Path planning in three dimensional live environment with randomly moving obstacles for viscoelastic bio‐particle

2021 ◽  
Author(s):  
Moharam Habibnejad Korayem ◽  
Zahra Rastegar
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Moving Obstacles ◽  
Live Environment

scholarly journals Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 20100-20116
Author(s):  
Xianjin Zhou ◽  
Fei Gao ◽  
Xi Fang ◽  
Zehong Lan
Keyword(s):  
Path Planning ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Bat Algorithm ◽  
Three Dimensional Space

Robot Path Planning and Obstacle Avoidance: A Design Optimization Approach

1989 ◽  
Author(s):  
E. Sandgren ◽  
S. Venkataraman
Keyword(s):  
Path Planning ◽  
Design Optimization ◽  
Dynamic Programming Algorithm ◽  
Three Dimensional ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Robot Arm ◽  
Robot Path Planning ◽  
Real Time Control ◽  
Robot Path

Abstract A design optimization approach to robot path planning in a two dimensional workplace is presented. Obstacles are represented as a series of rectangular regions and collision detection is performed by an operation similar to clipping in computer graphics. The feasible design space is approximated by a discrete set of robot arm and gripper positions. Control is applied directly through the angular motion of each link. Feasible positions which are located between the initial and final robot link positions are grouped into stages. A dynamic programming algorithm is applied to locate the best state within each stage which minimizes the overall path length. An example is presented involving a three link planar manipulator. Extensions to three dimensional robot path planning and real time control in a dynamically changing workplace are discussed.

Path Planning for Spheres in Three Dimensional Environments With Low Interference Index

1994 ◽  
Author(s):  
Duane W. Storti ◽  
Debasish Dutta
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Configuration Space ◽  
Local Knowledge ◽  
Three Dimensional ◽  
Target Point ◽  
Planning Problem ◽  
Spherical Object ◽  
Starting Point ◽  
Path Planning Problem

Abstract We consider the path planning problem for a spherical object moving through a three-dimensional environment composed of spherical obstacles. Given a starting point and a terminal or target point, we wish to determine a collision free path from start to target for the moving sphere. We define an interference index to count the number of configuration space obstacles whose surfaces interfere simultaneously. In this paper, we present algorithms for navigating the sphere when the interference index is ≤ 2. While a global calculation is necessary to characterize the environment as a whole, only local knowledge is needed for path construction.

A-star algorithm based path planning for the glasses-free three-dimensional display system

2016 ◽  
Author(s):  
Bin Yang ◽  
Xinzhu Sang ◽  
Shujun Xing ◽  
Huilong Cui ◽  
Binbin Yan ◽  
...  
Keyword(s):  
Path Planning ◽  
Three Dimensional ◽  
Display System ◽  
Three Dimensional Display

Task Assignment and Path Planning of a Multi-AUV System Based on a Glasius Bio-Inspired Self-Organising Map Algorithm

2017 ◽  
Vol 71 (2) ◽  
pp. 482-496 ◽  
Author(s):  
Daqi Zhu ◽  
Yu Liu ◽  
Bing Sun
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Three Dimensional ◽  
Task Assignment ◽  
Working Environment ◽  
Neuron Activity ◽  
Target Point ◽  
System Task ◽  
Self Organising Map

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

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