scholarly journals Phototropism rapidly exploring random tree: An efficient rapidly exploring random tree approach based on the phototropism of plants

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142094521
Author(s):  
Chengchen Zhuge ◽  
Jiayin Liu ◽  
Dongyan Guo ◽  
Ying Cui
Keyword(s):  
Light Intensity ◽  
Path Length ◽  
Beam Propagation Method ◽  
Optimal Solution ◽  
Sampling Strategy ◽  
Random Tree ◽  
Tree Algorithm ◽  
Novel Variant ◽  
Set Up ◽  
Exploration Period

Inspired by the phototropism of plants, a novel variant of the rapidly exploring random tree algorithm as called phototropism rapidly exploring random tree is proposed. The phototropism rapidly exploring random tree algorithm expands less tree nodes during the exploration period and has shorter path length than the original rapidly exploring random tree algorithm. In the algorithm, a virtual light source is set up at the goal point, and a light beam propagation method is adopted on the map to generate a map of light intensity distribution. The phototropism rapidly exploring random tree expands its node toward the space where the light intensity is higher, while the original rapidly exploring random tree expands its node based on the uniform sampling strategy. The performance of the phototropism rapidly exploring random tree is tested in three scenarios which include the simulation environment and the real-world environment. The experimental results show that the proposed phototropism rapidly exploring random tree algorithm has a higher sampling efficiency than the original rapidly exploring random tree, and the path length is close to the optimal solution of the rapidly exploring random tree* algorithm without considering the non-holonomic constraint.

scholarly journals Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141982594 ◽  
Author(s):  
Luiz GDO Véras ◽  
Felipe LL Medeiros ◽  
Lamartine NF Guimarães
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Computational Cost ◽  
Sampling Strategy ◽  
Random Tree ◽  
Sample Collection ◽  
Planning Time ◽  
Tree Algorithm ◽  
Aerial Vehicles ◽  
Intelligent Sampling

The path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.

scholarly journals How can remote sensing techniques help monitoring the vine and maximize the terroir potential?

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sampling Strategy ◽  
Wine Quality ◽  
Wine Production ◽  
Spectral Bands ◽  
High Resolution Satellite Images ◽  
Remote Sensing Techniques ◽  
Set Up

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.

Industrial Design of Motorcycle with Reference to Indian Population

2014 ◽  
Vol 592-594 ◽  
pp. 2659-2664 ◽  
Author(s):  
T. Jeyakumar ◽  
R. Gandhinathan
Keyword(s):  
Optimal Solution ◽  
Human Model ◽  
Anthropometric Data ◽  
Posture Analysis ◽  
Digital Human Model ◽  
Set Up ◽  
The Aesthetic ◽  
Decision Making Model ◽  
Digital Human ◽  
Data Collection Approach

India is the second largest 2W market in the world in terms of sales volumes after China. Motorcycles types that are marketed using sports tag are found to be anchored on performance attributes characterized by visual appeal, higher speeds, heady acceleration and superior ride, handling and braking. The chronograph of the sports segment in the Indian market is plotted. A goal defined design process is developed to produce creative ideas for aesthetic attributes-modern, youthful, aerodynamic, and aggressive. The optimal solution satisfying the aesthetic goal is determined using an operation decision making model based upon weighted generalized mean method. A motorcycle is generally straddled by the rider with manual transmission and can be considered as a constrained workstation. Some ergonomic considerations to fit users of different sizes on the same workstation should be taken into account when designing. A two-dimensional anthropometric data collection approach is followed for riders in India. The obtained anthropometric data concerning riding postures are used for posture analysis using digital human model in CAD software. The mutual trade-off between sporty riding style of the rider and comfort angles have been arrived to set up the final posture of the rider. The detailing of the appearance considering the aesthetic attributes and ergonomics are done. The developed design is aimed at improving appearance and ergonomic performance.

