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Distributed Connectivity Maintenance and Recovery for Quadrotor Motion Planning

Yutong Wang, Yichun Qu, Tengxiang Wang, Lishuo Pan, Nora Ayanian

TL;DR

This work tackles robust connectivity maintenance for multi-robot motion planning in cluttered environments by merging high-order control barrier and Lyapunov function techniques within an MPC framework. Connectivity is enforced with HOCBF constraints and connectivity recovery is driven via HOCLF-based terms, both integrated into a continuous-time Bezier-trajectory representation that is optimized over a receding horizon. The proposed MPC–CLF–CBF formulation yields a distributed QP for each robot, balancing safety, connectivity, and progress toward goals while handling obstacles. The approach is validated through simulations and a physical experiment with 4 Crazyflie quadrotors, demonstrating robust connectivity preservation and recovery in dynamic, obstacle-rich scenarios.

Abstract

Maintaining connectivity is crucial in many multi-robot applications, yet fragile to obstacles and visual occlusions. We present a real-time distributed framework for multi-robot navigation certified by high-order control barrier functions (HOCBFs) that controls inter-robot proximity to maintain connectivity while avoiding collisions. We incorporate control Lyapunov functions to enable connectivity recovery from initial disconnected configurations and temporary losses, providing robust connectivity during navigation in obstacle-rich environments. Our trajectory generation framework concurrently produces planning and control through a Bezier-parameterized trajectory, which naturally provides smooth curves with arbitrary degree of derivatives. The main contribution is the unified MPC-CLF-CBF framework, a continuous-time trajectory generation and control method for connectivity maintenance and recovery of multi-robot systems. We validate the framework through extensive simulations and a physical experiment with 4 Crazyflie nano-quadrotors.

Distributed Connectivity Maintenance and Recovery for Quadrotor Motion Planning

TL;DR

This work tackles robust connectivity maintenance for multi-robot motion planning in cluttered environments by merging high-order control barrier and Lyapunov function techniques within an MPC framework. Connectivity is enforced with HOCBF constraints and connectivity recovery is driven via HOCLF-based terms, both integrated into a continuous-time Bezier-trajectory representation that is optimized over a receding horizon. The proposed MPC–CLF–CBF formulation yields a distributed QP for each robot, balancing safety, connectivity, and progress toward goals while handling obstacles. The approach is validated through simulations and a physical experiment with 4 Crazyflie quadrotors, demonstrating robust connectivity preservation and recovery in dynamic, obstacle-rich scenarios.

Abstract

Maintaining connectivity is crucial in many multi-robot applications, yet fragile to obstacles and visual occlusions. We present a real-time distributed framework for multi-robot navigation certified by high-order control barrier functions (HOCBFs) that controls inter-robot proximity to maintain connectivity while avoiding collisions. We incorporate control Lyapunov functions to enable connectivity recovery from initial disconnected configurations and temporary losses, providing robust connectivity during navigation in obstacle-rich environments. Our trajectory generation framework concurrently produces planning and control through a Bezier-parameterized trajectory, which naturally provides smooth curves with arbitrary degree of derivatives. The main contribution is the unified MPC-CLF-CBF framework, a continuous-time trajectory generation and control method for connectivity maintenance and recovery of multi-robot systems. We validate the framework through extensive simulations and a physical experiment with 4 Crazyflie nano-quadrotors.

Paper Structure

This paper contains 13 sections, 23 equations, 1 figure.

Table of Contents

  1. INTRODUCTION
  2. PRELIMINARIES
  3. High-Order Control Barrier Functions
  4. Control Lyapunov Functions (CLF) and High-Order Implementation
  5. Connectivity in Multi-Robot Systems
  6. Bézier Curve
  7. PROBLEM STATEMENT
  8. MPC--CLF--CBF Framework
  9. Connectivity maintenance
  10. Connectivity via HOCBF
  11. Connectivity recovery via HOCLF
  12. Collision avoidance
  13. Quadratic program

Figures (1)

  • Figure 1: Long exposure of $4$ quadrotors navigating a cluttered environment with 6 obstacles. All quadrotors are connected at the beginning, and the team is forced to disconnect into 2 subgroups to avoid obstacles. Eventually, all robots reconnect and reach their goals.

Theorems & Definitions (1)

  • Remark 1