Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a moving vehicle to reach its goal while navigating around obstacles within its environment. This problem becomes increasingly challenging as the number of obstacles in the environment rises. We propose an iterative active-inactive obstacle approach, which involves identifying a subset of the obstacles as "active", that considers solely the effect of the "active" obstacles on the path of the moving vehicle. The remaining obstacles are considered "inactive" and are not considered in the path planning process. The obstacles are classified as 'active' on the basis of previous findings derived from prior iterations. This approach allows for a more efficient calculation of the optimal path by reducing the number of obstacles that need to be considered. The effectiveness of the proposed method is demonstrated with two different dynamic models using the various number of obstacles. The results show that the proposed method is able to find the optimal path in a timely manner, while also being able to handle a large number of obstacles in the environment and the constraints on the motion of the object.

翻译：时间最优避障是机器人学和自动驾驶等多个领域中普遍存在的问题，其任务是在有障碍物的环境中为移动载体规划一条到达目标的路径。随着环境中障碍物数量的增加，该问题的求解难度显著提升。我们提出了一种迭代式活跃-非活跃障碍物处理方法，通过识别一组"活跃"障碍物子集，仅考虑这些"活跃"障碍物对移动载体路径的影响，而将剩余障碍物视为"非活跃"并排除在路径规划过程之外。障碍物根据先前迭代的求解结果被分类为"活跃"。该方法通过减少需要处理的障碍物数量，实现了最优路径的高效计算。采用两种不同动力学模型和多种障碍物数量的实验验证了所提方法的有效性。结果表明，该方法不仅能够及时找到最优路径，还能有效处理环境中大量障碍物及物体运动约束。