An improved ant colony algorithm for robot path planning

Liu, Jianhua; Yang, Jing; Liu, Huaping; Tian, Xingjun; Gao, Mingsheng

doi:10.1007/s00500-016-2161-7

Cited by 308 publications

(131 citation statements)

References 19 publications

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“…The power consumption of the central air conditioning fan is the most important part of the power consumption of the entire air conditioning unit, and it is also an important factor affecting the airside delivery efficiency of the central air conditioning unit . The fan of an air conditioning unit in a cool storage air conditioning system always maintains the power frequency operation, and the fan power consumption is maintained in a fixed range.…”

Section: Power Saving Optimization Methods Of Central Air Conditioningmentioning

confidence: 99%

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Jiao

2019

Int Trans Electr Energ Syst

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Summary Owing to the large power consumption and poor indoor comfort of central air conditioning, controlling the central air conditioning for cool storage and analyzing the power saving optimization of the central air conditioning operation have become necessary. Therefore, a method of power saving optimization analysis based on an improved ant colony air conditioning system is proposed to solve the problem of power consumption. According to the characteristics of cold storage operation regulation, the power consumption of chilled water pumps and air conditioning fans were used to establish a model, and the influence of cold storage operation regulation on central air conditioning operation power consumption was analyzed. On this basis, the improved ant colony is used to optimize the power saving path, according to the factors affecting the power consumption of central air conditioning operation above, and the optimal operation of air conditioning and storage regulation was analyzed, and the power saving treatment of central air conditioning operation was completed. The experimental results show that when the method is adopted, power consumption can be reduced by 67 560 kWh for the entire year, the total consumption of the optimal working point is approximately 0.384 kW, and the power saving effect is better with certain advantages.

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Section: Power Saving Optimization Methods Of Central Air Conditioningmentioning

confidence: 99%

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Jiao

2019

Int Trans Electr Energ Syst

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“…A hybridization method is proposed for the path planning and navigation of humanoid robots in a cluttered two‐dimensional environment by using regression technique and adaptive ACO with the applications in the domain of engineering . An improved ACO algorithm is proposed in Reference for path planning of mobile robots in the grid environment, in which the pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The fuzzy ant colony optimization method is proposed in Reference in order to minimize the iterative learning error of ACO algorithm using fuzzy control in the path planning problem.…”

Section: Aco and Ffmentioning

confidence: 99%

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan

Salgaonkar

2020

Engineering Reports

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Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.

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“…ACO has been modified multiple times in recent research to improve important parameters as discussed below. In [10], ACO was compared with ACO-PD (pheromone diffusion) and ACO-PDG (geometric optimization) which differ in their usage of artificial potential field (APF) and geometric optimization are added onto the global optimization (bionic) popular with ACO. Results from multiple experiments show that the risk of premature (local) convergence is avoided, ACO-PDG results in the least cost of the three and number of iterations are significantly reduced which improves the rate of convergence.…”

Section: Applications and Performance 31 Motion Planning In Mobile Rmentioning

confidence: 99%

Applications of Ant Colony Optimization in Control Systems, Robotics and Vision

Jahagirdar¹,

Dhuri²,

Bobade³

et al. 2019

IJCA

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Swarm Intelligence is driving research in multi-agent system based robotic and mobile control applications. A swarm optimization algorithm-Ant Colony Optimization (ACO) provides a stochastic 'shortest path' approach inspired by ant colonies to obtain global solution in an optimization problem. This paper reviews the impact of ACO in robotics, computer vision, control systems and sensor networks for autonomous systems. The performance of ACO as a metaheuristic or general optimization is discussed with respect to its consequences on parameters like energy efficiency, time for convergence, route selection, etc.

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An improved ant colony algorithm for robot path planning

Cited by 308 publications

References 19 publications

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Optimization analysis on the effect of operational power consumption for central air conditioning under cold storage regulation

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Applications of Ant Colony Optimization in Control Systems, Robotics and Vision

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