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Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents

Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou

TL;DR

The paper addresses cooperative 3D surface coverage using a team of networked UAVs by formulating a receding-horizon optimal-control problem that jointly optimizes kinematic inputs and camera gimbal orientations. To handle the computational burden of visibility, it integrates a learning-based conversion of non-convex visibility checks into logical constraints that fit within a mixed-integer optimization framework. The approach includes a grid-based visibility learning step, a MILP-style constraint set, and a coverage objective that prioritizes early and non-duplicative facet observation. Simulations with three UAVs demonstrate feasible online planning over a short horizon and successful coordination to cover selected facets, highlighting the method's potential for distributed and robust extensions.

Abstract

This work proposes a receding horizon coverage control approach which allows multiple autonomous aerial agents to work cooperatively in order cover the total surface area of a 3D object of interest. The cooperative coverage problem which is posed in this work as an optimal control problem, jointly optimizes the agents' kinematic and camera control inputs, while considering coupling constraints amongst the team of agents which aim at minimizing the duplication of work. To generate look-ahead coverage trajectories over a finite planning horizon, the proposed approach integrates visibility constraints into the proposed coverage controller in order to determine the visible part of the object with respect to the agents' future states. In particular, we show how non-linear and non-convex visibility determination constraints can be transformed into logical constraints which can easily be embedded into a mixed integer optimization program.

Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents

TL;DR

The paper addresses cooperative 3D surface coverage using a team of networked UAVs by formulating a receding-horizon optimal-control problem that jointly optimizes kinematic inputs and camera gimbal orientations. To handle the computational burden of visibility, it integrates a learning-based conversion of non-convex visibility checks into logical constraints that fit within a mixed-integer optimization framework. The approach includes a grid-based visibility learning step, a MILP-style constraint set, and a coverage objective that prioritizes early and non-duplicative facet observation. Simulations with three UAVs demonstrate feasible online planning over a short horizon and successful coordination to cover selected facets, highlighting the method's potential for distributed and robust extensions.

Abstract

This work proposes a receding horizon coverage control approach which allows multiple autonomous aerial agents to work cooperatively in order cover the total surface area of a 3D object of interest. The cooperative coverage problem which is posed in this work as an optimal control problem, jointly optimizes the agents' kinematic and camera control inputs, while considering coupling constraints amongst the team of agents which aim at minimizing the duplication of work. To generate look-ahead coverage trajectories over a finite planning horizon, the proposed approach integrates visibility constraints into the proposed coverage controller in order to determine the visible part of the object with respect to the agents' future states. In particular, we show how non-linear and non-convex visibility determination constraints can be transformed into logical constraints which can easily be embedded into a mixed integer optimization program.
Paper Structure (13 sections, 12 equations, 2 figures, 2 algorithms)

This paper contains 13 sections, 12 equations, 2 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Agent Kinematic Model
  4. Agent Camera Model
  5. Problem Formulation
  6. Cooperative Receding Horizon 3D Coverage Control
  7. Visibility Determination
  8. Coverage Objective Function
  9. Constraints
  10. Evaluation
  11. Simulation Setup
  12. Simulation Experiment
  13. Conclusion

Figures (2)

  • Figure 1: Illustrative example of the visibility determination problem, where $x_\kappa^{j,\mathbf{p}}$ is the future predicted agent position at time-step $k+\kappa|k$, and $\mathcal{V}_\kappa$ is the predicted FOV configuration at the same time-step. With the FOV pose shown in the figure the facet indicated with $\times$ is not visible as there is no light-ray that can be traced back to it i.e., all light-rays are blocked. On the other hand the facet indicated with a $\star$ can be observed as shown.
  • Figure 2: The figure shows an illustrative example of the proposed cooperative receding horizon 3D coverage controller with a team of 3 agents.