基于改进避障策略和双优化蚁群算法的机器人路径规划

针对传统蚁群算法在移动机器人路径规划中存在的收敛速度慢、收敛路径质量低、死锁以及动态避障能力差的问题，本文提出基于改进避障策略和双优化蚁群算法（Double optimization ant colony algorithm，DOACO）的路径规划方法。首先，设计新的概率转移函数并对函数中的各分量权重进行自适应调整，以优化算法的收敛速度；然后，利用碰撞检测策略对路径进行再优化，进一步提高算法的性能；最后，针对常规避障策略避障能力差、实时性不足等问题，提出避障行为与局部路径重规划相结合的避障策略。实验结果表明，DOACO算法相对于传统的蚁群算法，不仅能规划出更优的路径，收敛速度也更快，而且新的避障策略也可以有效地应对多种碰撞情况。

Path Planning of Mobile Robot Based on Improved Obstacle Avoidance Strategy and Double Optimization Ant Colony Algorithm

In order to solve the problems of traditional ant colony algorithm in mobile robot path planning, such as low convergence speed, low quality of convergence path, deadlock, and poor dynamic obstacle avoidance capability, a path planning method based on improved obstacle avoidance strategy and double optimization ant colony algorithm (DOACO) was proposed. Firstly, a probability transfer method was designed. The pseudo-random probability adjustment factor was introduced to adjust the selection degree of high-quality path points in the probability transfer function. It avoided the problem that the probability of selecting high-quality path points in traditional ant colony algorithm was too low. Secondly, the weight of each component of the probability transfer function was adaptively adjusted to optimize the convergence speed of the algorithm. Then the elite saving strategy was introduced to prevent the data falling back of the algorithm. The elite saving strategy can also improve the quality of the path. In order to further improve the quality of path, a path optimization strategy was proposed based on key path points. This strategy tried to generate better path segments by looking for key path points. Finally, a obstacle avoidance strategy based on obstacle avoidance behavior and local path replanning was proposed to solve the problems of poor obstacle avoidance ability and lack of real-time performance. The experimental results showed that compared with the traditional ant colony algorithm, DOACO algorithm can not only plan a better path, but also can have a faster convergence speed, and the obstacle avoidance strategy can effectively deal with a variety of collision situations.