Research on path planning of mobile robot based on improved ant colony algorithm

Luo, Qiang; Wang, Haibao; Zheng, Yan; Jing-chang, HE

doi:10.1007/s00521-019-04172-2

Cited by 260 publications

(144 citation statements)

References 12 publications

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“…The performance of the MACO algorithm is evaluated by independently repeating 20 times. In the same environment and parameters, the comparison results of the MACO algorithm and the algorithm [18] with respect to the trajectory planning are given in Table 2. These results are measured in terms of the worst value (Worst), the best value (Best), and the average value (Mean).…”

Section: A Single Uav Path Planingmentioning

confidence: 99%

“…Shao et al [17] presented an improved PSO algorithm to improve the solution's quality. In [18], an improved algorithm is used to overcome the problems of the slow convergence speed and low search efficiency of the ACO algorithm. Based on the different types of journals, intelligent algorithms, and planning standards, the current status of trajectory planning of UAVs is reviewed [19].…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Planning for UAV Based on Improved ACO Algorithm

et al. 2020

IEEE Access

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Trajectory planning is an important subject in the field of unmanned aerial vehicles (UAVs). However, existing methods do not solve some problems well, such as slow convergence speed and low searching efficiency of related algorithms and collisions between UAVs and the obstacles. Therefore, a method is proposed to solve a trajectory planning problem for multi-UAV in a static environment, which includes three main phases: the initial trajectory generation, the trajectory correction, and the smooth trajectory planning. First, the improved algorithm called MACO which introduces the metropolis criterion into the node screening mechanism of the ant colony optimization (ACO) algorithm is presented to generate the initial trajectory that can effectively avoid falling into the local optimal solution and stagnation. Then, considering size constraint of UAVs, this paper gives three trajectory correction schemes to solve the collision avoidance problem and further to optimize the initial trajectory. Last, the discontinuity originated from the sharp turn which occurred in the trajectory planning is solved by the inscribed circle (IC) smooth method, and the produced trajectory has shown better performance in reducing fuel consumption and improving the trajectory safety. Experimental results demonstrate that the proposed method has the high feasibility and effectiveness from aspects of the optimal solution, the collision avoidance, and the smooth trajectory in the trajectory planning problem for UAVs.

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Section: A Single Uav Path Planingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory Planning for UAV Based on Improved ACO Algorithm

et al. 2020

IEEE Access

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“…Each order has a final delivery date constraint (the same distributor may have different delivery dates for the two products A and B). Contact with process providers to obtain process-related data such as total production time and total production cost for each order at each process provider [38,39]. The time required for each process workshop of the core manufacturer to complete each process with the co-manufacturer is shown in Tab.…”

Section: Instance Datamentioning

confidence: 99%

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Hou

Xia

et al. 2019

Teh. vjesn.

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With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to reexamine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.

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“…Due to the construction of the unequal allocation pheromone, although this method improves the convergence speed of the algorithm, it sacrifices the global optimization ability of the algorithm, to a certain extent, and an error occurs when finding the optimal solution. Luo et al 8 introduces direction coefficients, safety distance judgment strategies, and improved pheromone update mechanisms to improve the operating efficiency and the convergence speed, of the algorithm to avoid being trapped into a deadlock. Due to the introduction of the safety distance, the algorithm is good for avoiding obstacles, but the algorithm will still produce errors when it finds the optimal path, which affects the robustness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on path planning of robot based on adaptive ACS fused with SHAA neural network

Chen

Longhui

Yebiao

2020

Measurement and Control

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In this paper, we proposed an adaptive ACS algorithm by introducing an adaptive pheromone volatility coefficient and the algorithm diversity dynamically varying in different iterations of the algorithm. It incorporates a shunting hierarchical hybrid neural network application algorithm (Shunting HHNN Application Algorithm, SHAA) to overcome the drawbacks of global optimization capabilities of ant colony system (ACS) in solving robot path and easily being trapped into the local optimal solution. Considering the influence of the activation value size on the selection of the grid in the SHAA neural network algorithm, the distance factor and the activation value are combined to improve the heuristic function. This will not only ensure the convergence speed, but also avoid the premature stagnation and being trapped into a local optimal path. Simulation results show that the algorithm discussed in this paper outperforms better in both the global optimization ability and the robustness.

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Research on path planning of mobile robot based on improved ant colony algorithm

Cited by 260 publications

References 12 publications

Trajectory Planning for UAV Based on Improved ACO Algorithm

Trajectory Planning for UAV Based on Improved ACO Algorithm

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

Research on path planning of robot based on adaptive ACS fused with SHAA neural network

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