Long-Lived Distributed Relative Localization of Robot Swarms
This addresses the challenge of relative localization for large swarms of simple robots, which is incremental as it builds on existing techniques like non-linear least squares and trilateration.
The paper tackles the problem of enabling mobile robots in a swarm to estimate the relative positions and orientations of nearby robots using only distance measurements, presenting two distributed algorithms with different trade-offs in computational complexity and coordination requirements, which were analyzed theoretically and validated through simulations.
This paper studies the problem of having mobile robots in a multi-robot system maintain an estimate of the relative position and relative orientation of near-by robots in the environment. This problem is studied in the context of large swarms of simple robots which are capable of measuring only the distance to near-by robots. We present two distributed localization algorithms with different trade-offs between their computational complexity and their coordination requirements. The first algorithm does not require the robots to coordinate their motion. It relies on a non-linear least squares based strategy to allow robots to compute the relative pose of near-by robots. The second algorithm borrows tools from distributed computing theory to coordinate which robots must remain stationary and which robots are allowed to move. This coordination allows the robots to use standard trilateration techniques to compute the relative pose of near-by robots. Both algorithms are analyzed theoretically and validated through simulations.