A method to determine the local optimal path of ship navigation for convex obstacle

Introduction: Impacted teeth is complex anomaly of teeth eruption that requires a balanced approach not only in the differential diagnosis of its forms, but choice of rational methods of treatment. The aim: Optimization of the tactics of orthodontic-surgical treatment of patients with impacted teeth based on the development and implementation of computed tomographic indices (KT) and photometric indices (FM) of opening of surgical access (OSA) to crowns of impacted teeth Materials and methods: The results of treatment of 48 patients with delay of permanent teeth eruption have been analyzed. For an objective assessment of treatment results, a group of 24 (aged from 9 to 19 years old ) was formed. All 24 patients had typical clinical situation. Results: Orthodontic correction of patients envisaged, first of all, the elimination of obstacles in the way of teeth eruption, if necessary to provide space in dental arch and simultaneous treatment of associated bite malocclusions. Surgical exposure of impacted tooth crown was carried out and at the same time a triangular shaped guiding channel was formed, base of channel was at the impacted tooth and its angle finished into the dental arch. Precise dimensions and depth of the channel were preliminary planed on computed tomography slices with 3D reconstruction. Mean values of CT width (7,13±0,54mm), and length (6,42±0,78mm) of OSA and CT index (130,79±8,19%) of OSA to impacted teeth crowns were determined. Conclusion: To improve the quality of diagnosis and optimization of methodological approaches to treatment of patients with teeth impaction, we have proposed CT and FM OSA indices to the crowns of impacted teeth. The developed indices serve as specific reference points for optimization of diagnostic process, for reducing of probability of repeated surgical interventions and choosing the optimal path for instrumental orthodontic treatment of patients with impacted teeth

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Optimal path algorithm with multi-constrained condition

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.01101 ◽

2008 ◽

Vol 28 (5) ◽

pp. 1101-1103

Author(s):

Yong-gui ZOU

Keyword(s):

Optimal Path

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A Computational Approach for Near-Optimal Path Planning and Guidance for Systems with Nonholonomic Contraints

10.21236/ada520675 ◽

2010 ◽

Author(s):

Anil V. Rao ◽

William W. Hager

Keyword(s):

Path Planning ◽

Optimal Path ◽

Computational Approach ◽

Optimal Path Planning

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Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096512666190214105927 ◽

2020 ◽

Vol 13 (2) ◽

pp. 203-211 ◽

Cited By ~ 1

Author(s):

Swati Bhalaik ◽

Ashutosh Sharma ◽

Rajiv Kumar ◽

Neeru Sharma

Keyword(s):

Network Design ◽

Optical Networks ◽

Wavelength Division Multiplexing ◽

Optical Network ◽

Optimal Path ◽

Topology Design ◽

Mixed Integer ◽

Wdm Optical Networks ◽

Virtual Topology ◽

Wdm Network

Objective: Optical networks exploit the Wavelength Division Multiplexing (WDM) to meet the ever-growing bandwidth demands of upcoming communication applications. This is achieved by dividing the enormous transmission bandwidth of fiber into smaller communication channels. The major problem with WDM network design is to find an optimal path between two end users and allocate an available wavelength to the chosen path for the successful data transmission. Methods: This communication over a WDM network is carried out through lightpaths. The merging of all these lightpaths in an optical network generates a virtual topology which is suitable for the optimal network design to meet the increasing traffic demands. But, this virtual topology design is an NP-hard problem. This paper aims to explore Mixed Integer Linear Programming (MILP) framework to solve this design issue. Results: The comparative results of the proposed and existing mathematical models show that the proposed algorithm outperforms with the various performance parameters. Conclusion: Finally, it is concluded that network congestion is reduced marginally in the overall performance of the network.

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Recent advances on urban taxi sharing systems with the path arrival probability

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200402112913 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dui Hongyan ◽

Zhang Chi

Keyword(s):

Failure Probability ◽

Traffic Congestion ◽

Optimal Strategy ◽

Time Windows ◽

Optimal Path ◽

Route Planning ◽

Driving Time ◽

Load Rate ◽

Travel Efficiency ◽

Arrival Probability

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

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A novel navigation system for an autonomous mobile robot in an uncertain environment

Robotica ◽

10.1017/s0263574721000497 ◽

2021 ◽

pp. 1-26

Author(s):

Meng-Yuan Chen ◽

Yong-Jian Wu ◽

Hongmei He

Keyword(s):

Mobile Robot ◽

Navigation System ◽

Autonomous Navigation ◽

Clustering Algorithm ◽

Modular Design ◽

Optimal Path ◽

Surrounding Environment ◽

Computing Complexity ◽

The Arts ◽

Window Approach

Abstract In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

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

Sensors ◽

10.3390/s21062244 ◽

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.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse9030252 ◽

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.

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