[论文解读] Autonomous Reactive Mission Scheduling and Task-Path Planning Architecture for Autonomous Underwater Vehicle

Somaiyeh Mahmoud Zadeh|arXiv (Cornell University)|Jun 13, 2017

Underwater Vehicles and Communication Systems参考文献 4被引用 5

一句话总结

本文提出了一种新型的自主水下航行器（AUV）反应式任务调度与路径规划架构，可在动态、不确定的条件下实现实时决策。通过将分层控制框架与顶层任务优先级划分及自下而上的局部运动规划相结合，该系统能够自主管理资源约束，适应如海流等环境扰动，并在复杂、航路点映射的操作区域中确保任务按时完成。

ABSTRACT

An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a limited time interval. Since existing AUVs have limited battery capacity and restricted endurance, they should autonomously manage mission time and the resources to perform effective persistent deployment in longer missions. Task assignment requires making decisions subject to resource constraints, while tasks are assigned with costs and/or values that are budgeted in advance. Tasks are distributed in a particular operation zone and mapped by a waypoint covered network. Thus, design an efficient routing-task priority assign framework considering vehicle's availabilities and properties is essential for increasing mission productivity and on-time mission completion. This depends strongly on the order and priority of the tasks that are located between node-like waypoints in an operation network. On the other hand, autonomous operation of AUVs in an unfamiliar dynamic underwater and performing quick response to sudden environmental changes is a complicated process. Water current instabilities can deflect the vehicle to an undesired direction and perturb AUVs safety. The vehicle's robustness to strong environmental variations is extremely crucial for its safe and optimum operations in an uncertain and dynamic environment. To this end, the AUV needs to have a general overview of the environment in top level to perform an autonomous action selection (task selection) and a lower level local motion planner to operate successfully in dealing with continuously changing situations. This research deals with developing a novel reactive control architecture to provide a higher level of decision autonomy for the AUV operation that enables a single vehicle to accomplish multiple tasks in a single mission in the face of periodic disturbances in a turbulent and highly uncertain environment.

研究动机与目标

• 解决在电池和资源有限的约束下，实现AUV持续、长时间运行的挑战。
• 开发一种动态决策框架，根据成本、价值和车辆可用性分配任务优先级。
• 确保对周期性环境扰动（如海流不稳定性）的鲁棒性。
• 将高层任务调度与低层局部运动规划集成，以实现实时适应性。
• 在任务执行过程中，对突发环境变化实现自主、即时的重规划。

提出的方法

• 提出一种分层控制架构，包含顶层决策模块，用于任务调度与优先级划分。
• 使用航路点覆盖的网络对空间进行任务映射，定义操作区域以实现结构化规划。
• 采用反应式控制机制，持续监控环境条件和车辆状态。
• 集成局部运动规划器，以在动态扰动下实现实时避障和轨迹跟踪。
• 应用成本-价值预算机制，根据任务目标和资源可用性对任务进行优先级排序。
• 通过动态调整任务序列和路径，实现实时在线重规划，以响应环境变化和车辆约束。

实验结果

研究问题

• RQ1 AUV如何在任务执行过程中，针对动态环境变化自主地进行任务优先级排序与重调度？
• RQ2 何种架构框架能够实现实时适应周期性扰动（如海流）的同时保持任务目标？
• RQ3 在复杂水下环境中，如何在严格的资源与时间约束下优化任务-路径规划？
• RQ4 在不确定、高度动态的水下条件下，通过何种机制可确保鲁棒性与安全性？
• RQ5 单个AUV如何实现持久任务执行，同时保持高生产率和任务按时完成？

主要发现

• 所提出的架构可在动态且不确定的水下条件下实现自主、反应式的任务调度与路径规划。
• 系统在面对周期性环境扰动时，仍能有效维持任务生产力并确保任务按时完成。
• 高层任务优先级划分与低层运动控制的集成，确保了实时操作中的鲁棒性与适应性。
• 航路点映射的网络结构提升了复杂任务区域中的空间组织与规划效率。
• 该架构通过动态管理资源约束并响应变化条件实现重规划，支持持久部署。
• 该框架对海流不稳定性表现出有效适应能力，减少了轨迹偏差，提升了任务安全性与可靠性。