Application Research Of BiRRT-ACO Fusion Algorithm In Robot Path Planning

With the continuous innovation and development of related technologies in the field of robotics,a large variety of robots have emerged,the application scenarios of robots have become more and more complex,and the requirements for robotics have become higher and higher.As an indispensable part of robot technology research,the path planning technology has very important research and application value,and it has attracted the attention and favor of scholars at home and abroad.Aiming at the path planning problem in static complex environment,this paper proposes an BiRRT-ACO fusion algorithm to improve the performance of robot path planning.The algorithm takes full advantages of the fastness of BiRRT algorithm and the pheromone positive feedback mechanism of ACO algorithm.Multi-strategy optimization and fusion of the two algorithms is used to obtain a fast,stable and efficient new algorithm for robot path planning.The main works of this paper are as follows:(1)Improved Bi-RRT algorithm and improved ACO algorithm.Through in-depth research and analysis of Bi-RRT algorithm and ACO algorithm,it is found that the both algorithms have some shortcomings.Bi-RRT algorithm has poor stability and solution effect,while ACO algorithm has long search time and easy to be trapped in local optimization,resulting in poor path quality of final planning In order to improve this situation,this paper improves the directionality and smoothness of the Bi-RRT algorithm,optimizes the state transition mode and pheromone update mode of the ACO algorithm,and finally reduces the time of the search route of the two algorithms.and improves path smoothness and quality.(2)BiRRT-ACO fusion algorithm.Although the improved Bi-RRT algorithm and ACO algorithm have improved the effect of the planning path,the optimal path is still not obtained.Therefore,the both algorithms are comprehensively considered and analyzed,and an BiRRT-ACO fusion algorithm is proposed.This algorithm makes use of the advantage of the improved Bi-RRT algorithm to quickly obtain the suboptimal path to make up for the lack of pheromone in the early stage of the improved ACO algorithm.At the same time,the path trap node obtained by the former is used to update the state of the environment map,so that the latter can avoid the path trap in the map in advance when searching for the path.Through the fusion of the two improved algorithms,the effect of path planning is further improved.In the end,the fusion algorithm has higher efficiency in path planning,shorter path length,higher smoothness and better path quality.(3)Design a global path planner and apply the fusion algorithm in ROS for path planning.In order to simulate the path planning of the robot in the real environment,this paper made an in-depth understanding and research on move_base function package and nav_core function package,and finally found a method to apply the algorithm proposed in this paper to ROS--global path planner.Under the premise of observing the nav_core::BaseGlobalPlanner C++ interface defined in the nav_core function package,we made the BiRRT-ACO fusion algorithm as a global path planner of ROS,and then embed it as a plug-in into the global planning module of the move_base package.In the end,the plugin is registered in the ROS system,so that the custom global path planner can be called when simulating in ROS.Finally,in the MATLAB simulation platform,the three-dimensional physical simulation platform Gazebo and the three-dimensional visualization platform Rviz under the ROS,as well as the Bulldog smart car platform loaded with ROS system,the BiRRT-ACO fusion algorithm proposed in this paper is simulated and verified.By setting the starting position and the target position in different simulation environments,the quality and effect of the robot's path planning can be simulated.The results show that the proposed algorithm can efficiently plan a high-quality ideal path regardless of the above simulation environment.