped obstacles and dynamic environments. When dynamic obstacles are identified the algorithm finds the new path from the point of intervention and the original path is retained till that position. The algorithm is tested for five different environments.

This paper presents a mobile object detection algorithm which performs with two consecutive stereo images. Like most motion detection methods, the proposed one is based on dense stereo matching and optical flow (OF) estimation. Noting that the main computational cost of existing methods is related to the estimation of OF, we propose to use a fast algorithm based on Lucas–Kanade paradigm. We then derive a comprehensive uncertainty model by taking into account all the estimation errors occurring during the process. In contrast with most previous works, we rigorously expand the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the challenging KITTI dataset which demonstrates the effectiveness of the proposed approach.

The integration of Unmanned Aerial Vehicles (UAVs) is being proposed in a spectrum of applications varying from military to civil. In these applications, UAVs are required to safely navigate in real-time in dynamic and uncertain environments. Uncertainty can be present in both the UAV itself and the environment. Through a literature study, this paper first identifies, quantifies and models different uncertainty sources using bounding shapes. Then, the UAV model, path planner parameters and four scenarios of different complexity are defined. To investigate the effect of uncertainty on path planning performance, uncertainty in obstacle position and orientation and UAV position is varied between 2% and 20% for each uncertainty source first separately and then concurrently. Results show a deterioration in path planning performance with the inclusion of both uncertainty types for all scenarios for both A* and the Rapidly-Exploring Random Tree (RRT) algorithms, especially for RRT. Faster and shorter paths with similar same success rates (>95%) result for the RRT algorithm with respect to the A* algorithm only for simple scenarios. The A* algorithm performs better than the RRT algorithm in complex scenarios.

Advances in processor, memory and radio technology have enabled small and cheap nodes capable of sensing, communication and computation for constructing wireless sensor networks. They provide a pervasive intelligence solution for robot navigation in a dynamic and large scale environment. In this paper, a self-organising diffusion protocol of wireless sensors is proposed based on a competitive learning strategy, aiming to dynamically clustering similar regions in an environment based on perception of wireless sensors. The clusters of sensor nodes form a dynamic topological map of the environment and path planning to a destination can then be accomplished by propagation of a query to the network. An optimal safe path is achieved or updated efficiently by introducing a communication topology on the top of the self-organising sensor layer. This diffusion protocol is verified by a simulation of robot navigation in a toxic environment.

Nowadays, rigid-link manipulators have been extensively used in various industrial applications, such as automotive industry and manufacturing operations. Nonetheless, despite of their precise and well-established position control, rigidlink manipulators sufier from their lack of exibility, especially when operated in cluttered, unknown, dynamic environments, as well as their inherent rigidity which limits their applications in a shared human-robot workspace. In this paper, we report our current progress on mobile continuum manipulators application in dynamic environments. The results show that a continuum arm, mounted on a mobile platform and equipped with a reactive motion planner, is a promising candidate to be used in dynamic industrial environments.

Recent developments information technology shifts the computing paradigm towards more dynamic, which also raises some new challenges. Based on our previous research work MobiPass, this paper proposes a technique which can help transacting entities select the most suitable transacting entities by establishing trusted interaction in dynamic environments in a real time manner by using Multi Criteria Decision Support System(MCDSS) as well as MobiPass framework,

Over the past few decades, the method of reliability assessment based on performance degradation data has attracted many researchers because of its effectiveness and practical significance for complex systems of small-sample, highly reliable and longevity. A degradation model was presented in this paper for system with linear failure threshold under dynamic environment based on Wiener process, and the parameter of the influence function determined by dynamic environment in the degradation model was estimated by Bayesian method. Then a method of reliability assessment was presented for system with linear failure threshold in terms of the distribution of first passage time. The models and method are appropriate in applications and engineering which is meaningful and will play an important role in the remaining useful life estimation and condition-based maintenance for complex systems.