| Autonomous vehicle is able to solve traffic safety and traffic congestion problems,and have broad application prospects.Both universities and enterprises have intensified their research and development of unmanned driving.These developments have benefited from recent advances in sensing and computer technologies.As one of the core technologies of autonomous vehicles,path planning is the basic guarantee to realize the autonomous driving of vehicles.Therefore,the global path planning and local obstacle-avoiding trajectory planning of autonomous vehicles in dynamic environment are studied in this thesis,which has important practical significance.Ant colony optimization is the most famous bionic optimization method which has been widely used in global path planning.However,it has some drawbacks such as slow convergence speed and ease of falling into the local optimal solution.A new inspiration function and pheromone updating strategy are used to propose an enhanced ant colony optimization algorithm for global path planning of autonomous vehicles,which aims to improve the convergence speed and search ability.In addition,our ant colony algorithm integrates the non-uniform rational B-spline curve to smooth the path.The simulation experiments indicate that this algorithm is feasible and efficient in common scene and maze scenario.Compared with the traditional ant colony algorithm,the enhanced ant colony optimization algorithm has shorter path length and fewer iterations to converge to the optimal path.When an autonomous vehicle travels in a complex road environment,reliable and stable collision avoidance method is required.Therefore,a dynamic obstacle avoiding trajectory planning strategy of autonomous vehicles based on model predictive control is suggested.A two-degree-of-freedom vehicle dynamic model is adopted to describe the real vehicle dynamics characteristics,and to establish the vehicle state prediction model.Then,considering the current location information of moving obstacles,the objective function including the trajectory tracking cost,control cost and obstacle threat cost is solved under the condition of satisfying the vehicle state and control constraints.The increment of front wheel steering angle is used to make the autonomous vehicle avoid moving obstacles while tracking the trajectory.Finally,the performance of the dynamic obstacle avoidance trajectory planning system is validated under different moving obstacles conditions.The simulation results demonstrate that the dynamic obstacle-avoiding trajectory planning system has satisfactory local planning performance and satisfies the constraints to ensure the manipulation stability of vehicles.In order to further validate the enhanced ant colony optimization algorithm in this thesis,the construction of an unmanned driving platform is briefly discussed and the actual vehicle test is carried out.The real road environment is projected into a raster map by processing the point cloud data collected by Li DAR in the ROS environment.Then,real-time path planning of autonomous vehicles is achieved on the grid map by using the combination of enhanced ant colony optimization algorithm and the non-uniform rational B-spline curve.The performance of the enhanced ant colony optimization algorithm in practical application is verified according to the test results under three different driving scenarios. |
