多无人机协同动态规划巡检，智能避重高效覆盖3D物体表面。
Rolling Horizon Coverage Control with Collaborative Autonomous Agents
- 基于滚动时域的分布式模型预测控制，优化飞行与摄像动作。
- 通过光路传播预测未来可见区域，减少重复巡检覆盖。
- 将非线性可视性约束转为二元变量，适配混合整数优化框架。
本文提出一种覆盖控制器，使空中分布式自主智能体团队能够协同生成非短视的覆盖计划，在滚动有限时域内覆盖目标三维物体表面的特定点。该协同覆盖问题被建模为分布式模型预测控制问题，优化智能体的运动与相机控制输入，同时考虑智能体间约束以降低工作冗余。所提控制器引入基于光路传播技术的约束，用于预测智能体未来预期状态下的可见表面区域。本工作还展示了如何将复杂的非线性可视性评估约束转化为逻辑表达式，并作为二元约束嵌入混合整数优化框架中。该方法在建筑物巡检场景下通过仿真和实际应用验证，使用无人飞行器（UAV）完成对3D结构的高效覆盖任务。
原文摘要 · Abstract (English)
This work proposes a coverage controller that enables an aerial team of distributed autonomous agents to collaboratively generate non-myopic coverage plans over a rolling finite horizon, aiming to cover specific points on the surface area of a 3D object of interest. The collaborative coverage problem, formulated, as a distributed model predictive control problem, optimizes the agents' motion and camera control inputs, while considering inter-agent constraints aiming at reducing work redundancy. The proposed coverage controller integrates constraints based on light-path propagation techniques to predict the parts of the object's surface that are visible with regard to the agents' future anticipated states. This work also demonstrates how complex, non-linear visibility assessment constraints can be converted into logical expressions that are embedded as binary constraints into a mixed-integer optimization framework. The proposed approach has been demonstrated through simulations and practical applications for inspecting buildings with unmanned aerial vehicles (UAVs).
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