Dynamic Topological Mapping with Biobotic Swarms

TL;DR

This paper introduces a robust method for dynamic environment mapping using a swarm of biobotic agents, combining local topological maps into a global map with topological data analysis, verified through simulations.

Contribution

It presents a novel topological mapping approach for biobotic swarms that uses encounter data and local interactions to build and merge environment maps.

Findings

Successfully constructs global topological maps from encounter data.

Effective in dynamic environments with local interactions.

Simulation results verify the algorithm's correctness.

Abstract

In this paper, we present an approach for dynamic exploration and mapping of unknown environments using a swarm of biobotic sensing agents, with a stochastic natural motion model and a leading agent (e.g., an unmanned aerial vehicle). The proposed robust mapping technique constructs a topological map of the environment using only encounter information from the swarm. A sliding window strategy is adopted in conjunction with a topological mapping strategy based on local interactions among the swarm in a coordinate-free fashion to obtain local maps of the environment. These maps are then merged into a global topological map which can be visualized using a graphical representation that integrates geometric as well as topological feature of the environment. Localized robust topological features are extracted using tools from topological data analysis. Simulation results have been presented to illustrate and verify the correctness of our dynamic mapping algorithm.