Flight Test Setup for Cooperative Swarm Navigation in Challenging Environments using UWB, GNSS, and Inertial Fusion

M. Haag, Mats Martens, Kevin Kotinkar, Jakob Dommaschk
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Abstract

This paper describes a basic framework for cognitive and collaborative navigation of small Unmanned Aerial Vehicles (sUAVs) with a focus on operation in challenging environments where GNSS performance may be deteriorated or even unavailable. The basic framework is based on a dynamic decision system where swarm members, a.k.a. agents, collect local sensor data and data from other agents in the swarm, to estimate the absolute and relative pose state of the swarm and its members and, hence, get better situational awareness to make decision that maintain safety but also satisfy the mission objectives. The paper discusses one possible way to integrate this swarm information using factor graphs and non-linear solvers. Simulation results will show the initial effectiveness of this method within the current architecture. The paper will, furthermore, describe the hardware and software architecture of the TU Berlin swarm test sUAVs and focus on the common GNSS, IMU, range radio board (SwarmEx) that forms the common core of the platforms' sensor payloads. Some initial results of the range radio performance will be included as well. Finally, the flight test environment will be described.
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使用超宽带、GNSS和惯性融合的具有挑战性环境下的协同群导航飞行试验设置
本文描述了小型无人机(suav)的认知和协作导航的基本框架,重点关注GNSS性能可能恶化甚至不可用的挑战性环境中的操作。其基本框架是基于一个动态决策系统,群体成员(agent)收集群体中的局部传感器数据和群体中其他agent的数据,估计群体及其成员的绝对姿态和相对姿态状态,从而获得更好的态势感知,从而做出既维护安全又满足任务目标的决策。本文讨论了一种利用因子图和非线性求解器来整合这一群信息的可能方法。仿真结果将显示该方法在当前体系结构中的初步有效性。此外,本文将描述TU柏林蜂群测试suav的硬件和软件架构,并重点介绍构成平台传感器有效载荷共同核心的通用GNSS, IMU,距离无线电板(SwarmEx)。一些距离无线电性能的初步结果也将包括在内。最后,对飞行试验环境进行了描述。
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