PSO algorithm for UAV autonomous path planning with threat and energy cost optimization

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

Download Full-text

Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

Advances in Production Engineering & Management ◽

10.14743/apem2021.4.409 ◽

2021 ◽

Vol 16 (4) ◽

pp. 405-417

Author(s):

L. Banjanovic-Mehmedovic ◽

I. Karabegovic ◽

J. Jahic ◽

M. Omercic

Keyword(s):

Path Planning ◽

Optimal Path ◽

Pso Algorithm ◽

Optimal Path Planning ◽

Localization And Mapping ◽

Increasing Demand ◽

Window Approach ◽

Path Planner ◽

Hardware Platforms ◽

Reactive Collision

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.

Download Full-text

Using ALC-PSO algorithm with particle growing method path planning in dynamic environments

2014 CACS International Automatic Control Conference (CACS 2014) ◽

10.1109/cacs.2014.7097170 ◽

2014 ◽

Author(s):

Hung-Yuan Chung ◽

Yong-An Ye ◽

Jyun-Fu Jiang

Keyword(s):

Path Planning ◽

Pso Algorithm ◽

Dynamic Environments

Download Full-text

Energy cost optimization in water distribution systems using Markov Decision Processes

2013 International Green Computing Conference Proceedings ◽

10.1109/igcc.2013.6604516 ◽

2013 ◽

Cited By ~ 3

Author(s):

Paulo Thiago Fracasso ◽

Frank Stephenson Barnes ◽

Anna Helena Reali Costa

Keyword(s):

Markov Decision Processes ◽

Energy Cost ◽

Distribution Systems ◽

Water Distribution ◽

Cost Optimization ◽

Water Distribution Systems ◽

Decision Processes ◽

Markov Decision

Download Full-text

Research on Manipulator Path Planning Based on PSO Algorithm

10.1007/978-981-16-6320-8_32 ◽

2021 ◽

pp. 302-309

Author(s):

Yulin Li ◽

Dong Guo ◽

Weier Qin

Keyword(s):

Path Planning ◽

Pso Algorithm

Download Full-text

Energy Cost Optimization for Water Distribution Networks Using Demand Pattern and Storage Facilities

Sustainability ◽

10.3390/su10041118 ◽

2018 ◽

Vol 10 (4) ◽

pp. 1118 ◽

Cited By ~ 7

Author(s):

Yungyu Chang ◽

Gyewoon Choi ◽

Juhwan Kim ◽

Seongjoon Byeon

Keyword(s):

Energy Cost ◽

Water Distribution ◽

Cost Optimization ◽

Distribution Networks ◽

Water Distribution Networks ◽

Demand Pattern ◽

And Storage ◽

Storage Facilities

Download Full-text

Path planning for intelligent robot based on switching local evolutionary PSO algorithm

Assembly Automation ◽

10.1108/aa-10-2015-079 ◽

2016 ◽

Vol 36 (2) ◽

pp. 120-126 ◽

Cited By ~ 28

Author(s):

Nianyin Zeng ◽

Hong Zhang ◽

Yanping Chen ◽

Binqiang Chen ◽

Yurong Liu

Keyword(s):

Markov Chain ◽

Path Planning ◽

Three Dimensional ◽

Grid Method ◽

Pso Algorithm ◽

Planning Problem ◽

Homogeneous Markov Chain ◽

Intelligent Robot ◽

Environment Modeling ◽

Content Type

Purpose This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment. Design/methodology/approach The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method. Findings Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method. Originality/value Therefore, this can provide a new method for the area of path planning of intelligent robot.

Download Full-text

Camera-based autonomous mobile robot path planning and trajectory tracking using PSO algorithm and PID controller

2017 International Conference on Control, Automation and Diagnosis (ICCAD) ◽

10.1109/cadiag.2017.8075657 ◽

2017 ◽

Author(s):

Feten Hmeyda ◽

Faouzi Bouani

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Trajectory Tracking ◽

Pid Controller ◽

Pso Algorithm ◽

Autonomous Mobile Robot ◽

Robot Path Planning ◽

Robot Path

Download Full-text

A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

Journal of Intelligent Systems ◽

10.1515/jisys-2013-0066 ◽

2015 ◽

Vol 24 (1) ◽

pp. 69-83 ◽

Cited By ~ 5

Author(s):

Zhonghua Tang ◽

Yongquan Zhou

Keyword(s):

Path Planning ◽

Heuristic Algorithms ◽

Optimization Methods ◽

Pso Algorithm ◽

Population Based ◽

Computational Accuracy ◽

Swarm Optimization ◽

Glowworm Swarm Optimization ◽

Air Vehicle ◽

The Individual

AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

Download Full-text