Distributed Connectivity Maintenance and Recovery for Quadrotor Motion Planning
🤖 AI Summary
To address frequent communication disconnections in multi-robot systems operating in complex environments with obstacles and occlusions, this paper proposes a distributed cooperative navigation framework integrating Model Predictive Control (MPC), Control Lyapunov Functions (CLFs), and High-Order Control Barrier Functions (HOCBFs). The method unifies HOCBFs and CLFs within a single MPC optimization—first achieving simultaneous formal guarantees on safety, connectivity maintenance, and proactive reconnection after disconnection in continuous time. Bézier curve parameterization enables efficient real-time trajectory planning. Extensive simulations and physical experiments with four Crazyflie nano-quadcopters demonstrate stable connectivity and collision-free flight under highly dynamic obstacle conditions. The approach significantly enhances the robustness of multi-rotor swarms in vision-limited scenarios, outperforming prior methods in maintaining both safety and network integrity.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Maintaining robot connectivity amidst obstacles and occlusions
Recovering connectivity from initial or temporary disconnections
Generating smooth collision-free trajectories for multi-robot systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Real-time distributed framework using HOCBFs for connectivity
Control Lyapunov functions enable connectivity recovery from disconnections
Unified MPC-CLF-CBF framework for continuous-time trajectory generation
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