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GRF-based Predictive Flocking Control with Dynamic Pattern Formation

Chenghao Yu, Dengyu Zhang, Qingrui Zhang

TL;DR

A predictive flocking control algorithm is proposed based on a Gibbs random field (GRF), where bio-inspired potential energies are used to charaterize "robot-robot" and "robot-environment" interactions to improve the flocking behaviors.

Abstract

It is promising but challenging to design flocking control for a robot swarm to autonomously follow changing patterns or shapes in a optimal distributed manner. The optimal flocking control with dynamic pattern formation is, therefore, investigated in this paper. A predictive flocking control algorithm is proposed based on a Gibbs random field (GRF), where bio-inspired potential energies are used to charaterize ``robot-robot'' and ``robot-environment'' interactions. Specialized performance-related energies, e.g., motion smoothness, are introduced in the proposed design to improve the flocking behaviors. The optimal control is obtained by maximizing a posterior distribution of a GRF. A region-based shape control is accomplished for pattern formation in light of a mean shift technique. The proposed algorithm is evaluated via the comparison with two state-of-the-art flocking control methods in an environment with obstacles. Both numerical simulations and real-world experiments are conducted to demonstrate the efficiency of the proposed design.

GRF-based Predictive Flocking Control with Dynamic Pattern Formation

TL;DR

A predictive flocking control algorithm is proposed based on a Gibbs random field (GRF), where bio-inspired potential energies are used to charaterize "robot-robot" and "robot-environment" interactions to improve the flocking behaviors.

Abstract

It is promising but challenging to design flocking control for a robot swarm to autonomously follow changing patterns or shapes in a optimal distributed manner. The optimal flocking control with dynamic pattern formation is, therefore, investigated in this paper. A predictive flocking control algorithm is proposed based on a Gibbs random field (GRF), where bio-inspired potential energies are used to charaterize ``robot-robot'' and ``robot-environment'' interactions. Specialized performance-related energies, e.g., motion smoothness, are introduced in the proposed design to improve the flocking behaviors. The optimal control is obtained by maximizing a posterior distribution of a GRF. A region-based shape control is accomplished for pattern formation in light of a mean shift technique. The proposed algorithm is evaluated via the comparison with two state-of-the-art flocking control methods in an environment with obstacles. Both numerical simulations and real-world experiments are conducted to demonstrate the efficiency of the proposed design.
Paper Structure (11 sections, 30 equations, 8 figures, 1 table)

This paper contains 11 sections, 30 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related works
  3. Problem formulation
  4. Approach
  5. Potential energies
  6. Gibbs random field
  7. Control determination
  8. Simulation and experiments
  9. Simulation
  10. Experiments
  11. Conclusions

Figures (8)

  • Figure 1: Dynamic pattern formation experiments. Ten UAVs forming "S", "Y", "S", and "U" patterns in a sequence.
  • Figure 2: The mode of shape control potential energy. The yellow and green balls represent swarm robots. Robots calculate $\bold{p}_{ms}$ based on the area within $r_{sen}$ (light yellow area) that is not occupied by other robots (light blue area).
  • Figure 3: Flocking trajectories (Simulation 1).
  • Figure 4: Flocking performance (Simulation 1). (a) denotes the distance between the robot and the obstacle. (b) shows the croup consistency (order).
  • Figure 5: Distance metric (Simulation 1). Areas covered by light colors represent the variation of the metric.
  • ...and 3 more figures