In the realm of Advanced Air Mobility (AAM), the ability for multiple autonomous UAVs to arrive at predefined destinations simultaneously is critical—whether to transport heavy payloads cooperatively or to synchronize actions in mission-critical scenarios. However, this task is far from simple. UAVs must navigate complex spatial environments, not only avoiding collisions with each other, but also steering clear of non-cooperative flying objects (NCFOs) that aren’t part of the system.
To tackle this challenge, the MARHES Lab has contributed to the development of MORRIS—the safe terminal tiMe-cOordinated contRolleR for multI-uav Systems. MORRIS is a novel safe linear quadratic optimal control algorithm designed to achieve time-synchronized arrivals while also ensuring collision-free trajectories.
MORRIS is made up of two integrated components:
1. Terminal Time-Coordinated Planner: Calculates optimal acceleration inputs to minimize the timing error between actual and desired arrival times for all UAVs.
2. Safety Layer using Control Barrier Functions (CBFs): Adjusts those acceleration commands to guarantee that the UAVs maintain safe distances from each other and from NCFOs, ensuring real-time collision avoidance.
This layered approach means that MORRIS not only keeps UAVs safely spaced in dynamic environments but also ensures that they complete their missions in perfect temporal coordination.
The Mobile Adaptive/Reactive Counter-Uncrewed System (MARCUS) is an international collaboration between the University of New Mexico, Sandia National Laboratories, ETH Zurich, the University of Zagreb, and Switzerland’s armasuisse. Supported by the NATO Science for Peace and Security Programme, the project addresses the rising threat posed by unauthorized drones (UAS) entering protected airspaces.
The MARCUS project develops a heterogeneous autonomous multi-robot system that can detect, track, and intercept rogue drones with minimal collateral damage. It combines ground-based mobile robots, aerial pursuer UAVs, and interceptor UAVs with capture mechanisms—each fitted with a diverse array of sensors including LiDAR, RGB-D cameras, radar, and stereo vision.
Key Features:
Runtime Assurance Control: Developed and implemented by Isaac Seslar, this ensures safe UAV operation by dynamically switching between high-performance and safety controllers when needed.
Deep Learning for Drone Detection: Using variants of YOLO and Kalman Filters for robust visual tracking in real-world and simulated environments.
Deep Reinforcement Learning (DRL): Algorithms like MAGNET and PPO-A2C were developed to train pursuers to collaborate while avoiding collisions in dynamic airspaces.
Multimodal Sensing: Integration of multiple sensor types improves robustness in unstructured or cluttered environments.
Field-Tested Systems: The final MARCUS architecture has been physically tested in outdoor environments, successfully demonstrating target detection, interception, and cooperative behavior between robots.
Applications:
MARCUS is aimed at protecting sensitive airspaces around:
Airports
Military installations
Large public events
Critical infrastructure
Its modular and scalable design makes it adaptable for future defense and civilian applications.
