Robot Path Planning Studies Based On Improved Ant Colony Algorithm

Intelligent mobile robot is an intelligent body, utilizing multi-disciplinary research, on behalf of the achievements of electric, mechanics and artificial intelligence.The ant colony algorithm is a new kind of used to solve combinatorial optimization function optimization problem heuristic bionic algorithm, widely used in mobile robot path planning.This paper first introduces the background, development of the mobile robot, and the typical mobile robot path planning algorithm, decision-making systems, machine learning, and motion control, followed by the ant colony algorithm-The new generation of bionic algorithm principle introduced and the impact of the parameter change on the ant colony algorithm performance is studied.Through the introduction of two typical improved ant colony algorithm, learning from the rapid expansion of tree characteristics of bidirectional search, The thesis proposes an improved bidirectional ant colony algorithm (BACO),and simulations show that improvements larger ant colony algorithm to enhance the performance of the ant colony algorithm. For the case of the path of the same length, due to the existence of the deceleration, the curve path of the energy-consuming to be the problem of the cost of energy is greater than the straight path, through the introduction of fuzzy algorithm ant colony algorithm improvements proposed minimum energy ant colony algorithm (MEACO). Simulation results show that the improved ant colony algorithm can effectively reduce the energy consumption in the process of moving.