Dynamic obstacle avoidance is a popular research topic for autonomous systems, such as micro aerial vehicles and service robots. Accurately evaluating the performance of dynamic obstacle avoidance methods necessitates the establishment of a metric to quantify the environment's difficulty, a crucial aspect that remains unexplored. In this paper, we propose four metrics to measure the difficulty of dynamic environments. These metrics aim to comprehensively capture the influence of obstacles' number, size, velocity, and other factors on the difficulty. We compare the proposed metrics with existing static environment difficulty metrics and validate them through over 1.5 million trials in a customized simulator. This simulator excludes the effects of perception and control errors and supports different motion and gaze planners for obstacle avoidance. The results indicate that the survivability metric outperforms and establishes a monotonic relationship between the success rate, with a Spearman's Rank Correlation Coefficient (SRCC) of over 0.9. Specifically, for every planner, lower survivability leads to a higher success rate. This metric not only facilitates fair and comprehensive benchmarking but also provides insights for refining collision avoidance methods, thereby furthering the evolution of autonomous systems in dynamic environments.

翻译：动态障碍物规避是自主系统（如微型飞行器和服务机器人）的一个热门研究课题。准确评估动态障碍物规避方法的性能，需要建立量化环境难度的指标，这是一个关键但尚未探索的方面。在本文中，我们提出了四个度量动态环境难度的指标。这些指标旨在全面捕捉障碍物数量、大小、速度及其他因素对难度的影响。我们将所提出的指标与现有的静态环境难度指标进行比较，并通过在定制模拟器中进行超过150万次试验加以验证。该模拟器排除了感知和控制误差的影响，并支持不同的运动规划和视线规划器以进行障碍物规避。结果表明，生存能力指标表现最优，且与成功率建立了单调关系，其斯皮尔曼秩相关系数（SRCC）超过0.9。具体而言，对于每个规划器，较低的生存能力导致较高的成功率。该指标不仅促进了公平且全面的基准测试，还为改进碰撞规避方法提供了见解，从而进一步推动了自主系统在动态环境中的演进。