Research on global path planning of unmanned vehicles based on improved ant colony algorithm in the complex road environment

Abstract

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.

Path-Planning Strategy: Adaptive Ant Colony Optimization Combined with an Enhanced Dynamic Window Approach

“…The raster method is a widely employed technique for modeling environmental maps in path planning. It is adaptable to obstacles and can significantly reduce the complexity of modeling the environment [22]. It uses a combination of the right-angled coordinate system and sequence number for grid identification, as illustrated in Figure 1a.…”

Improved ant colony algorithm based on artificial gravity field for adaptive dynamic path planning

“…Bai Jianlong and others [12] addressed the problem of the enhanced ACO easily falling into local optimal solutions by proposing an AS-N algorithm that fully utilizes the negative feedback mechanism, achieving improved results in complex map environments. Xiaowei Li and others [13] proposed an improved ACO applied to non-urbanized roads, improving the heuristic function and pheromone distribution initialization mechanism based on the A* algorithm, and applied the pruning method to global path planning results.…”

Path planning for storage robots using the RRT algorithm

“…These constraints may include collision avoidance, staying within the boundaries of the search space, and others. If a randomly generated node conflicts with these constraints, it is discarded to ensure that the generated path is both valid and safe [9]. Collision detection typically involves comparing the robot's trajectory with obstacles in the environment to ensure the path does not intersect with obstacles.…”