When planning the path of a non-urbanized road, the default ant colony optimization (ACO) algorithm does not consider complex road state function such as uneven surface, road attachment coefficient, and vehicle turning angle limit. Based on the actual situation of roads and vehicles, a pavement state function that considers uneven areas such as road bumps and pavement attachment is proposed to improve the description of path length. Then, a heuristic function based on the A* algorithm and an improved mechanism for the initialization of pheromone distribution is proposed, which changes the blindness of ant colony search, accelerates the convergence of the ACO, and improves the search efficiency. The global search capability of the algorithm is enhanced by improving the path selection strategy and path transition probability function. The pheromone updating method is improved by using the MAX-MIN Ant System, which increases the algorithm diversity and avoids local optima. Further, using the pruning algorithm to reduce the number of paths significantly increases the convergence speed. Simulation results show that the improved ACO algorithm has better convergence speed and global search ability. Combining road state processing with vehicle corner processing can effectively improve the safety, adaptability, and reliability of autonomous vehicles. And the global optimal path planning of unmanned vehicles on complex roads can also be realized.

In special locations (scenes) such as campuses and closed parks, small unmanned vehicles have gained more attention and application. Autonomous navigation is one of the key technologies of low-speed small unmanned vehicles. It has become a research hotspot, but there are still many problems, such as perception sensitivity, navigation, and positioning accuracy, motion planning accuracy, and tracking control accuracy. In order to sort out the research status of the key technologies of autonomous navigation for small unmanned vehicles more clearly, this paper firstly reviews the key technologies of autonomous navigation and presents an analysis and summary. Finally, future research trends of small unmanned vehicles with low speed are given.