| With the development and progress of science and technology,autonomous navigation of mobile robots has been widely used in production,life,military and other fields.Path planning is a key technology for autonomous navigation of mobile robots.According to the grasp of map information,it can be divided into global path planning with known environmental information and local path planning with obtained environmental information based on sensors.In the practical application of path planning,with the development trend of generalized and complicated application scenarios,it is difficult to meet the optimality and real-time requirements of path planning by applying global path planning or local path planning alone.In view of the limitations of single path planning,this paper implements a hybrid path planning based on improved Q-RRT*(Quick Rapidly-Exploring Random Tree,Q-RRT*)algorithm and improved dynamic window algorithm,and verifies the feasibility and effectiveness of the proposed method in simulation environment and real scene.The main research content of this paper is divided into the following three aspects:(1)In terms of global path planning,this thesis proposes a global path planning based on regional sampling Q-RRT*,aiming at problems such as blind search,low efficiency of path planning and unstable path quality in Q-RRT* algorithm.Firstly,the Jump Point Serach(JPS)algorithm was used to search the initial path,and the elliptically connected sampling area and the jump point sampling area were constructed according to the initial path.Besides,the traditional random sampling method was changed,and the sampling points were generated by the combination of sequential sampling and random sampling in the area,so as to reduce the blindness of Q-RRT*path planning.Accelerate the convergence rate of the optimal path.Secondly,when the random tree is expanding new nodes,the node rejection strategy is introduced to refuse nodes with high heurizing cost to join the random tree and reduce the running memory of the algorithm.Finally,the feasibility and efficiency of the improved Q-RRT*algorithm are verified by MATLAB simulation experiments.(2)In terms of local path planning,a dynamic window algorithm based on dipole field model is proposed to solve the defect of the dynamic window algorithm in dynamic environment.Firstly,the local path planning detects the dynamic obstacle movement information through the sensor and determines whether it collides with the robot.Then,the dynamic dipole field model of the robot and the dynamic obstacle is established.The repulsive force evaluation function is introduced to evaluate the repulsive force of the dynamic obstacle to the simulated trajectory of the algorithm,forcing the robot to timely turn or slow down in advance to avoid the dynamic obstacle in the environment.Finally,the effectiveness of the improved dynamic window algorithm is verified by MATLAB simulation experiments.(3)Based on the improvement of global path planning and local path planning,this thesis takes global path nodes in order as subitems of local path planning,and implements a hybrid path planning based on improved Q-RRT* algorithm and improved dynamic window algorithm.In order to verify the feasibility of mixed path planning,multiple simulation experiments are designed on MATLAB software to verify the feasibility of mixed path planning.Simulation results show that the proposed hybrid path planning method can effectively solve the defects of single path planning and has strong adaptability in complex scenarios.Finally,the effectiveness of the proposed method and the superiority of obstacle avoidance ability are verified by setting up a real environment for path planning indoors and carrying out real machine experiments in static and dynamic environments respectively. |
PDF Full Text Request