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Intelligent Warehouse Robot Path Planning Based on Improved ant colony algorithm

IEEE ACCESS 2023年 11卷 12360-12367页

作者： Chen, Yun Wu, Jinfeng He, Chaoshuai Zhang, Si Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang Peoples R China China State Shipbuilding Corp Res Inst Lianyungang 222061 Peoples R China

To improve the safety and accuracy of path planning of intelligent warehouse robots, this paper establishes a storage shelf model, incorporates Poisson Distribution to simulate the influence of unknown factors, and establishes a three-color raster map. The pheromone update mechanism is optimized by considering the path safety, path length, and turning elements under the influence of unknown factors. The two models based on the three-dimensional shelves are simulated separately, and the planned paths are de-pointed and smoothed. The simulation results show that the improved algorithm can design the optimal route safely and effectively in the storage environment under the influence of unknown factors. The proposed algorithm not only solves the blind search and deadlock problems, but also has better performances than other algorithms, i.e., 4 iterations compared to 22 and 30 iterations, 3 turns compared to 9 and 7 turns, 8.468s running time compared to 16.974s and 13.754s.

关键词： ant colony optimization Path planning Convergence Safety Optimization Heuristic algorithms Robot kinematics Warehouses Mobile robots Poisson equations Intelligent warehouse robot ant colony algorithm path planning poisson distribution three-color grid map

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A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

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COMPLEX & INTELLIGENT SYSTEMS 2023年第3期9卷 2491-2508页

作者： Wu, Hongguang Gao, Yuelin Wang, Wanting Zhang, Ziyu North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Key Lab Intelligent Informat & Data Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Cooperat Innovat Ctr Sci Comp & Inte Yinchuan 750021 Ningxia Peoples R China

In this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method;the second is the introduction of adaptive parameters;and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

关键词： Vehicle routing problem ant colony algorithm Mutation operation Adaptive parameter Practical application

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Research on Global Ship Path Planning Method Based on Improved ant colony algorithm

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2023年 4卷 143-152页

作者： Zhang, Ming Ren, Hongxiang Zhou, Yi Fujian Chuanzheng Commun Coll Nav Coll Fujian 350007 Peoples R China Dalian Maritime Univ Nav Coll Dalian 160000 Peoples R China CNOOC Energy Technol Co Service Oil Prod Serv Branch Tianjin 300000 Peoples R China

To solve the global path planning problem of the ship in the static and dynamic environment, we propose an improved ant colony algorithm to plan the ship's navigation path. We use the artificial potential field method to compute the force direction of the ship at the initial iteration stage. The attraction potential field function is modified to improve the iteration efficiency of the hybrid ant colony algorithm. We design the pseudo-random state transition rule and improve the convergence of the hybrid algorithm by strengthening the selection of good paths. When updating the pheromone, we consider the path's length, safety, and smoothness to plan a safer navigation path. The simulation results show that the improved ant colony algorithm has a faster convergence speed than the original ant colony algorithm. The optimal solution quality is higher, which can realize global ship path planning in static and dynamic environments.

关键词： Marine vehicles Path planning Heuristic algorithms Navigation Force Convergence Safety Ship path planning ant colony algorithm artificial potential field pheromone update

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Mobile Robot Path Planning Based on Improved Elite ant colony algorithm 8

Mobile Robot Path Planning Based on Improved Elite Ant Colon...

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8th International Conference on Robotics, Control and Automation (ICRCA)

作者： Yu, Kaiying Xu, Bin Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai Peoples R China

ISBN: (纸本)9798350344738;9798350344721

Aiming at the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm in solving path planning problem, an improved elite ant colony algorithm is proposed. Firstly, the pseudo-random state transition rule is used to select the path, and a dynamic selection factor is defined to adaptively update the selection ratio to improve the search efficiency and path search ability of the algorithm. Secondly, an elite path selection operator is designed to improve the quality of the search path by retaining the better path. Finally, based on the elite ant model, an improved pheromone incentive strategy is proposed to update the pheromone increment and improve the convergence speed of the algorithm. The comparative experiments of path planning are carried out under different complexity grid maps. The results show that the improved elite ant colony algorithm has faster convergence speed, and has better global search ability while improving search efficiency, which verifies the effectiveness and practicability of the algorithm.

