Optimal Path Planning in Environments with Static Obstacles by Harmony Search Algorithm

2019 ◽  
pp. 186-193 ◽  
Author(s):  
Eva Tuba ◽  
Ivana Strumberger ◽  
Nebojsa Bacanin ◽  
Milan Tuba
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Optimal Path ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Path Planning

Optimal Path Planning of Redundant Cooperative Robots Under Equality and Inequality Constraints

2003 ◽  
Author(s):  
Ali Hosseini ◽  
Mehdi Keshmiri
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Minimum Principle ◽  
Optimal Path ◽  
Inequality Constraints ◽  
Optimal Path Planning ◽  
Nonlinear Inequality ◽  
Nonlinear Inequality Constraints ◽  
Cooperative Manipulators

Using kinematic resolution, the optimal path planning for two redundant cooperative manipulators carrying a solid object on a desired trajectory is studied. The optimization problem is first solved with no constraint. Consequently, the nonlinear inequality constraints, which model obstacles, are added to the problem. The formulation has been derived using Pontryagin Minimum Principle and results in a Two Point Boundary Value Problem (TPBVP). The problem is solved for a cooperative manipulator system consisting of two 3-DOF serial robots jointly carrying an object and the results are compared with those obtained from a search algorithm. Defining the obstacles in workspace as functions of joint space coordinates, the inequality constrained optimization problem is solved for the cooperative manipulators.

scholarly journals Mobile Robot Dynamic Path Planning Based on Self-adaptive Harmony Search Algorithm and Morphin Algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yongbin Quan ◽  
Haibin Ouyang ◽  
Chunliang Zhang ◽  
Steven Li ◽  
Liqun Gao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Self Adaptive

New Harmony Search Algorithm for Mobile Robot Path Planning

2021 ◽  
pp. 3463-3473
Author(s):  
Yongbin Quan ◽  
Wenqi Wei ◽  
Haibin Ouyang ◽  
Xuejing Lan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Robot Path Planning ◽  
New Harmony ◽  
Robot Path

scholarly journals Optimal Path Planning using Informed Probabilistic Road Map Algorithm

2021 ◽  
Author(s):  
Muhammad Aria ◽  
Keyword(s):  
Path Planning ◽  
Local Search ◽  
Search Algorithm ◽  
Optimal Path ◽  
Local Search Algorithm ◽  
Optimal Path Planning ◽  
Road Map ◽  
Planning Algorithm ◽  
Sample Points ◽  
Path Planning Algorithm

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems.

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