Incremental graph search algorithms, such as D* Lite, reuse previous search efforts to speed up subsequent similar path planning tasks. These algorithms have demonstrated their efficiency in comparison with search from scratch, and have been leveraged in many applications such as navigation in unknown terrain. On the other hand, path planning typically involves optimizing multiple conflicting objectives simultaneously, such as travel risk, arrival time, etc. Multi-objective path planning is challenging as the number of "Pareto-optimal" solutions can grow exponentially with respect to the size of the graph, which makes it computationally burdensome to plan from scratch each time when similar planning tasks needs to be solved. This article presents a new multi-objective incremental search algorithm called Multi-Objective Path-Based D* Lite (MOPBD*) which reuses previous search efforts to speed up subsequent planning tasks while optimizing multiple objectives. Numerical results show that MOPBD* is more efficient than search from scratch and runs an order of magnitude faster than existing incremental method for multi-objective path planning.

中文翻译：

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基于多目标路径的 D* Lite

增量图搜索算法，例如 D* Lite，重用以前的搜索工作来加速后续类似的路径规划任务。与从头开始搜索相比，这些算法已经证明了它们的效率，并且已在许多应用程序中得到利用，例如在未知地形中进行导航。另一方面，路径规划通常涉及同时优化多个相互冲突的目标，例如旅行风险、到达时间等。多目标路径规划具有挑战性，因为“帕累托最优”解决方案的数量可以相对于规模呈指数增长的图形，这使得每次需要解决类似的规划任务时从头开始规划在计算上很繁重。本文介绍了一种名为 Multi-Objective Path-Based D* Lite (MOPBD*) 的新多目标增量搜索算法，该算法重用以前的搜索工作来加速后续规划任务，同时优化多个目标。数值结果表明 MOPBD* 比从头开始搜索更有效，并且运行速度比现有的多目标路径规划增量方法快一个数量级。