关键词： path planning ant colony algorithm elite strategy pheromone concentration

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3D Path Planning for Amphibious UAV with lmproved ant colony algorithm 4

3D Path Planning for Amphibious UAV with lmproved Ant Colony...

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4th International Conference on Computer, Control and Robotics (ICCCR)

作者： Sun, Qiurong Li, Xinde Southeast Univ Sch Elect Engn Nanjing Peoples R China Southeast Univ Sch Automat Nanjing Peoples R China Nanjing Ctr Appl Math Nanjing Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350373141;9798350373158

Land-Air Amphibious UAVs possess agile mobility and remarkable adaptability, but conventional path planning algorithms struggle to meet their high energy density requirements. This paper proposes a land-air amphibious energy-efficient ant colony optimization algorithm (LAE2-ACO) to address the issues of high energy consumption, long search times, and susceptibility to local optima. Several improvements are made to the pheromone updating and heuristic functions. Firstly, time decay factors are introduced to enhance early exploration and accelerate convergence. Secondly, a mirror attraction field is incorporated to conserve energy consumption. Additionally, a smoothing factor is introduced to reduce path jitter. Through simulation experiments, we compare LAE2-ACO algorithm with traditional ant colony optimization (ACO), A*, and RRT algorithms by evaluating UAV operation time, route length, and energy consumption. Experimental results demonstrate that our algorithm achieves lower energy consumption, shorter search times, and shorter route lengths.

关键词： amphibious UAV path planning ant colony algorithm

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Research on UAV cloud control system based on ant colony algorithm

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Journal of Systems Engineering and Electronics 2022年第4期33卷 805-811页

作者： ZHANG Lanyong ZHANG Ruixuan School of Intelligent Science and Engineering Harbin Engineering UniversityHarbin 150001China

In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the difficulty of data processing in the cloud era, it is extremely important to perform massive data operations through cloud servers. Unmanned aeriel vehicle(UAV) control is the representative of the intelligent field. Based on the ant colony algorithm and incorporating the potential field method, an improved potential field ant colony algorithm is designed. To deal with the path planning problem of UAVs, the potential field ant colony algorithm shortens the optimal path distance by 6.7%, increases the algorithm running time by39.3%, and increases the maximum distance by 24.1% compared with the previous improvement. The cloud server is used to process the path problem of the UAV and feedback the calculation results in real time. Simulation experiments verify the effectiveness of the new algorithm in the cloud environment.

关键词： ant colony algorithm potential field method cloud server path planning

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A Flexible Charging Pile Scheduling Optimization Platform Based on ant colony algorithm 7

A Flexible Charging Pile Scheduling Optimization Platform Ba...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Li, Guoqing Hu, Hanghua Miao, Leying Wu, Hao Li, Shenna Huang, Yihua Liu, Jiaxin Wang, Linyan Cai, Xingpu Guo, Xiongbai Xiao, Yiyao Shangcheng Power Supply Branch 158 Jianguo South Rd Hangzhou Peoples R China

ISBN: (纸本)9798350377040;9798350377033

In this paper, taking the load of electric vehicle charging piles as the cutting point, by analyzing various constraints such as the access state of electric vehicles and the characteristics of charging piles, a refined single equipment model and a participation demand response adjustment model are established, an optimal scheduling strategy and incentive compensation measures are proposed, and a charging station heuristic insertion algorithm is designed to quickly calculate the insertion position of charging stations. At the same time, an improved ant colony algorithm was proposed by designing a local search algorithm and integrating it into the iterative process of ant colony algorithm. Experimental results show that the local search embedded in the ant colony algorithm has significant advantages in improving the quality of the solution. It provides theoretical and practical support for the application of demand-side resources in the power market, and provides new ideas and methods for the balance of supply and demand in the power system and the development of new energy.

