SwarmGear：面向多机器人巡检的具有可重构领航无人机和虚拟阻抗链路的异构无人机集群
SwarmGear: Heterogeneous Swarm of Drones with Reconfigurable Leader Drone and Virtual Impedance Links for Multi-Robot Inspection
AI总结：
本文提出异构无人机集群巡检系统SwarmGear，采用具备柔性腿的领航无人机实现空地协同，并利用虚拟阻抗链路保持编队，实验表明其在复杂环境中具有高运动精度与低跟踪误差。
摘要：
由于缺乏电力供应以及无人机无法在不平整表面降落，无人机集群的持续监测仍然是一个具有挑战性的问题。包括地面和空中飞行器在内的异构集群可以支持更长时间的巡检，并搭载更多机载传感器。然而，它们的能力受限于轮式和腿式机器人在杂乱环境中的移动能力。\n在本文中，我们提出了一种用于自主巡检的新概念，我们称之为SwarmGear。SwarmGear利用异构集群，以领航-跟随编队的形式对环境进行勘察。领航无人机能够降落在崎岖地形上，并通过四条柔性机器腿在地形上行走，兼具空中和移动机器人的功能。为了在集群运动过程中保持编队，我们在领航无人机和跟随无人机之间开发了虚拟阻抗链路。\n我们通过实验评估了混合领航无人机地面运动的准确性。通过改变步态参数，找到了最优的步态配置。我们评估了两种类型的步态。实验显示出了较低的横向跟踪误差（平均2 cm，最大4.8 cm），并且领航无人机能够以190 mm的步长和3度的标准偏航偏差移动。我们考虑了四种类型的无人机编队。将最佳编队用于SwarmGear的实验，结果显示集群整体的横向跟踪误差较低（1型步态平均7.9 cm，2型步态平均5.1 cm）。\n所提出的系统通过允许集群中的所有智能体在空中和地面编队之间切换，以克服各种障碍物并在大面积区域执行任务，从而有望提高自主集群在杂乱和非结构化环境中的性能。
英文摘要：
The continuous monitoring by drone swarms remains a challenging problem due to the lack of power supply and the inability of drones to land on uneven surfaces. Heterogeneous swarms, including ground and aerial vehicles, can support longer inspections and carry a higher number of sensors on board. However, their capabilities are limited by the mobility of wheeled and legged robots in a cluttered environment. In this paper, we propose a novel concept for autonomous inspection that we call SwarmGear. SwarmGear utilizes a heterogeneous swarm that investigates the environment in a leader-follower formation. The leader drone is able to land on rough terrain and traverse it by four compliant robotic legs, possessing both the functionalities of an aerial and mobile robot. To preserve the formation of the swarm during its motion, virtual impedance links were developed between the leader and the follower drones. We evaluated experimentally the accuracy of the hybrid leader drone's ground locomotion. By changing the step parameters, the optimal step configuration was found. Two types of gaits were evaluated. The experiments revealed low crosstrack error (mean of 2 cm and max of 4.8 cm) and the ability of the leader drone to move with a 190 mm step length and a 3 degree standard yaw deviation. Four types of drone formations were considered. The best formation was used for experiments with SwarmGear, and it showed low overall crosstrack error for the swarm (mean 7.9 cm for the type 1 gait and 5.1 cm for the type 2 gait). The proposed system can potentially improve the performance of autonomous swarms in cluttered and unstructured environments by allowing all agents of the swarm to switch between aerial and ground formations to overcome various obstacles and perform missions over a large area.
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。
