A novel method for real-time globally optimized path planning in a dynamic environment based on ant colony system algorithm

2016 ◽  
Author(s):  
Qing-Quan Wu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Ant Colony ◽  
Ant Colony System ◽  
Novel Method

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

Travelling and Steering Path Adjustment of Blueberry Harvesters

2011 ◽  
Vol 486 ◽  
pp. 25-28
Author(s):  
Zhi Peng Li ◽  
Dong Sheng Li
Keyword(s):  
Control System ◽  
Path Planning ◽  
Real Time ◽  
System Development ◽  
Path Selection ◽  
Ant Colony ◽  
New Knowledge ◽  
Knowledge Support ◽  
Virtual Modeling ◽  
Adjustment System

A picking and steering adjustment system for blueberry harvesters has been developed. In this paper, the main hardware and working principles of the system is introduced first, then the application of an ant colony simplification algorithm in the system development is presented. Information of virtual modeling the blueberry plant images and fruit distributions is obtained through the control system which is used as input for the ant colony simplification algorithm calculation. Then results are translated into real-time travelling path planning instructions for the blueberry harvester. The research provided technological and new knowledge support for future investigations into intelligent travelling path selection, thus playing an important role in mechanization and intelligent harvesting processes for blueberry harvesters.

scholarly journals Real-Time Path Planning for Multi-DoF Manipulators in Dynamic Environment

10.5772/5749 ◽  
2006 ◽  
Vol 3 (2) ◽  
pp. 20 ◽  
Author(s):  
Samir Lahouar ◽  
Said Zeghloul ◽  
Lotfi Romdhane
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Time Path

An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment

2000 ◽  
Vol 4 (3) ◽  
pp. 220-229 ◽  
Author(s):  
Simon X. Yang ◽  
◽  
Max Meng ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Lyapunov Stability Theory ◽  
Network Size ◽  
Computationally Efficient ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Stability

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.

Sign in / Sign up

Export Citation Format

Share Document