Collective transport via sequential caging

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


page 1

page 2

page 3

page 4


Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots

Despite the potential benefits of collaborative robots, effective manipu...

Zero-Shot Object Searching Using Large-scale Object Relationship Prior

Home-assistant robots have been a long-standing research topic, and one ...

Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport

We consider task allocation for multi-object transport using a multi-rob...

Decentralized Multi-Agent Reinforcement Learning with Global State Prediction

Deep reinforcement learning (DRL) has seen remarkable success in the con...

Fragile object transportation by a multi-robot system in an unknown environment using a semi-decentralized control approach

In this paper, we introduce a semi-decentralized control technique for a...

A Study of Reinforcement Learning Algorithms for Aggregates of Minimalistic Robots

The aim of this paper is to study how to apply deep reinforcement learni...

Communication-free Cohesive Flexible-Object Transport using Decentralized Robot Networks

Decentralized network theories focus on achieving consensus and in speed...

Please sign up or login with your details

Forgot password? Click here to reset