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Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

Manuel Boldrer, Michal Kamler, Afzal Ahmad, Martin Saska

TL;DR

A novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view.

Abstract

We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.

Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

TL;DR

A novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view.

Abstract

We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.
Paper Structure (12 sections, 12 equations, 10 figures, 6 tables)

This paper contains 12 sections, 12 equations, 10 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem description and background material
  4. Proposed Approach
  5. Computing the set $\mathcal{B}_i$ and $\mathcal{W}_i$
  6. The density function $\psi_i(q)$
  7. Low-level controller
  8. Simulation results
  9. Single-robot simulations
  10. Multi-robot simulations
  11. Experimental Results
  12. Conclusions, limitations and future work

Figures (10)

  • Figure 1: Snapshot during a real-world experiment in the forest with $3$ UAVs.
  • Figure 2: Graphical representation of the sensor field-of-view $\mathrm{FoV} = \{f_x, f_z, f_a\}~(\deg)$.
  • Figure 3: 2D Graphical representation of the set $\mathcal{A}_i$. On the left, the case where the robots are separated by more than $2\Delta_{ij}$; on the right, the case where they are separated by less than $2\Delta_{ij}$. The half-spaces are constructed according to \ref{['eq:halfspaces']} such that all the points are collision-free.
  • Figure 4: 2D Graphical representation of the set $\mathcal{C}_i$ (which in this particular case correspond also with the set $\mathcal{B}_i$) computed using the proposed seeds. The obstacles are represented by grey circles.
  • Figure 5: Illustration of the main variables associated with the $i$-th robot during a real-world experiment. The robot’s pose $p_i$ is shown as an axis reference frame. The set $\mathcal{W}_i$ is represented by the magenta set, and the centroid position $c_{\mathcal{B}_i}$ is shown in orange. The planned path is depicted by a red line. The $wp_i$ and $e_i$ indicate respectively the active waypooint and the final goal positions. Other robots positions $p_j$ are indicated by a grey box. The colored point cloud illustrates obstacles, with different colors corresponding to different z-values.
  • ...and 5 more figures

Theorems & Definitions (1)

  • Remark 1