Aim at search precocity of particle swarm algorithm and slow convergence speed problem for ant colony algorithm, in the automatic guided vehicle path optimization a path optimization algorithm is proposed, which is fused by particle swarm algorithm and ant colony algorithm. Firstly, robot motion space model of the algorithm is created using link figure. After got fixed circulation rapid global, search to get more optimal path by means of improved fastest convergence ant system, then using a particle ants information communication method to update pheromone, finally, optimal path is drew. The simulation experiment shows that, even in the complex environment, this algorithm can also has the advantage of ant colony algorithm to optimize the result accurately and particle swarm algorithm local optimization accurately and rapidly, and a global security obstacle avoidance of optimal path is plot, the route is shorten 8% compare than the general ant colony algorithm.
Intelligent vehicle is a preferable way for dealing with transportation problems. In cross-country environments, no apparent roads could be found, instead boscage, bumps and hollows, scattered materials make it hard to choose suitable paths. Path planning is a combinational optimization issue to find out a short path from many candidates. A combinational optimization algorithm was adopted to solve the problem. A multi-scale grid environment modeling mechanism was designed to model the unordered and irregular environment by a standard grid map. Then an ant colony optimization based algorithm was presented for finding out the most suitable path from the map. The algorithm introduced a local optima breaking mechanism based on early convergence judging process to avoid falling into local optima. Three criteria were designed for the process, say criteria of single grid, of static colony and of dynamic convergence. Feasibility and effectiveness of the method were verified by experiments.
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