A minimal navigation solution is presented for swarms of tiny flying robots to explore unknown, GPS-denied environments. Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGBA), a minimal navigation solution that allows a swarm of tiny flying robots to autonomously explore an unknown environment and subsequently come back to the departure point. SGBA maximizes coverage by having robots travel in different directions away from the departure point. The robots navigate the environment and deal with static obstacles on the fly by means of visual odometry and wall-following behaviors. Moreover, they communicate with each other to avoid collisions and maximize search efficiency. To come back to the departure point, the robots perform a gradient search toward a home beacon. We studied the collective aspects of SGBA, demonstrating that it allows a group of 33-g commercial off-the-shelf quadrotors to successfully explore a real-world environment. The application potential is illustrated by a proof-of-concept search-and-rescue mission in which the robots captured images to find “victims” in an office environment. The developed algorithms generalize to other robot types and lay the basis for tackling other similarly complex missions with robot swarms in the future.

中文翻译：

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一群小型飞行机器人探索未知环境的最小导航解决方案

针对成群的微型飞行机器人，提出了一种最小的导航解决方案，以探索未知的，被GPS拒绝的环境。成群的微型飞行机器人具有探索未知的室内环境的巨大潜力。它们的体积小，可以在狭窄的空间中移动，而且重量轻，可以安全地在人类周围操作。到目前为止，由于缺乏适当的导航策略，这项任务一直遥不可及。没有外部基础设施意味着任何定位尝试都必须由机器人本身执行。最先进的解决方案，例如同时定位和地图绘制，仍然对资源要求很高。本文介绍了群体梯度错误算法（SGBA），最小的导航解决方案，它允许一群微型飞行机器人自主探索未知的环境，然后返回出发点。SGBA使机器人在离开出发点的不同方向上行驶，从而最大限度地扩大了覆盖范围。机器人通过视觉测距法和跟随墙壁的行为在环境中导航并处理飞行中的静态障碍物。而且，它们彼此通信以避免冲突并最大化搜索效率。为了返回出发点，机器人对家用信标进行了梯度搜索。我们研究了SGBA的集体方面，表明它使一组33 g的商用现成四旋翼飞行器能够成功地探索现实世界的环境。概念验证搜索和救援任务说明了应用潜力，其中机器人捕获图像以在办公室环境中找到“受害者”。所开发的算法可以推广到其他类型的机器人，并为将来用机器人群解决其他类似的复杂任务奠定基础。