Simultaneous Configuration Formation and Information Collection by Modular Robotic Systems

TL;DR

This paper introduces a heuristic algorithm for modular robotic systems that simultaneously plans their configuration formation and maximizes information collection within energy constraints, with proven convergence and improved runtime.

Contribution

It presents a novel budget-limited heuristic search method for configuration and information collection, with theoretical convergence guarantees and better runtime performance.

Findings

The proposed algorithm converges within finite time.

It achieves higher information entropy along paths.

It outperforms auction-based algorithms in runtime.

Abstract

We consider the configuration formation problem in modular robotic systems where a set of singleton modules that are spatially distributed in an environment are required to assume appropriate positions so that they can configure into a new, user-specified target configuration, while simultaneously maximizing the amount of information collected while navigating from their initial to final positions. Each module has a limited energy budget to expend while moving from its initial to goal location. To solve this problem, we propose a budget-limited, heuristic search-based algorithm that finds a path that maximizes the entropy of the expected information along the path. We have analytically proved that our proposed approach converges within finite time. Experimental results show that our planning approach has lower run-time than an auction-based allocation algorithm for selecting modules' spots.