Distributed State Estimation Using Intermittently Connected Robot Networks

Abstract

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) samplingbased 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.Index Terms-Distributed state estimation, intermittent connectivity, sampling-based planning, multi-robot networks.

Algebraic Connectivity Control in Distributed Networks by Using Multiple Communication Channels

“…A more accurate probabilistic model that predicts signal quality during agent’s motion is provided in [ 45 ]. An intermittent connectivity framework for a multi-robot system is proposed in [ 46 ]. The authors consider a team of networked robots with limited communication capabilities, where connectivity between robots can be lost under some strict conditions.…”

Multi-UAV Surveillance With Minimum Information Idleness and Latency Constraints

“…In contrast to related work, we consider a cooperative data transport by multiple UAVs to a single BS by investigating which UAVs should meet when and where. Other work focuses on recurrent connectivity without explicitly minimizing data latency [22], [11], [16], [18]. All these works have in common that which robots should meet is determined in advance.…”

Model-Based Active Source Identification in Complex Environments

“…Optimal measurement collection has been long studied in the robotics literature to solve state estimation problems. Given a probabilistic model of the measurement noise, informationtheoretic indices, e.g., covariance [19], Fisher Information Matrix (FIM) [20], different notions of entropy [21], mutual information [22], [23], and information divergence [24], have been used for general robotic planning. For example, given an information distribution, the authors in [20] propose an optimal controller to navigate the robot through an ergodic path.…”

“…. , c m (xm)); 2: for m =m to m max do 3: Solve the SI problem (17) with y m to get p m ; 4: Compute S m = S(p m ) and the design matrix X m = X(x m ) according to equations (19) and (21); 5: Compute the constant matrix F m = S T m X T m X m S m ; 6: Given S m and F m , solve the planning problem (22) for x m+1 utilizing the SSDP approach of Algorithm 7;…”