Research On Three - Dimensional Space Path Planning Of Robot Based On Heuristic Search Method

With the rapid development of computer technology in recent years, the special robot has continue to emerge with the ability to move in three-dimensional space, The one of the most critical technology in robot intelligent control system is to solve three-dimensional space path planning problems.By studying on the current research and development of intelligent mobile robot path planning algorithm for three-dimensional space, and based on the requirements of state space modeling of path planning, proposed based on improved ant colony algorithm and the rapid expansion of the random tree algorithm based on heuristic search constraints, and applied to the robot path planning in three-dimensional space. The main work is as follows:First of all, systematic summarized the domestic mobile robot path planning research status and methods, analyzed their advantages and shortcomings, and laid the foundation for the research of this article.Secondly, the modeling of three-dimensional path planning environment, using the grid method to establish a working space model for robot path planning. Improved the basic Ant Colony algorithm to construct a three-dimensional terrain environment and improved ant colony algorithm for mobile robot path planning three-dimensional space. Improved algorithms for simulation, simulation results show that the improved ant colony algorithm optimal value path and the path planning time was all better than the basic ant colony algorithm, the simulation is to prove the feasibility of the algorithm.Finally, by studying the A*algorithm path planning methods, and to introduce the ideas of A*heuristic search to the rapid expansion of the random tree algorithm, we proposed the improved algorithm rapidly-explore random tree with constrains based on heuristic search algorithm, the algorithm improves the question of rapidly random tree extended way too average, no purpose, and generate real-time path of poor quality is not high inadequacies, without direct state space modeling, we can do path planning in state space. Improved algorithms for simulation, experimental results show that the improved algorithm rapidly random tree in the search speed, the quality of the generated path planning results are better than basic rapidly-exploring random tree algorithm.