Abstract
To improve the path optimization effect and search efficiency of ant colony optimization (ACO), an improved ant colony algorithm is proposed. A collar path is generated based on the known environmental information to avoid the blindness search at early planning. The effect of the ending point and the turning point is introduced to improve the heuristic information for high search efficiency. The adaptive adjustment of the pheromone intensity value is introduced to optimize the pheromone updating strategy. A variety of control strategies for updating the parameters are given to balance the convergence and global search ability. Then, the improved obstacle avoidance strategies are proposed for dynamic obstacles of different shapes and motion states, which overcome the shortcomings of existing obstacle avoidance strategies. Compared with other improved algorithms in different simulation environments, the results show that the algorithm in this paper is more effective and robust in complicated and large environments. On the other hand, the comparison with other obstacle avoidance strategies in a dynamic environment shows that the strategies designed in this paper have higher path quality after local obstacle avoidance, lower requirements for sensor performance, and higher safety.
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