scholarly journals Study of Goal Oriented Obstacle Avoidance for Mobile Robot

2021 ◽  
Vol 11 (10) ◽  
pp. 13-21
Author(s):  
Yeon Taek OH ◽  
Keyword(s):  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Optimal Path ◽  
Local Environment ◽  
The Other ◽  
Fixed Weight ◽  
Time Period ◽  
Optimum Path ◽  
The Cost ◽  
Future Plans

—This paper suggests simultaneous mechanism which makes UGV to secure its safety and recreate optimum path right after obstacle avoidance occurs. Research target and sensors is shown and the suggested optimum region path algorithm is presented. Also, this paper provides the performance of suggested algorithm by simulation, and will propose the direction of the future plans. The fixed and varying weight algorithm satisfy the constraints, once the robot arrives at the goal position in the simulation. However, the fixed weight algorithm was not able to drive on the shortest path, which is optimal, with many other successful weight values. On the other hand, the varying weight algorithm successfully generated the optimized path by changing the weight values in term of local environment. Especially, in the second simulation, the robot was able to safely arrive to the goal with little time period. This paper proposed VTV and Local Optimal path algorithm that the robot can generate the optimal path to the goal position without colliding obstacles as adapting Fuzzy controller which continuously optimizes the weight values of the cost function in terms of local environment

Probabilistic fuzzy controller based robotics path decision theory

2016 ◽  
Vol 13 (2) ◽  
pp. 181-192 ◽  
Author(s):  
B.K. Patle ◽  
D.R. Parhi ◽  
A. Jagadeesh ◽  
Sunil Kumar Kashyap
Keyword(s):  
Objective Function ◽  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Complex Environment ◽  
Content Type ◽  
New Ideas ◽  
Level Of Significance ◽  
High Level

Purpose This paper aims to comprise the new ideas for the efficient implementation to autonomous mobile robot navigation over the complex environment in presence of static obstacles. Design/methodology/approach The fuzzy decision via probability and its distribution is applied for the generating objective function subject to the robotics path and obstacle avoidance. The present objective function is formed to achieve high level of significance for the real-time obstacle avoidance and the efficiency. Findings The proposed controller makes a robot take its decision effectively in complex environment in a feasible time. In comparison with other AI approaches the proposed controller reflects that the proposed method outperforms in terms of optimal path and quality of solution. The experimental and simulation results are nearly same. Originality/value It has been tested in a complex crowded environment to find shorter path than existed approaches. For the validation, the experimental and simulation result using Matlab Software (R2008) has provided at the end of the paper.

The incumbent, challenger, and population during revolution and civil war

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Kjell Hausken ◽  
Mthuli Ncube
Keyword(s):  
Civil War ◽  
Public Goods ◽  
Arab Spring ◽  
The Other ◽  
Time Period ◽  
Geographic Regions

We consider revolutions and civil war involving an incumbent, a challenger, and the population. Revolutions are classified into eight outcomes. In four outcomes incumbent repression occurs (viewed as providing sub-threshold benefits such as public goods to the population). Accommodation occurs in the other four outcomes (benefits provision above a threshold). The incumbent and challenger fight each other. The incumbent may win and retain power or else lose, thereby causing standoff or coalition. In a standoff, which is costly, no one backs down and uncertainty exists about who is in power. In a coalition, which is less costly, the incumbent and challenger cooperate, compromise, and negotiate their differences. If the population successfully revolts against the incumbent, the challenger replaces the incumbent. Eighty-seven revolutions during 1961–2011, including the recent Arab spring revolutions, are classified into the eight outcomes. When repressive, the incumbent loses 46 revolutions, remains in power through 21 revolutions, and builds a coalition after 12 revolutions. When accommodative, the incumbent loses seven revolutions and builds a coalition after one revolution. The 87 revolutions are classified across geographic regions and by time-period.

scholarly journals Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls

2020 ◽  
Vol 12 (7) ◽  
pp. 2767 ◽  
Author(s):  
Víctor Yepes ◽  
José V. Martí ◽  
José García
Keyword(s):  
Black Hole ◽  
Sustainable Design ◽  
Retaining Walls ◽  
The Other ◽  
Trade Off ◽  
Construction Company ◽  
Geometric Variables ◽  
The Stability ◽  
The Cost ◽  
Black Hole Algorithm

The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.

scholarly journals Analysis of Operational Efficiencies and Costs for Extracting Thinned Woods in Small-Scale Forestry, Nasunogahara Area, Tochigi Prefecture, Japan

2020 ◽  
Vol 3 (1) ◽  
pp. 61
Author(s):  
Kazuhiro Aruga
Keyword(s):  
Transportation Costs ◽  
The Other ◽  
Small Scale ◽  
Light Truck ◽  
Operation Costs ◽  
Other Hand ◽  
Light Trucks ◽  
The Cost ◽  
Manual Extraction ◽  
Truck Transportation

In this study, two operational methodologies to extract thinned woods were investigated in the Nasunogahara area, Tochigi Prefecture, Japan. Methodology one included manual extraction and light truck transportation. Methodology two included mini-forwarder forwarding and four-ton truck transportation. Furthermore, a newly introduced chipper was investigated. As a result, costs of manual extractions within 10 m and 20 m were JPY942/m3 and JPY1040/m3, respectively. On the other hand, the forwarding cost of the mini-forwarder was JPY499/m3, which was significantly lower than the cost of manual extractions. Transportation costs with light trucks and four-ton trucks were JPY7224/m3 and JPY1298/m3, respectively, with 28 km transportation distances. Chipping operation costs were JPY1036/m3 and JPY1160/m3 with three and two persons, respectively. Finally, the total costs of methodologies one and two from extraction within 20 m to chipping were estimated as JPY9300/m3 and JPY2833/m3, respectively, with 28 km transportation distances and three-person chipping operations (EUR1 = JPY126, as of 12 August 2020).