关键词： Flexible charging ant colony algorithm Charging load

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ant colony algorithm based on magnetic neighborhood and filtering recommendation

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SOFT COMPUTING 2021年第13期25卷 8035-8050页

作者： Yu, Jin You, Xiaoming Liu, Sheng Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Univ Engn Sci Sch Management Shanghai 201620 Peoples R China

To deal with the problems that the ant colony algorithm has slow convergence speed and easy falling into the local optimum when solving TSP, an ant colony algorithm based on magnetic neighborhood and filtering recommendation (MRACS) is proposed to solve these problems. First, a dynamic magnetic neighborhood strategy is adopted to balance the convergence speed and the solution accuracy by magnetic attraction. It attracts ants to enlarge the exploration of a better neighborhood, thus improving the accuracy of the result. Second, a cross-excitation strategy based on filtering recommendation is applied to increase the diversity of the algorithm by dynamic weakening or enhancing local pheromones in the neighborhoods. It aids the algorithm get rid of the local optimum. Through the simulation experiments and the rank-sum test analysis, it is observed that the MRACS can effectively balance the convergence speed and the accuracy of the solution.

关键词： ant colony algorithm TSP Collaborative filtering recommendation Magnetic neighborhood

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Enhanced the security of path planning for mobile robots on uneven terrain based on multi-factor inspiration adaptive ant colony algorithm 3

Enhanced the security of path planning for mobile robots on ...

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3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC)

作者： Yao, Zheng Khalil, Fahad Khan Hubei Key Lab Automot Power Train & Elect Control Shiyan 442002 Peoples R China Hubei Univ Automot Technol Sch Elect & Informat Engn Shiyan Peoples R China

ISBN: (纸本)9798350352634;9798350352627

To further enhance the security of path planning for mobile robots on uneven terrain, we propose a new multi-factor inspiration adaptive ant colony algorithm (MIAACO). First, to improve the safety of the path, we introduce the factor of smoothness, and stability into the state transition probability to reconstruct the multi-factor heuristic function. And then, to improve the global searching capability, the information update strategy of the maximum and minimum the ant system is introduced based on pheromone optimization selection. Finally, to accelerate the convergence of the algorithm, we adopt the dynamic global adaptive strategy involving the pheromone heuristic factor, expected heuristic factor, and pheromone volatility coefficient. To reflect the performance of the proposed method effectively, experiments are carried out under two kinds of grid maps and compared with the other improved ant colony algorithm methods proposed by others. Experimental results show that the proposed method can enhance the efficiently global searching ability and convergence speed.

关键词： path planning ant colony algorithm multi-factor heuristic function global adaptive strategy

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Truss Assembly Sequence Planning Under Multiple Constraints Based on ant colony algorithm 19

Truss Assembly Sequence Planning Under Multiple Constraints ...

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IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)

作者： Liu, Yu Chen, Gang Huang, Zeyuan Hao, Zixuan Jia, Qingxuan Wang, Yifan Beijing Univ Posts & Telecommun Sch Automat Beijing Peoples R China

ISBN: (纸本)9798350360875;9798350360868

Aiming at the characteristics of multi-constraints and multi-optimization objectives in manipulator truss assembly tasks, a truss assembly sequence planning method based on ant colony algorithm was proposed. Firstly, the mathematical representation model of the manipulator truss assembly task is established to describe the truss properties and the task status. Secondly, a comprehensive evaluation index of assembly sequence considering assembly stability and energy consumption of the manipulator was established. Then, the search strategy of truss assembly sequence based on ant colony algorithm is designed. Finally, the feasibility and superiority of the proposed method are proved by simulation and comparison.

关键词： assembly sequence planning ant colony algorithm multi-constraint problem

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