Mobile Robot Path Planning Based on Ant Colony Optimization

Zhang, Bao Feng; Wang, Ya Chun; Zhang, Xiaoling

doi:10.4028/www.scientific.net/amm.687-691.706

Cited by 7 publications

(15 citation statements)

References 3 publications

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“…Figure 9a is the robot’s moving trajectory of the basic ant colony algorithm, and Figure 9b is that of the improved ant colony algorithm. According to the definition of path trajectory in [39], the two trajectories are continuous and uninterrupted, so they satisfy condition C0. Both trajectories have inflection points, which we usually call non-derivable points, and neither trajectory satisfies the C1 condition.…”

Section: Simulation Results and Analysesmentioning

confidence: 99%

“…Among these, ant colony optimization is based on the research of ants searching for food, which was proposed in the 1990s [31]. It is a heuristic search algorithm that has a successful application in solving the problems of path planning [3,20,32,33,34,35,36,37,38,39] and function optimization [40,41]. Ant colony optimization does well in positive feedback, parallelism, and robustness.…”

Section: Introductionmentioning

confidence: 99%

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3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Wang

Kan

Guo

et al. 2019

Sensors

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Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.

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Section: Simulation Results and Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Wang

Kan

Guo

et al. 2019

Sensors

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“…From the methodologies review on the solution approaches to solve the proposed problem, ALNS will be used in connection with the ant colony optimization. The ant colony optimization is used in the present study in combination with ALNS due to many researchers having proved that the ACO is good for path planning problems, scheduling problems, and assignment problems [34][35][36][37][38][39][40][41][42][43]. In the present study, the destroy and repair methods were used to determine and select a sequence of the farms which were assigned for pig production.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Large Neighborhood Search for a Production Planning Problem Arising in Pig Farming

Praseeratasang

Pitakaso

Sethanan

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

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This article aims to resolve a particular production planning and workforce assignment problem. Many production lines may have different production capacities while producing the same product. Each production line is composed of three production stages, and each stage requires different periods of times and numbers of workers. Moreover, the workers will have different skill levels which can affect the number of workers required for production line. The number of workers required in each farm also depends on the amount of pigs that it is producing. Production planning must fulfill all the demands and can only make use of the workers available. A production plan aims to generate maximal profit for the company. A mathematical model has been developed to solve the proposed problem, when the size of problem increases, the model is unable to resolve large issues within a reasonable timeframe. A metaheuristic method called adaptive large-scale neighborhood search (ALNS) has been developed to solve the case study. Eight destroy and four repair operators (including ant colony optimization based destroy and repair methods) have been presented. Moreover, three formulas which are used to make decisions for acceptance of the newly generated solution have been proposed. The present study tested 16 data sets, including the case study. From the computational results of the small size of test instances, ALNS should be able to find optimal solutions for all the random data sets in much less computational time compared to commercial optimization software. For medium and larger test instance sizes, the findings of the heuristics were 0.48% to 0.92% away from the upper bound and generated within 480–620 h, in comparison to the 1 h required for the proposed method. The Ant Colony Optimization-based destroy and repair method found solutions that were 0.98 to 1.03% better than the original ALNS.

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“…When the path is calculated, the list of path coordinates is returned to VS-VP program. According to the path coordinates, the totally multi-tasking assignment [9][10][11][12] for each agent and paths planned for each subgroup are achieved respectively.…”

Section: Multi-tasking Path Planning Simulation System Designmentioning

confidence: 99%