Path planning of autonomous mobile robots in a real-world environment presents several challenges which are usually not raised in other areas. The real world is inherently complex, uncertain and dynamic. Therefore, accurate models of path planning are difficult to obtain and quickly become outdated. Anytime planners are ideal for this type of problem as they can find an initial solution very quickly and then improve it as time allows. This paper proposes a new anytime incremental search algorithm named improved Anytime Dynamic A*(iADA*). The algorithm is based on the currently popular anytime heuristic search algorithm, which is Anytime Dynamic A*(ADA*). The iADA* algorithm improves the calculation of the path lengths and decreases the calculating frequency of the path throughout the search, making it significantly faster. The algorithm is designed to provide an efficient solution to a complex, dynamic search environment when the locally changes affected. Our study shows that the two-dimensional path-planning iADA* experiments were between 2.0 to 3.7 times faster than ADA*, both in partially known and fully unknown dynamic environments. Additionally, in this paper shows the experiment results of the comparison with other four existing algorithms based on computing time and path lengths. iADA* was an average 2.57 times reduced on the computational time for the environment which locally changes effected. For the path length is little increase, but it is not the worst case. According to the experiments, the more the environmental problems and complexity increases, the more iADA* provides a rapid in-search time and total time to obtain the final solution.

现实环境中的自主移动机器人的路径规划提出了一些其他方面通常不会提出的挑战。现实世界具有内在的复杂性，不确定性和动态性。因此，很难获得准确的路径规划模型，并且很快就会过时。随时计划人员都非常适合此类问题，因为他们可以快速找到初始解决方案，然后在时间允许的情况下对其进行改进。本文提出了一种新的随时间增量搜索算法，称为改进的随时间动态A *（iADA *）。该算法基于当前流行的随时启发式搜索算法，即随时动态A *（ADA *）。iADA *算法改善了路径长度的计算，并降低了整个搜索过程中路径的计算频率，使其速度大大提高。该算法旨在为受影响的本地更改提供有效的解决方案，以解决复杂的动态搜索环境。我们的研究表明，在部分已知和完全未知的动态环境中，二维路径规划iADA *实验的速度是ADA *的2.0到3.7倍。另外，本文显示了基于计算时间和路径长度与其他四种现有算法进行比较的实验结果。对于局部影响的环境，iADA *平均减少了2.57倍的计算时间。对于路径长度几乎没有增加，但这不是最坏的情况。根据实验，环境问题和复杂性增加得越多，iADA *提供的搜索时间和总时间就越多，从而最终获得了最终解决方案。