scholarly journals Spatial cellular robot in orbital truss collision-free path planning

2020 ◽  
Vol 11 (2) ◽  
pp. 233-250
Author(s):  
Ye Dai ◽  
Zhaoxu Liu ◽  
Yunshan Qi ◽  
Hanbo Zhang ◽  
Bindi You ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Early Stage ◽  
Gravitational Search Algorithm ◽  
Space Station ◽  
Optimal Solution ◽  
Ant Colony ◽  
Triangular Prism ◽  
Exploration Area

Abstract. Aiming at the problem of moving path planning of a cellular robot on trusses in space station, a triangular prism truss is taken as the research object, and an optimized ant colony algorithm incorporating a gravitational search algorithm is proposed. The innovative use of the hierarchical search strategy which limits the exploration area, the use of gravity search algorithm to get the optimal solution of truss nodes, and further transform it into the initial value of pheromone in ant colony algorithm, can effectively prevent the algorithm from falling into the local optimal solution in the early stage, and make the optimization algorithm have a faster convergence speed. This paper proposes a heuristic function including the angle between the targets, which can effectively avoid blind search in the early stage and improve the ability of path search. The simulation results show that the path and planning time of the cellular robot can be effectively reduced when choosing truss path.

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

scholarly journals AGV optimal path planning based on improved ant colony algorithm

2018 ◽  
Vol 232 ◽  
pp. 03052 ◽  
Author(s):  
Chengwei He ◽  
Jian Mao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Undirected Network ◽  
Optimal Path Planning ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Improved Ant Colony Algorithm

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

scholarly journals Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

2022 ◽  
Vol 355 ◽  
pp. 03002
Author(s):  
Hongchao Zhao ◽  
Jianzhong Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Search Speed ◽  
Aerial Vehicle ◽  
Improved Ant Colony Algorithm

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

scholarly journals An Improved Ant Colony Algorithm and Its Application in Vehicle Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Min Huang ◽  
Ping Ding
Keyword(s):  
Path Planning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Optimal Path ◽  
Optimal Solution ◽  
Planning Method ◽  
Ant Colony ◽  
Routing Problem

Optimal path planning is an important issue in vehicle routing problem. This paper proposes a new vehicle routing path planning method which adds path weight matrix and save matrix. The method uses a new transition probability function adding the angle factor function and visibility function, while setting penalty function in a new pheromone updating model to improve the accuracy of the route searching. Finally, after each cycle, we use 3-opt method to update the optimal solution to optimize the path length. The results of comparison also confirm that this method is better than the traditional ant colony algorithm for vehicle routing path planning method. The result of computer simulation confirms that the method can plan a more rational rescue path focused on the real traffic situation.

Research on QoS Multicast Tree Based on Ant Colony Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1734-1737 ◽  
Author(s):  
Yong Huang
Keyword(s):  
Data Transmission ◽  
Ant Colony Algorithm ◽  
Multicast Routing ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Multicast Tree ◽  
Ant Colony ◽  
Shortest Path Algorithm ◽  
Routing Problem ◽  
Delay Constraints

Ant colony algorithm is a stochastic search algorithm, evolutionary algorithm with other models, like the evolution of the composition of the population by the candidate solutions to find the optimal solution, this paper proposes a new ant colony algorithm to solve by bandwidth and QoS multicast routing problem delay constraints, k shortest path algorithm by means of genetic algorithm we propose obtained, and then use the ant colony algorithm to construct optimal multicast tree for data transmission.

Collision-Free Path Planning for Mobile Cranes Based on Ant Colony Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 1108-1115 ◽  
Author(s):  
Xin Wang ◽  
Y.Y. Zhang ◽  
Di Wu ◽  
Shun De Gao
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Degrees Of Freedom ◽  
Ant Colony Algorithm ◽  
Engineering Application ◽  
Optimal Solution ◽  
Ant Colony ◽  
Working Environment ◽  
Full Account ◽  
Comparison Results

This paper presents the work done towards searching a collision-free path for mobile crane based on C-space in the complex 3D working environment. The crane is simplified into three degrees of freedom (DOFs) robot, each of which is represented as an axis of configuration space (C-space). In this paper, we propose an improved ant colony approach for crane path planning, which takes into full account of not only the factor of the shortest path but also the factor of safety. In this approach, we employ more complete heuristic information, introduce adaptive pheromone volatilization coefficient and pheromone penalty factors, and prevent ants from falling into trap and the stagnation. The reasonability and practicability of the proposed approach for automated path planning is verified by comparing the performances of the present approaches in the practice case, and the comparison results show that the algorithm can gain a relatively optimal solution in short time and have a great value of engineering application.

scholarly journals Research on Reactive Power Optimization for Distribution Network Based on Dual Population Improved Ant Colony Algorithm

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yiran Jiang
Keyword(s):  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Distribution Network ◽  
Optimal Solution ◽  
Ant Colony ◽  
Reactive Power Optimization ◽  
Heuristic Search Algorithm ◽  
Improved Ant Colony Algorithm

Ant Colony Algorithm is a heuristic search algorithm based on probability selection. It fits for solving the reactive power optimization problem of distribution network, but at the same time, easily falling into the problems of local optimal solution. So Dual Population Improved Ant Colony Algorithm is used to study Reactive Power Optimization Solution. Finally, with an actual example calculation and analysis, and node voltage comparison with and without compensation, the results are proved to be satisfactory. It verified the effectiveness and feasibility of the algorithm and the results show that the algorithm has better effect on optimization.

scholarly journals Application of an Improved Ant Colony Algorithm to Optimal Path Planning for Automatic Guided Vehicles

2020 ◽  
Author(s):  
weikang zhu ◽  
jicheng liu
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Transition Probability ◽  
Optimal Solution ◽  
Random Selection ◽  
Ant Colony ◽  
Smoothing Factor ◽  
Local Optimal Solution ◽  
Selection Operator ◽  
Improved Ant Colony Algorithm

Abstract The path planning is the key technology of AGV path finding. This paper uses an improved ant colony algorithm to plan the path of an AGV. For avoiding the defects of traditional ant colony algorithm such as low smoothness of route and local optimal solution, the transition probability and pheromone update method are improved. Various actual turning situations are analyzed in the transition probability, the basis for defining the smoothing factor is provided by the Bezier curve, and a random selection operator is formed for updating local pheromone by extracting characteristic information of iterative process. The simulation results in different environments prove that the smoothing factor plays an important role in optimizing the smoothness of the path and the diversity of the constructed solutions, and the random selection operator is effective in solving the contradiction of the local optimal solution and in finding the optimal solution.

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