Multitask allocation and trajectory planning for multiple unmanned aerial vehicles (UAVs) have been extensively used in various real-world applications. This article presents a framework of multi-UAV multitask allocation and trajectory planning with collision avoidance. The scenario of interest is one where multiple UAVs are launched in order to investigate selected targets in a massive wildfire disaster relief terrain. Initially, one UAV is launched to search wildfire locations and wildfire lines by a developed informative path planning algorithm. An informative exploratory search mechanism is developed that provides the exploration trajectories to precisely locate the wildfire positions in the wildfire environments. Afterward, with the investigated environmental information including GPS coordinates of wildfire positions and distribution as targets, UAVs are deployed to multiple target positions. In order to perform effective collision avoidance, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. Concurrent multitask allocation, trajectory planning, and collision avoidance are successfully carried out with unequal numbers of UAVs and targets. The proposed framework has been validated by simulation studies and comparative analyzes.

中文翻译：

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多空中机器人空间错位碰撞避免的多任务分配框架


多架无人机（UAV）的多任务分配和轨迹规划已广泛应用于各种实际应用中。本文提出了一个多无人机多任务分配和避免碰撞的轨迹规划框架。令人感兴趣的场景是发射多架无人机以调查大规模野火救灾地区的选定目标。最初，发射一架无人机，通过开发的信息丰富的路径规划算法来搜索野火位置和野火线。开发了一种信息丰富的探索性搜索机制，提供探索轨迹以精确定位野火环境中的野火位置。随后，以调查到的山火位置GPS坐标、分布等环境信息为目标，将无人机部署到多个目标位置。为了有效避免碰撞，通过为不同高度的无人机引入额外的维度来开发空间错位方案，而无人机则使用所提出的速度剖面范例来避免相同高度的碰撞。在无人机和目标数量不等的情况下，成功地进行了并发多任务分配、轨迹规划和避免碰撞。所提出的框架已通过模拟研究和比较分析得到验证。