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arXiv 2401.02899cs.ROcs.MAmath.OC
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多无人机在ergodic势场引导下的预测高度和速度控制

Model predictive altitude and velocity control in ergodic potential field directed multi-UAV search

Faculty of Engineering, University of Rijeka(里耶卡大学工程学院)

Luka Lanča, Karlo Jakac, Stefan Ivić

AI总结:

本研究提出一种结合二维ergodic搜索与模型预测控制的多无人机高度和速度控制方法,以提高复杂地形中的搜索效率。

摘要:

本研究针对在地形多样且海拔变化显著的区域执行多无人机巡检任务所面临的挑战。所提出的方法将先进的二维ergodic搜索技术与无人机高度和速度的模型预测控制相结合。在预计的无人机地面路线中优化高度和速度,考虑了多个目标和约束条件。这产生了适应地形以及无人机运动和感知特性的飞行方案。所提出的无人机运动控制策略通过真实搜索任务和实际地形模型的仿真进行评估。结果表明,模型预测的高度和速度控制与二维势场引导的ergodic搜索成功结合。调整无人机高度至接近理想水平有助于利用感知范围,从而提高搜索效率。此外,该控制算法具备实时计算能力,鼓励其在现实场景中的实际应用。

英文摘要:

This research addresses the challenge of executing multi-UAV survey missions over diverse terrains characterized by varying elevations. The approach integrates advanced two-dimensional ergodic search technique with model predictive control of UAV altitude and velocity. Optimization of altitude and velocity is performed along anticipated UAV ground routes, considering multiple objectives and constraints. This yields a flight regimen tailored to the terrain, as well as the motion and sensing characteristics of the UAVs. The proposed UAV motion control strategy is assessed through simulations of realistic search missions and actual terrain models. Results demonstrate the successful integration of model predictive altitude and velocity control with a two-dimensional potential field-guided ergodic search. Adjusting UAV altitudes to near-ideal levels facilitates the utilization of sensing ranges, thereby enhancing the effectiveness of the search. Furthermore, the control algorithm is capable of real-time computation, encouraging its practical application in real-world scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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