Onboard Ranging-based Relative Localization and Stability for Lightweight Aerial Swarms

TL;DR

This letter presents an autonomous relative localization technique for lightweight aerial swarms without infrastructure by fusing ultra-wideband wireless distance measurements and the shared state information from neighbors, which is relevant not only for tiny drones but can be readily applied to many other resource-restricted robots.

Abstract

Lightweight aerial swarms have potential applications in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is efficient relative localization, which enables cooperation and collision avoidance. Computing the real-time position is challenging due to extreme resource constraints. This paper presents an autonomous relative localization technique for lightweight aerial swarms without infrastructure by fusing ultra-wideband wireless distance measurements and the shared state information (e.g., velocity, yaw rate, height) from neighbors. This is the first fully autonomous, tiny, fast, and accurate relative localization scheme implemented on a team of 13 lightweight (33 grams) and resource-constrained (168MHz MCU with 192 KB memory) aerial vehicles. The proposed resource-constrained swarm ranging protocol is scalable, and a surprising theoretical result is discovered: the unobservability poses no issues because the state drift leads to control actions that make the state observable again. By experiment, less than 0.2m position error is achieved at the frequency of 16Hz for as many as 13 drones. The code is open-sourced, and the proposed technique is relevant not only for tiny drones but can be readily applied to many other resource-restricted robots. Video and code can be found at \textnormal{\url{https://shushuai3.github.io/autonomous-swarm/}}.

Figures (15)

• Figure 1: The scheme of the multi-robot system and all onboard sensors. Specifically, each robot has an inertial measurement unit (IMU), an optical flow sensor, and a downward-pointing laser sensor for obtaining acceleration, rotation rates, velocities, and height. This information is fused by an onboard filter to get the body-frame velocity, yaw rate, and height, which is further rotated to get the horizontal-frame velocity, yaw rate and height. By UWB wireless ranging and communication, other robots' state information is received and combined, the relative positions and yaw are estimated.
• Figure 2: The diagram of the relative kinematic model, composed by two robots shown in a horizontal plane for simplicity (as they can be at different heights with the relative height $h_{ij}$). 3D purple axes represent the body frame of each robot, while the 3D blue axes denote the horizontal frame with a vertical z-axis. The relative 2D position $[x_{ij}, y_{ij}]$ and relative yaw $\psi_{ij}$ of $j^{\mathrm{th}}$ robot is shown in $i^{\mathrm{th}}$ robot horizontal frame in this figure.
• Figure 3: An illustration of the swarm ranging protocol for the three-side example.
• Figure 4: The proposed ranging message that supports relative localization.
• Figure 5: Message exchanging between $A$ and $Y$ for performing continuous ranging.

Theorems & Definitions (6)

• Theorem 1
• proof
• Theorem 2
• proof
• Theorem 3
• proof