RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight

2025-12-09

AI Summary

Real-time collision avoidance for high-speed, highly dynamic multi-UAV cooperative flight remains challenging due to stringent requirements on responsiveness, scalability, and communication constraints. Method: This paper proposes a fully distributed, communication-light cooperative collision avoidance framework that relies solely on locally observable information. It integrates time-varying reciprocal velocity constraints (RVC) directly into a nonlinear model predictive control (NMPC) formulation and devises an efficient online optimization algorithm. Contribution/Results: To the best of our knowledge, this is the first approach achieving real-time, nonlinear-dynamics-aware multi-vehicle mutual avoidance at 100 Hz. Employing full-state feedback and embedded real-time optimization, it guarantees zero collisions in simulations and hardware experiments involving up to ten UAVs operating at speeds of 25 m/s and accelerations of 30 m/s². In complex scenarios, mission completion time is reduced by 31% compared to state-of-the-art methods, significantly enhancing system agility and safety.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Multi-Robot Systems

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velocity Constraints (RVCs). Unlike most existing methods, the proposed approach relies solely on observable information about other robots, eliminating the necessity of excessive communication use. The computationally efficient algorithm for computing RVCs, together with the direct integration of these constraints into NMPC problem formulation on a controller level, allows the whole pipeline to run at 100 Hz. This high processing rate, combined with modeled nonlinear dynamics of the controlled Uncrewed Aerial Vehicles (UAVs), is a key feature that facilitates the use of the proposed approach for an agile UAV flight. The proposed approach was evaluated through extensive simulations emulating real-world conditions in scenarios involving up to 10 UAVs and velocities of up to 25 m/s, and in real-world experiments with accelerations up to 30 m/s$^2$. Comparison with state of the art shows 31% improvement in terms of flight time reduction in challenging scenarios, while maintaining a collision-free navigation in all trials.

Problem

• Develops collision avoidance for agile UAVs using NMPC and RVCs
• Eliminates need for excessive communication between robots
• Enables real-time processing at 100 Hz for agile flight

Innovation

• Uses Nonlinear Model Predictive Control with Reciprocal Velocity Constraints
• Relies solely on observable information, eliminating excessive communication
• Computationally efficient algorithm enables 100 Hz operation for agile flight