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

scholarly journals Competing Conventions with Costly Information Acquisition

Games ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 53
Author(s):  
Roberto Rozzi
Keyword(s):  
Information Acquisition ◽  
Evolutionary Model ◽  
Equilibrium Selection ◽  
The Other ◽  
Group Interactions ◽  
Social Coordination ◽  
Long Run ◽  
Stochastic Stability Analysis ◽  
The Stability ◽  
The Cost

We consider an evolutionary model of social coordination in a 2 × 2 game where two groups of players prefer to coordinate on different actions. Players can pay a cost to learn their opponent’s group: if they pay it, they can condition their actions concerning the groups. We assess the stability of outcomes in the long run using stochastic stability analysis. We find that three elements matter for the equilibrium selection: the group size, the strength of preferences, and the information’s cost. If the cost is too high, players never learn the group of their opponents in the long run. If one group is stronger in preferences for its favorite action than the other, or its size is sufficiently large compared to the other group, every player plays that group’s favorite action. If both groups are strong enough in preferences, or if none of the groups’ sizes is large enough, players play their favorite actions and miscoordinate in inter-group interactions. Lower levels of the cost favor coordination. Indeed, when the cost is low, in inside-group interactions, players always coordinate on their favorite action, while in inter-group interactions, they coordinate on the favorite action of the group that is stronger in preferences or large enough.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

Economic analysis of endobronchial ultrasound (EBUS) as a tool in the diagnosis and staging of lung cancer in Singapore

2010 ◽  
Vol 26 (2) ◽  
pp. 170-174 ◽  
Author(s):  
Shin Yuh Ang ◽  
Rachel Woo Yin Tan ◽  
Mariko Siyue Koh ◽  
Jeremy Lim
Keyword(s):  
Lung Cancer ◽  
Lung Biopsy ◽  
Cost Savings ◽  
Needle Aspiration ◽  
The Other ◽  
Transbronchial Lung Biopsy ◽  
Endobronchial Ultrasound ◽  
Economic Evaluations ◽  
Diagnosis Of Lung Cancer ◽  
The Cost

Objectives: Endobronchial ultrasound (EBUS), encompassing endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) and Endobronchial ultrasound transbronchial lung biopsy (EBUS-TBLB) has been proven to be a useful modality in the staging and diagnosis of lung cancer. However, there are limited publications on the cost-effectiveness of EBUS and no economic evaluations relevant to the Singapore setting. An economic evaluation using our hospital's data was used to assess the cost implications of EBUS substituting where clinically appropriate: transthoracic needle aspiration; (TTNA), fluoroscopy-guided transbronchial lung biopsy (TBLB), and mediastinoscopy in the diagnosis and staging of lung cancer.Methods: Relationship between the clinical and economic implications of alternative modalities was modeled using data inputs that were relevant to the Singapore setting. Two decision analytic models were constructed to evaluate the cost of EBUS compared with TTNA, TBLB, and staging mediastinoscopy. Only direct costs were imputed.Results: In the base–case analysis, TTNA was the most economical strategy (SGD3,335 = US$2,403) where clinically suitable for the diagnosis of lung cancer as compared to the other options: TBLB (SGD4,499) and EBUS-TBLB (SGD4,857). On the other hand, EBUS-TBNA resulted in expected cost savings of SGD1,214 per positive staging of lung cancer as compared to mediastinoscopy.Conclusions: The use of EBUS-TBNA could result in cost savings of SGD1,214 per positive staging of lung cancer as compared to mediastinoscopy. Whereas TTNA was the most economical intervention for the diagnosis of lung cancer as compared to the other options, its main limitation lies in its suitability only for peripheral lung lesions and high complication rate.

The Total Costs of Ultrasonography Summary

PEDIATRICS ◽  
1990 ◽  
Vol 86 (2) ◽  
pp. 323-323
Author(s):  
CHUNG-PIN SHEIH ◽  
CHING-YUANG LIN
Keyword(s):  
School Children ◽  
Correct Diagnosis ◽  
The Other ◽  
Renal Ultrasonography ◽  
Benefit Ratio ◽  
The Cost ◽  
Renal Abnormalities

In Reply.— In our article, we reported on 645 renal abnormalities found in 132 686 school children screened through the use of renal ultrasonography. Of those with renal abnormalities, 50 patients had surgically correctable lesions. The other 595 cases have been examined fully to establish the correct diagnosis and the prevalence of renal abnormalities in school children. However, in this study, the cost to benefit ratio was determined by total expense to number of surgically treatable diseases.

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