Path planning algorithms for intelligent agents aim to generate feasible paths for the agent, such that it can safely and efficiently reach the target point from the starting point without colliding with obstacles. Currently, path planning algorithms have been widely applied in various critical cyber-physical systems. Therefore, it is necessary to test the path planning algorithms before putting them into practice to evaluate whether their performance meet the requirements. However, the distribution patterns of threat obstacles in the task space, which serve as inputs to the path planning algorithm, can be various and complex. Moreover, executing each test case using the path planning algorithm incurs inevitable computational resources. To improve the testing efficiency of path planning algorithms, this paper adapts the concept of dynamic random testing into path planning algorithms and proposes the Dynamic Random Testing approach for Path Planning algorithms method (DRT-PP). Specifically, DRT-PP discretizes the task space into sub-regions and introduces a threat generation probability within each sub-region, thereby constructing the testing profile. This test profile is then taken as a strategy to generate test cases. Furthermore, DRT-PP dynamically adjusts the test profile during the testing process to gradually optimize it, and hence enhance the testing efficiency. Experiment results show that, compared to random testing and adaptive random testing, DRT-PP can not only ensure the diversity of the generated test suite, but also generate more test cases revealing potential performance failures.