scholarly journals A Navigation Probability Map in Pedestrian Dynamic Environment Based on Influencer Recognition Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 19
Author(s):  
Zhi Qiao ◽  
Lijun Zhao ◽  
Xinkai Jiang ◽  
Le Gu ◽  
Ruifeng Li
Keyword(s):  
Robot Navigation ◽  
Dynamic Environment ◽  
Artificial Potential Field ◽  
Data Set ◽  
Train Station ◽  
Recognition Model ◽  
Probability Map ◽  
Rapid Movement ◽  
Collision Problem ◽  
Navigation Method

One of the challenging problems in robot navigation is efficient and safe planning in a highly dynamic environment, where the robot is required to understand pedestrian patterns in the environment, such as train station. The rapid movement of pedestrians makes the robot more difficult to solve the collision problem. In this paper, we propose a navigation probability map to solve the pedestrians’ rapid movement problem based on the influencer recognition model (IRM). The influencer recognition model (IRM) is a data-driven model to infer a distribution over possible causes of pedestrian’s turning. With this model, we can obtain a navigation probability map by analyzing the changes in the effective pedestrian trajectory. Finally, we combined navigation probability map and artificial potential field (APF) method to propose a robot navigation method and verified it on our data-set, which is an unobstructed, overlooked pedestrians’ data-set collected by us.

scholarly journals The complete mitochondrial genome of the intertidal spider (Desis jiaxiangi) provides novel insights into the adaptive evolution of the mitogenome and the evolution of spiders

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fan Li ◽  
Yunyun Lv ◽  
Zhengyong Wen ◽  
Chao Bian ◽  
Xinhui Zhang ◽  
...  
Keyword(s):  
Positive Selection ◽  
Adaptive Evolution ◽  
Physiological Activity ◽  
Complete Mitochondrial Genome ◽  
Dynamic Environment ◽  
Data Set ◽  
History Of ◽  
Complete Mitogenome ◽  
Selection Signal ◽  
Positive Selection Signal

Abstract Background Although almost all extant spider species live in terrestrial environments, a few species live fully submerged in freshwater or seawater. The intertidal spiders (genus Desis) built silk nests within coral crevices can survive submerged in high tides. The diving bell spider, Argyroneta aquatica, resides in a similar dynamic environment but exclusively in freshwater. Given the pivotal role played by mitochondria in supplying most energy for physiological activity via oxidative phosphorylation and the environment, herein we sequenced the complete mitogenome of Desis jiaxiangi to investigate the adaptive evolution of the aquatic spider mitogenomes and the evolution of spiders. Results We assembled a complete mitogenome of the intertidal spider Desis jiaxiangi and performed comparative mitochondrial analyses of data set comprising of Desis jiaxiangi and other 45 previously published spider mitogenome sequences, including that of Argyroneta aquatica. We found a unique transposition of trnL2 and trnN genes in Desis jiaxiangi. Our robust phylogenetic topology clearly deciphered the evolutionary relationships between Desis jiaxiangi and Argyroneta aquatica as well as other spiders. We dated the divergence of Desis jiaxiangi and Argyroneta aquatica to the late Cretaceous at ~ 98 Ma. Our selection analyses detected a positive selection signal in the nd4 gene of the aquatic branch comprising both Desis jiaxiangi and Argyroneta aquatica. Surprisingly, Pirata subpiraticus, Hypochilus thorelli, and Argyroneta aquatica each had a higher Ka/Ks value in the 13 PCGs dataset among 46 taxa with complete mitogenomes, and these three species also showed positive selection signal in the nd6 gene. Conclusions Our finding of the unique transposition of trnL2 and trnN genes indicates that these genes may have experienced rearrangements in the history of intertidal spider evolution. The positive selection signals in the nd4 and nd6 genes might enable a better understanding of the spider metabolic adaptations in relation to different environments. Our construction of a novel mitogenome for the intertidal spider thus sheds light on the evolutionary history of spiders and their mitogenomes.

Model Predictive Control-Based Probabilistic Search Method for Autonomous Ground Robot in a Dynamic Environment

2015 ◽  
Author(s):  
Chang Liu ◽  
Shengbo Eben Li ◽  
J. Karl Hedrick
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Gaussian Function ◽  
Dynamic Environment ◽  
Gaussian Mixture ◽  
Search Method ◽  
Stationary Target ◽  
Probability Map ◽  
Probabilistic Search ◽  
Binary Sensor

Target search using autonomous robots is an important application for both civil and military scenarios. In this paper, a model predictive control (MPC)-based probabilistic search method is presented for a ground robot to localize a stationary target in a dynamic environment. The robot is equipped with a binary sensor for target detection, of which the uncertainties of binary observation are modeled as a Gaussian function. Under the model predictive control framework, the probability map of the target is updated via the recursive Bayesian estimation and the collision avoidance with obstacles is enforced using barrier functions. By approximating the updated probability map using a Gaussian Mixture Model, an analytical form of the objective function in the prediction horizon is derived, which is promising to reduce the computation complexity compared to numerical integration methods. The effectiveness of the proposed method is demonstrated by performing simulations in dynamic scenarios with both static and moving obstacles.

scholarly journals Review of Vision-Based Robot Navigation Method

2015 ◽  
Vol 4 (4) ◽  
pp. 254
Author(s):  
Budi Rahmani ◽  
Agfianto Eko Putra ◽  
Agus Harjoko ◽  
Tri Kuntoro Priyambodo
Keyword(s):  
Robot Navigation ◽  
Map Building ◽  
Environment Map ◽  
Research Theme ◽  
Navigation Method

Vision-based robot navigation is a research theme that continues to be developed up to now by the researchers in the field of robotics. There are innumerable methods or algorithms are developed, and this paper described the reviews of the methods. The methods are distinguished whether  the robot is equipped with the navigation map (map-based), the map is built incrementally as robot observes the environment (map-building), or the robot navigates using no map (mapless). In this paper will described navigation methods of map-based, map-building, and mapless category.

A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

2021 ◽  
Author(s):  
N.P. Demenkov ◽  
Kai Zou
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Urban Environment ◽  
Predictive Control ◽  
Potential Field ◽  
Dynamic Environment ◽  
Field Method ◽  
Artificial Potential Field ◽  
Physical Constraints ◽  
Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

Multi-Robot SLAM in Dynamic Environments with Parallel Maps

2021 ◽  
pp. 2150011
Author(s):  
Sajad Badalkhani ◽  
Ramazan Havangi ◽  
Mohsen Farshad
Keyword(s):  
Large Scale ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Extensive Literature ◽  
Real World Data ◽  
Data Set ◽  
Cooperative Approach ◽  
Localization And Mapping ◽  
Multi Robot

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.

scholarly journals Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Wang ◽  
Jun Feng ◽  
Xinpeng Zhao ◽  
Yeping Bai ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cement Mortar ◽  
Recognition Accuracy ◽  
Image Data ◽  
Concrete Lining ◽  
Learning Method ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Model ◽  
High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

scholarly journals A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Rendong Wang ◽  
Youchun Xu ◽  
Miguel Angel Sotelo ◽  
Yulin Ma ◽  
Thompson Sarkodie-Gyan ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Initial Position ◽  
Urban Environments ◽  
Data Set ◽  
Registration Method ◽  
Position Errors ◽  
Initial Errors ◽  
High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

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