Distributed State Estimation Using Intermittently Connected Robot Networks
This addresses the problem of efficient state estimation for multi-robot systems with limited communication, offering a novel approach that relaxes connectivity assumptions, though it is incremental in advancing distributed robotics.
The paper tackles distributed state estimation with multi-robot systems under intermittent communication, proposing a framework that combines communication schedules and motion planning to minimize estimation uncertainty, with simulations showing significant accuracy improvements over methods requiring constant network connectivity.
This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, and (ii) sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first distributed state estimation framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.