• collision avoidance
• formation flight
• uav

The algorithm showcased in this work aims to provide collision avoidance capabilities for single agents and swarms of UAVs in a simple and efficient way, also accounting for minimum deviation and rerouting to intended trajectory and time spent within a formation maximization, all while keeping safe distance from potential threats and computational power requirements low. The algorithm also looks at providing an interface compatible with existing autopilot systems to keep it suitable for a real world implementation.
The work is focused on utilizing and enhancing existing methodologies. The case of a single agent having to avoid multiple obstacles will be addressed first and then later extended to formation flight. The system developed is capable of avoiding both static obstacles and dynamic ones, like other aerial vehicles. A protection sphere gets generated around the obstacles and an avoiding command is found and issued to the autopilot.
The algorithm includes the capability of recognizing which obstacles are more dangerous than others in order to solve crowded scenarios in which avoiding all of them with the same maneuver would be unlikely, by transforming the problem into a sequential one, in which obstacles are accounted for in smaller groups. Threat level of the obstacles gets accounted for through the use of collision times, that incorporate both speed and distance into the formulation, effectively representing how much an obstacle is dangerous.
Geometric methodologies are used to command the agent through the danger to keep complexity and computation times low but still find an optimal solution to the problem, minimizing deviation from the original path through airspeed and heading changes.
The main innovation lies into the extension of the same methodologies implemented for the single agent through combination with continuum deformation of the swarm’s shape in order to minimize occurrences of formation breaking to avoid incoming obstacles.
Monte Carlo simulations are being provided to validate the algorithm and showcase its capabilities, both in the single agent against multiple obstacles case and the multiple agents, meaning a swarm of drones, against one obstacle.