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

TL;DR

This paper introduces a real-time nonlinear model predictive control method with reciprocal velocity constraints for collision avoidance in UAVs, enabling agile flight with high computational efficiency and minimal communication.

Contribution

It presents a novel NMPC approach incorporating RVCs that relies only on observable data, running at 100 Hz for agile UAV collision avoidance.

Findings

Achieved 31% longer flight times compared to existing methods.

Maintained collision-free navigation in all simulated and real-world tests.

Supported up to 10 UAVs flying at 25 m/s with high acceleration.

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.