An overview of path planning algorithms

Tang, Zhuozhen; Ma, Hongzhong

doi:10.1088/1755-1315/804/2/022024

Cited by 14 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The path planning of inspection robot is mainly the selection of intelligent optimization algorithm [7, 8]. An excellent optimization algorithm plays a vital role in the path planning of inspection robot.…”

Section: Introductionmentioning

confidence: 99%

Path planning of patrol robot based on modified grey wolf optimizer

Zhang

Ning

Li³

et al. 2023

Robotica

View full text Add to dashboard Cite

The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.

show abstract

Section: Introductionmentioning

confidence: 99%

Path planning of patrol robot based on modified grey wolf optimizer

Zhang

Ning

Li³

et al. 2023

Robotica

View full text Add to dashboard Cite

show abstract

“…w = w + 1 26: end while 27: Calculate T utilizing formula (8) 28: return T append b to U . Then, we judge whether the node p u [w] has been explored for each space robot u in U .…”

Section: B Greedy-based Task Assignmentmentioning

confidence: 99%

“…As early as 1994, some scholars have already systematically studied the classical path planning problem [6]. According to the availability of information of global environment, path planning can be simply divided into global path planning, and local path planning [7], [8]. Global path planning is usually implemented on the premise that the information of global environment has been known.…”

Section: Introductionmentioning

confidence: 99%

Joint Task Allocation and Path Planning for Space Robot

Sun

Liu

2023

IEEE Access

View full text Add to dashboard Cite

Space robots have a broad application prospect in the aerospace industry. It is difficult for space robots to keep running for a long time due to limited fuel. In addition, it is impossible to replenish the fuel for space robots at any time due to the unique working environment. Therefore, a proper path is crucial for the effective operation of the space robot. In this paper, we investigate the allocation of exploration tasks and the path planning of space robots jointly. The goal is to minimize the completion latency of exploration tasks. We propose two algorithms named subbranch insertion task allocation (SI-TA) and parallel search task allocation (PS-TA) to solve the problem. We also customize an algorithm named random path planning task allocation (RTA) as the baseline. At last, we implement extensive experiments to demonstrate that proposed algorithms can obtain lower completion latency than RTA. Compared with RTA, the proposed algorithms SI-TA and PS-TA can reduce completion latency by at least 20% and 40%, respectively. Moreover, both algorithms work more stably than RTA.

show abstract

“…The A* algorithm is a modification of the Dijkstra algorithm. A-star is considered the most representative algorithm [15]. The task of determining a valid low-cost path from start to goal locations in an environmental map is referred to as path planning.…”

Section: Path Planningmentioning

confidence: 99%

Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

Yosif¹,

Mahmood²,

Saeed³

2022

JESA

View full text Add to dashboard Cite

The autonomous mobile robot must be capable of avoiding static and dynamic obstacles in the environment and navigating towards the target without any human effort. A valid low-cost path from start to goal is obtained by A* algorithm. Neural network used for Zone classification. The relative values between mobile robot and obstacle are used for classification which are distance, velocity, and angle. Zone1 is very dangerous while zone 5 is not dangerous. If the neural network classifies the obstacle as a dangerous obstacle and activates the controller. The fuzzy logic makes a decision as a reaction of mobile robot to prevent collision. There are three inputs to the fuzzy logic (relative velocity, relative distance, and relative angle) between mobile robot and obstacle. The outputs of fuzzy logic are velocity and steering angle of mobile robot. Static obstacles have been added to the environment in addition to dynamic obstacles to make the environment more complex. Three dangerous dynamic obstacles to the mobile robot are tested. While mobile robot is avoiding one obstacle, another obstacle enters critical zone and becomes dangerous to mobile robot. The mobile robot avoids the second obstacle while it is avoiding the first obstacle. Then the velocities of mobile robot and obstacles have been increased to prove that the proposed system can handle cases with faster velocities. The simulation results for the tested cases shows the capability of the proposed method for avoiding static and dynamic obstacles in fully known environment.

show abstract

An overview of path planning algorithms

Cited by 14 publications

References 5 publications

Path planning of patrol robot based on modified grey wolf optimizer

Path planning of patrol robot based on modified grey wolf optimizer

Joint Task Allocation and Path Planning for Space Robot

Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

Contact Info

Product

Resources

About