Prof. Dr. techn. Gerhard Elsbacher
Head of TTZ Unmanned Aerial Systems | Cluster Speaker Unmanned Flying | Research Professor
The primary reason to deploy a swarm of UAVs instead of a single entity is merely just to ‘cover more ground‘ and ensure robustness by sheer numbers, but to gain (collective) capabilities that are, if done correctly, more than just the sum of its parts.
Hence, in any swarm of UAVs, every single entity shall contribute to the collective mission that the group is tasked to fulfil. Since a mission is usually divided into several phases, the importance of different subtasks (which are necessary for the overall mission goal) changes over the course of flight.
Additionally, a swarm configuration may be either homogeneous or heterogeneous regarding the sensor-, effector- and communication-capabilities of each airframe.
Since the quality of the sensor fusion is greatly depending on relative geometry and physical systems are subject to various (geometric) constraints, the optimal spatial shape, that is the formation of the swarm, is of immense interest to guarantee mission success.
A network of agents (nodes) forms a graph G with n agents and edges E for each valid line-of-sight (LoS) connection:
The network forms a multi-agent system, in which each agent is modeled as a nonlinear system that responds to control:
Definition of the control input based on a consensus:
The vector rij describes the spatial offset between agents i and j.
This vector must be determined for the entire swarm in such a way that the objective function (i.e., the reward) is maximized:
→ Two actors are pursuing a goal but have no direct connection to one another. A third actor acts as a mediator but does not pursue any goal.
Cooperative Localization:
Cooperative Navigation:
Cooperative instructions:
Multi-objective / multi-constraint problem.
Static or dynamic (depending on the application).
Approaches to solving the problem: