Global path planning approach based on ant colony optimization algorithm

Wen, Zhiqiang; Cai, Zixing

doi:10.1007/s11771-006-0018-4

Cited by 20 publications

(7 citation statements)

References 5 publications

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“…The principle of ant colony searching for food is shown in Figure 9. Wen et al [37] modified ACO to optimize the global path. When only the pheromone was used to search the optimum path, the ACO converges easily.…”

Section: Acomentioning

confidence: 99%

Path Planning for the Mobile Robot: A Review

2018

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Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.

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Section: Acomentioning

confidence: 99%

Path Planning for the Mobile Robot: A Review

2018

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“…The ant colony algorithm is an optimization algorithm for simulating behavior of ant colony foraging. 33…”

Section: Aco Of Cutting Trajectory Planningmentioning

confidence: 99%

“…The ant colony algorithm is an optimization algorithm for simulating behavior of ant colony foraging. 33 For roadheader, assuming the starting position g begin of the cutting is the ant nest and the target location g end is the food source. The cutting trajectory planning based on ACO actually makes use of the interaction and collaborating between the ants in the ant colony to avoid all the big dirt bands and finally finds an optimal path.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Cutting trajectory planning of sections with complex composition for roadheader

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

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“…Fig.1 shows that the ants can find the optimum route again quickly when a barrier suddenly appears in the path they pass [18]. In general, the ant moves from state x to state y with probability = .... (4) Where, is the amount of pheromone deposited for transition from state x to y, 0 ≤ α is a parameter to control the influence of , is the desirability of state transition xy (a priori knowledge, typically 1 / , where d is the distance) and b ≤ 1 is a parameter to control the influence of .…”

Section: Givingmentioning

confidence: 99%

Robot Path Planning using Swarm Intelligence: A Survey

Pal¹,

Sharma²

2013

IJCA

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The concept of swarm intelligence is based on the collective social behaviour of decentralized body, either natural or artificial like ant, fish, bird, bee etc. Swarm intelligence has gained very high priority among the researchers from different field like commerce, science and engineering. Multiple editions of swarm intelligence's techniques made it suitable for optimization problems. In this paper, we have present a review of 4 different algorithms based on swarm intelligence for finding the path by mobile robot. Path planning is an interesting problem in mobile robotics. It is about finding the shortest, collision free and smooth path by the robot form predefined starting position to fixed goal position in an environment with obstacles either moving or stationary. This problem is difficult to solve particularly in the case of dynamic environment where the optimal path needs to be rerouted in real time when a new obstacle come out.

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Global path planning approach based on ant colony optimization algorithm

Cited by 20 publications

References 5 publications

Path Planning for the Mobile Robot: A Review

Path Planning for the Mobile Robot: A Review

Cutting trajectory planning of sections with complex composition for roadheader

Robot Path Planning using Swarm Intelligence: A Survey

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