The Effect of Communication Topology on Scalar Field Estimation by Networked Robotic Swarms
This work addresses the challenge of efficient environmental monitoring for applications like oceanography, though it is incremental in focusing on topology comparisons within an existing framework.
The paper tackles the problem of reconstructing a 2D scalar field using a robotic swarm with local communication, comparing chain and grid topologies, and finds that grid networks generally offer better estimation capability and robustness, as validated with simulated and ocean salinity data.
This paper studies the problem of reconstructing a two-dimensional scalar field using a swarm of networked robots with local communication capabilities. We consider the communication network of the robots to form either a chain or a grid topology. We formulate the reconstruction problem as an optimization problem that is constrained by first-order linear dynamics on a large, interconnected system. To solve this problem, we employ an optimization-based scheme that uses a gradient-based method with an analytical computation of the gradient. In addition, we derive bounds on the trace of observability Gramian of the system, which helps us to quantify and compare the estimation capability of chain and grid networks. A comparison based on a performance measure related to the H2 norm of the system is also used to study robustness of the network topologies. Our resultsare validated using both simulated scalar fields and actual ocean salinity data.