Distributed robot positioning works with few signals and limited communication
Distributed Relative Localization Based on Ultra-WideBand and LiDAR for Multi-robot with Limited Communication
Robotics
Summary
Robots working together need to know where each other are, especially when GPS or strong communication links are not available. This paper shows how a team of similar-looking robots can figure out their relative positions using just on-board sensors like ultra-wideband radios to measure distances and lasers to detect nearby objects. The authors use graphs to connect these measurements and solve a matching puzzle to identify which robot is which, even when obstacles block views. Their method lets the robots find their places without relying on a central system, sharing only minimal information.
What this means in practice
- •For robot system developers: Build teams of robots that can navigate and coordinate their positions in environments without GPS or reliable communication using only onboard sensors.
- •For warehouse automation engineers: Enable fleets of identical robots to localize relative to each other accurately indoors, improving coordination without needing expensive infrastructure.
Authors
Zhiqiang Cao, Ran Liu, Billy Pik Lik Lau, Chau Yuen, U-Xuan Tan
Abstract
Relative localization is crucial for a multi-robot system to collaboratively perform tasks, such as exploration and formation. However, this is highly challenging for homogeneous robots with similar appearance in GPS-denied and communication-limited environments. In this paper, we propose a fully distributed relative position estimation approach for a team of robots based on onboard UWB and LiDAR sensors, in which LiDAR is utilized to obtain the position of anonymous objects in Line-of-Sight (LOS), and UWB is used for ranging between robots. We construct two graphs, namely UWB connection graph and LiDAR connection graph, to represent the spatial relationship among objects (robots and obstacles) based on UWB and LiDAR measurements. Identification and relative position estimation are formulated as a common subgraph matching problem. A falsely-matched robot identification approach is designed to recognize the falsely-matched results caused by obstacle blockage in LiDAR field of view. These robots are then localized by leveraging the well-matched robots and the UWB ranging measurements in the UWB connection graph. We conducted experiments to evaluate the performance of our approach. The results show that the proposed approach is capable of achieving satisfactory positioning accuracy for a team of robots in a distributed manner with only exchanging limited information.