Learning Environment Constraints in Collaborative Robotics: A Decentralized Leader-Follower Approach

TL;DR

This paper introduces a decentralized leader-follower control strategy for two robots collaboratively transporting objects in obstacle-rich environments without explicit communication, using local sensing and model predictive control to enhance success rates and efficiency.

Contribution

It presents a novel decentralized leader-follower approach with role switching and obstacle inference, improving collaborative transport in unknown environments without communication.

Findings

Achieves higher success rate than alternative strategies.

Completes tasks faster in obstacle-rich environments.

Effective obstacle inference from control discrepancies.

Abstract

In this paper, we propose a leader-follower hierarchical strategy for two robots collaboratively transporting an object in a partially known environment with obstacles. Both robots sense the local surrounding environment and react to obstacles in their proximity. We consider no explicit communication, so the local environment information and the control actions are not shared between the robots. At any given time step, the leader solves a model predictive control (MPC) problem with its known set of obstacles and plans a feasible trajectory to complete the task. The follower estimates the inputs of the leader and uses a policy to assist the leader while reacting to obstacles in its proximity. The leader infers obstacles in the follower's vicinity by using the difference between the predicted and the real-time estimated follower control action. A method to switch the leader-follower roles is used to improve the control performance in tight environments. The efficacy of our approach is demonstrated with detailed comparisons to two alternative strategies, where it achieves the highest success rate, while completing the task fastest. See the link www.dropbox.com/s/hexadigqkvspaeh/IROS_Video.mp4?dl=0 for a descriptive video of the algorithm.