A Planning Algorithm with Temporal Constraints in the Working Procedures

2013 ◽  
Vol 760-762 ◽  
pp. 1786-1789
Author(s):  
Xiao Long Chai
Keyword(s):  
Ant Colony Algorithm ◽  
Convergence Acceleration ◽  
Research Area ◽  
Ant Colony ◽  
Temporal Constraints ◽  
Temporal Planning ◽  
Control Rules ◽  
Real World Applications ◽  
Planning Algorithm ◽  
Planning Problems

Temporal planning is a broad research area in automated planning. In most real-world applications such as the problems of working procedures planning, many real planning problems often require the planning goals can be satisfied in shorter time and some temporal constraints should be satisfied in the planning answer. In this paper, the ant colony algorithm under the temporal constraints is presented which with the heuristic control rules and the evaluation tactics in the framework of the ant colony planning algorithm. The searching way of the algorithm has the character of global and parallel. And it has the ability of convergence acceleration in the solution searching.

scholarly journals Path Planning for Construction Machinery Based on Improved Potential Field Ant Colony Algorithm

2021 ◽  
Vol 2095 (1) ◽  
pp. 012062
Author(s):  
Peigang Li ◽  
Pengcheng Li ◽  
Yining Xie ◽  
Xianying Feng ◽  
Bin Hu ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Weight Coefficient ◽  
Field Method ◽  
Ant Colony ◽  
Construction Machinery ◽  
Field Force ◽  
Heuristic Information ◽  
Planning Algorithm

Abstract The path planning algorithm of unmanned construction machinery is studied, and the potential field ant colony algorithm is improved to be applied in the field of unmanned construction machinery. Firstly, the raster map modeling was optimized to eliminate the trap grid in the map. At the beginning of algorithm iteration, the heuristic information of artificial potential field method was added and the global pheromone updating model was improve the convergence speed of the algorithm. In addition, the weight coefficient of potential field force and local pheromone updating model were introduced to enhance the development of raster map in the later iteration of ant colony algorithm and reduce the influence of heuristic information of potential field force. Finally, the selection range of parameters such as optimal pheromone heuristic factor and ant colony number is determined by simulation, and it is verified that the algorithm is better than the basic ant colony algorithm.

Simulation of English intelligent system based on CA-IAFSA algorithm and artificial intelligence

2021 ◽  
pp. 1-11
Author(s):  
Xinyu Wei
Keyword(s):  
Artificial Intelligence ◽  
Information Search ◽  
Ant Colony Algorithm ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Transition Probability ◽  
Ant Colony ◽  
Feedback Strategies ◽  
Planning Algorithm ◽  
Update Strategy

The traditional English teaching mode mostly relies on rote memorization of textbooks, but it lacks the training of oral expression skills and lacks intelligent guidance for students. Taking machine learning algorithm as the system algorithm, this paper combines the CA-IAFSA algorithm to construct an English intelligent system based on artificial intelligence. The system uses image recognition technology, introduces population pheromone and tribal pheromone, and adopts multiple ant colony planning and dual pheromone feedback strategies. Moreover, this paper improves the heuristic information search strategy, pheromone update strategy, and state transition probability of the basic ant colony algorithm. In addition, this paper proposes the MACDPA path planning algorithm to realize the intelligent analysis of English textbook images. Finally, after constructing the model, this paper conducts research and analysis on the performance of the model and uses controlled experimental methods and mathematical statistics to analyze the data. The research results show that the model constructed in this paper performs well in assisted teaching and intelligent translation and meets the expected requirements.

A Path-Planning Algorithm for AGV Based on the Combination between Ant Colony Algorithm and Immune Regulation

2011 ◽  
Vol 422 ◽  
pp. 3-9 ◽  
Author(s):  
Jian Zhong Huang ◽  
Yu Wan Cen
Keyword(s):  
Path Planning ◽  
Immune Regulation ◽  
Ant Colony Algorithm ◽  
Visibility Graph ◽  
Ant Colony ◽  
Environment Modeling ◽  
Environment Model ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

For the demand of AGV’s environment modeling and path-planning,the paper discusses how to establish static environment model of visibility graph and proposes a visibility table method.Moreover,based on the environment modeling,we put forward a new kind of global path-planning algorithm by the combination between ant colony algorithm and immune regulation.

Optimization Design of Fuzzy Control Rules Based on Ant Colony Algorithm

2014 ◽  
Vol 716-717 ◽  
pp. 1662-1665
Author(s):  
Ya Lang Xing ◽  
He Xin ◽  
Jin Cheng Zhao
Keyword(s):  
Fuzzy Control ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Fuzzy Rule ◽  
Coefficient Matrix ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Control Rules ◽  
And Control

To avoid the fuzzy rules getting into “rule exploding” in fuzzy control system, a fuzzy control rules optimization algorithm based on compatibility coefficient is proposed. The method defines the compatibility coefficient of fuzzy rules, and the compatibility coefficient matrix is used to be the heuristic information in ant colony algorithm. Ant colony algorithm is used to optimize designed complete fuzzy rule base. Simulation results show that the fuzzy rules have good compatibility and control performance.

scholarly journals Path Planning Algorithm for Mobile Robot Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2083 (4) ◽  
pp. 042033
Author(s):  
Yanyun Li ◽  
Fenggang Liu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Path Length ◽  
Ant Colony Algorithm ◽  
Planning Process ◽  
Optimal Path ◽  
Ant Colony ◽  
Position Information ◽  
Environment Model ◽  
Planning Algorithm

Abstract Due to the influence of full traversal environment, the path length obtained by existing methods is too long. In order to improve the performance of path planning and obtain the optimal path, a full traversal path planning method for omnidirectional mobile robots based on ant colony algorithm is proposed. On the basis of the topology modeling schematic diagram, according to the position information of the mobile robot in the original coordinate system, a new environment model is established by using the Angle transformation. Considering the existing problems of ant colony algorithm, the decline coefficient is introduced into the heuristic function to update the local pheromone, and the volatility coefficient of the pheromone is adjusted by setting the iteration threshold. Finally, through the design of path planning process, the planning of omnidirectional mobile robot’s full traversal path is realized. Experimental results show that the proposed method can not only shorten the full traversal path length, but also shorten the time of path planning to obtain the optimal path, thus improving the performance of full traversal path planning of omnidirectional mobile robot.

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