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Collaborative Multi-Robot Transportation in Obstacle-Cluttered Environments via Implicit Communication.

Charalampos P Bechlioulis1, Kostas J Kyriakopoulos1

  • 1Mechanical Engineering, National Technical University of Athens, Athens, Greece.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary

This study enables multiple robots to transport objects in cluttered spaces using implicit communication. Robots coordinate without direct data exchange, enhancing robustness and reducing bandwidth for complex tasks.

Keywords:
cooperative manipulationimplicit communicationinteraction forcesobstacle avoidanceprescribed performance estimator

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Cooperative object transportation in constrained workspaces presents significant challenges.
  • Existing methods often require explicit communication, limiting scalability and robustness.
  • Decentralized control architectures are crucial for multi-robot systems.

Purpose of the Study:

  • To develop a decentralized leader-follower framework for cooperative object transportation using implicit communication.
  • To address challenges posed by static obstacles and parametric uncertainty in robot dynamics.
  • To enhance robustness and reduce communication bandwidth requirements in multi-robot coordination.

Main Methods:

  • A decentralized leader-follower architecture with implicit communication via the grasped object.
  • Prescribed performance estimation laws for follower robots to estimate object trajectories.
  • Integration of navigation functions with adaptive control for robust motion planning.
  • Utilizing force/torque, position, and velocity measurements for feedback control.

Main Results:

  • Successful cooperative transportation of objects in constrained, obstacle-rich environments.
  • Demonstrated robustness against parametric uncertainty in robot dynamics.
  • Validation of reduced communication bandwidth through implicit coordination strategies.
  • Achieved arbitrarily small estimation errors in follower robots' trajectory predictions.

Conclusions:

  • The proposed implicit communication strategy enhances robustness and efficiency in multi-robot cooperative transportation.
  • The leader-follower architecture effectively manages complex tasks with static obstacles.
  • The methodology extends the state-of-the-art in robust motion planning and collision avoidance for non-linear systems.