2009 International Conference on Information Technology and Computer Science 2009
DOI: 10.1109/itcs.2009.177
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Abstract: This paper addressed a robot path planning algorithm based on improved ant colony optimization. The ant colony algorithm is used for a global path planning in robot rescue. A target attracting function is introduced to guide the searching process which can improve the search quality of ant colony algorithm in the complex and dynamic environment. The affectivity of proposed algorithm is verified in a standard test bed, RoboCupRescue simulation system.

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Cited by 4 publications
(4 citation statements)
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References 4 publications
(7 reference statements)
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“…Therefore, the sensors can detect the change of environmental information at any time, and the change has no influence on obstacle avoidance path planning. From Figure 2, the rolling window view is the dashed circle including the detected effective information of nine grids (g=13, 14,15,21,22,23,29,30,31).…”
Section: Problem Formulationmentioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, the sensors can detect the change of environmental information at any time, and the change has no influence on obstacle avoidance path planning. From Figure 2, the rolling window view is the dashed circle including the detected effective information of nine grids (g=13, 14,15,21,22,23,29,30,31).…”
Section: Problem Formulationmentioning
confidence: 99%
“…To solve the problem of path planning, some modern intelligent path planning algorithms, such as genetic algorithm (GA) [5][6][7], simulated annealing (SA) [8,9], neural network (NN) [10][11][12], particle swarm optimization (PSO) [13,14], and ant colony optimization (ACO) [15][16][17][18], are adopted. Among those approaches, the ACO is the only meta-heuristic approach inspired by the behaviour of the biological ants in real world.…”
Section: Introductionmentioning
confidence: 99%
“…In [28], Zhang et al proposed an improved ACO algorithm. The key differences with the traditional ACO algorithm are (1) the definition of an objective attraction function in the transition rule probability which is based on the attraction of the goal position.…”
Section: Related Workmentioning
confidence: 99%
“…Aiming at the rescue problem in mines, Tian et al developed a modified neural network to expand the Kalman filter and realized a method of robot path planning based on multisensor fusion [20]. Zhang et al employed a modified ant colony algorithm (ACO) to the problem of rescue path planning [21]. Basilico and Amigoni introduced a strategy based on a multicriterion decision to solve the problem of robot path planning [22].…”
Section: Related Workmentioning
confidence: 99%