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Communication Backbone Reconfiguration with Connectivity Maintenance

Leonardo Santos, Caio C. G. Ribeiro, Douglas G. Macharet

TL;DR

This work proposes a simple and effective trajectory planning framework that tackles the design, deployment, and reconfiguration of a communication backbone by reframing the problem of networked multi-agent motion planning as a manipulator motion planning problem.

Abstract

The exchange of information is key in applications that involve multiple agents, such as search and rescue, military operations, and disaster response. In this work, we propose a simple and effective trajectory planning framework that tackles the design, deployment, and reconfiguration of a communication backbone by reframing the problem of networked multi-agent motion planning as a manipulator motion planning problem. Our approach works for backbones of variable configurations both in terms of the number of robots utilized and the distance limit between each robot. While research has been conducted on connection-restricted navigation for multi-robot systems in the last years, the field of manipulators is arguably more developed both in theory and practice. Hence, our methodology facilitates practical applications built on top of widely available motion planning algorithms and frameworks for manipulators.

Communication Backbone Reconfiguration with Connectivity Maintenance

TL;DR

This work proposes a simple and effective trajectory planning framework that tackles the design, deployment, and reconfiguration of a communication backbone by reframing the problem of networked multi-agent motion planning as a manipulator motion planning problem.

Abstract

The exchange of information is key in applications that involve multiple agents, such as search and rescue, military operations, and disaster response. In this work, we propose a simple and effective trajectory planning framework that tackles the design, deployment, and reconfiguration of a communication backbone by reframing the problem of networked multi-agent motion planning as a manipulator motion planning problem. Our approach works for backbones of variable configurations both in terms of the number of robots utilized and the distance limit between each robot. While research has been conducted on connection-restricted navigation for multi-robot systems in the last years, the field of manipulators is arguably more developed both in theory and practice. Hence, our methodology facilitates practical applications built on top of widely available motion planning algorithms and frameworks for manipulators.
Paper Structure (12 sections, 12 figures, 1 table, 2 algorithms)

This paper contains 12 sections, 12 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem formulation
  4. Methodology
  5. Robot arm modeling
  6. Backbone deployment
  7. Trajectory planning
  8. Experiments
  9. Illustrative example
  10. Quantitative analysis
  11. Hardware experiment
  12. Conclusion and Future Work

Figures (12)

  • Figure 1: Illustration of a backbone reconfiguration with a different number of robots. The initial configuration is at the bottom, and the new one is shown at the top. The leader robot is depicted in black, and relay robots in orange. Planned trajectories (blue arrows), are guaranteed by construction to keep the backbone connected throughout the trajectories execution.
  • Figure 2: Outline of the full pipeline.
  • Figure 3: Manipulator draw in perspective as if the $z\text{-axis}$ was parallel to the page to demonstrate that the 3D manipulator approach solves the fixed distance limitation by varying $\theta$.
  • Figure 4: Backbone configurations composed by a different number of robots without changing the robot arm model. The base station is represented by the green base joint, and the robots are represented by the blue joints.
  • Figure 5: Example in which the manipulator configuration is valid, but the backbone configuration is not, because the arm is above the obstacle. The 3D obstacle is depicted in purple, and the 2D obstacle in gray.
  • ...and 7 more figures

Theorems & Definitions (5)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5