DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems

Abstract

An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g 2 o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations.The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.

HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With Open-Source ROS Support

“…The HeRoSwarm robot has a huge potential in swarm robotics and related applications, as its functionality exposed in the robot hardware and software design gives the user flexibility to implement any desired multi-robot and swarm intelligence algorithms (e.g., [29]- [31]). While the HeRoSwarm has met its primary design goals, it continues to undergo improvements to expose more features and additional hardware and software functionalities (compatibility with ROS2 for instance).…”