Collective transport via sequential caging

TL;DR

This paper introduces a decentralized swarm algorithm for collaboratively transporting arbitrarily shaped objects without prior shape knowledge, using sequential caging and task allocation to guide the object to a goal.

Contribution

A novel decentralized method for cooperative object transport that does not require shape pre-knowledge, utilizing sequential caging and dynamic task allocation.

Findings

Effective in simulation with up to 100 robots

Successful real-world experiments with KheperaIV robots

Accurate object transport along multiple paths

Abstract

We propose a decentralized algorithm to collaboratively transport arbitrarily shaped objects using a swarm of robots. Our approach starts with a task allocation phase that sequentially distributes locations around the object to be transported starting from a seed robot that makes first contact with the object. Our approach does not require previous knowledge of the shape of the object to ensure caging. To push the object to a goal location, we estimate the robots required to apply force on the object based on the angular difference between the target and the object. During transport, the robots follow a sequence of intermediate goal locations specifying the required pose of the object at that location. We evaluate our approach in a physics-based simulator with up to 100 robots, using three generic paths. Experiments using a group of KheperaIV robots demonstrate the effectiveness of our approach in a real setting. Keywords: Collaborative transport, Task Allocation, Caging, Robot Swarms