Temperature Measurements of a Gold Nanosphere Solution in Response to Light-Induced Hyperthermia

2013 ◽  
Author(s):  
Jun Kai Wong ◽  
Robert Taylor ◽  
Sungchul Baek ◽  
Yasitha Hewakuruppu ◽  
Xuchuan Jiang ◽  
...  
Keyword(s):  
Experimental Data ◽  
Thermal Treatment ◽  
Light Intensity ◽  
Cancer Cells ◽  
High Power ◽  
Temperature Rise ◽  
Plasmon Resonance ◽  
Resonance Wavelength ◽  
Set Up ◽  
Lamp Source

Gold nanospheres (GNSs), biocompatible nanoparticles that can be designed to absorb visible and near-infrared light, have shown great potential in induced thermal treatment of cancer cells via Plasmonic Photothermal Therapy (PPTT) [3]. In this study, light induced heating of a water-based dispersion of 20 nm diameter GNSs was investigated at their plasmon resonance wavelength (λ = 520 nm). Temperature changes of the solution at the point of light irradiation were measured experimentally. A heat transfer model was used to verify the experimental data. The effect of two key parameters, light intensity and particle concentration, on the solution’s temperature was investigated. The experimental results showed a significant temperature rise of the GNS solution compared to de-ionized water. The temperature rise of GNS solution was linearly proportional to the concentration of GNS (from 0.25–1.0 C, C = 1×1013 particles per ml) and the light intensity (from 0.25 to 0.5 W cm−2). The experimental data matches the modeling results adequately. Overall, it can be concluded that the hyperthermic ablation of cancer cells via GNS can be achieved by controlled by the light intensity and GNS concentration. A novel component of this study is that a high power lamp source was used instead of a high power laser. This means that only low cost components were used in the current experimental set-up. Moreover, by using suitable filters and white light from the high power lamp source, it is possible to obtain light in many wavelength bands for the study of other nanoparticles with different plasmon wavelength ranges. The current results represtent just one example in this versatile experimental set-up developed. It should be noted, however, the plasmon resonance wavelength used in this study is not within the therapeutic window (750–1300 nm) [13]. Therefore, the GNSs used in this experiment are only applicable to the surface induced thermal treatment of cancer cells, for instance, in the skin.

scholarly journals Building a Classifier of Onset Stroke Prediction Using Random Tree Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 61-66 ◽  
Author(s):  
Yu-Chen Chen ◽  
◽  
Takashi Suzuki ◽  
Masaaki Suzuki ◽  
Hiroyuki Takao ◽  
...  
Keyword(s):  
Random Tree ◽  
Tree Algorithm

scholarly journals Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yangyang Shi ◽  
Qiongqiong Li ◽  
Shengqiang Bu ◽  
Jiafu Yang ◽  
Linfeng Zhu
Keyword(s):  
Convergence Speed ◽  
Random Tree ◽  
Tree Search ◽  
Vehicle Model ◽  
Turning Angle ◽  
Tree Algorithm ◽  
Slow Convergence ◽  
Joint Simulation ◽  
Simulation Based ◽  
Vehicle Path

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.

scholarly journals Economic growth and the transition from non-renewable to renewable energy

2013 ◽  
Vol 19 (4) ◽  
pp. 417-439 ◽  
Author(s):  
Alfred Greiner ◽  
Lars Gruene ◽  
Willi Semmler
Keyword(s):  
Renewable Energy ◽  
Predictive Control ◽  
Capital Stock ◽  
Optimal Solution ◽  
Renewable Resource ◽  
Energy Sources ◽  
Negative Externality ◽  
Tax Rates ◽  
Set Up ◽  
The Impact

AbstractThe paper considers the transition of an economy from non-renewable to renewable energy. We set up a canonical growth model with damages in the household's welfare function and two energy sources – non-renewable and renewable energy. To produce renewable energy a capital stock must be built up. A socially optimal solution is considered that takes into account the negative externality from the non-renewable energy. We also study how the optimal solution can be mimicked in a market economy by policies using subsidies and tax rates. To solve the model numerically, we use Nonlinear Model Predictive Control. We study when a transition to renewable energy takes place and whether it occurs before the non-renewable resource is exhausted. In addition, we analyze the impact of the initial values of the non-renewable resource and of the capital stock on the time of paths of the variables.

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