Dynamic Multi-Drone Route Planning with Uncertainty and Constraints, DyMuDRoP

Purpose and goal:

The overall goal of the project is to develop novel AI techniques for achieving robust and resilient collaborative goal achievement in dynamic, uncertain and potentially adversarial environments. As a shared, over-arching use case, the project is focusing on handling constraints in a multi-drone scenario with multiple service providers, including static and dynamic constraints.

In this scenario the complex routing of multiple drones in urban environments are central and this routing is dependent on: Trajectory planning Dynamic constraints Regulatory constraints. Expected results and effects:

By adding AI capability, it is foreseen that the dimensioning of drone missions within controlled areas can be optimized and offer an overall more efficient solution where constraints, such as spatial network coverage and user population, is handled. Based on scheduled tasks and missions, drone trajectories and service demands can be forecasted, planned, and adapted to ensure safe operation in the controlled area in consideration of service demands.

Approach and implementation:

The central aim of this project is to solve a real problem that will arise in urban environments with unmanned agents and multiple constraints. In short, we need to handle a dynamical environment in a controlled area. Given this, the project will be structured in five technical work packages (WP), as follows;

WP1 - Use cases, needs and capabilities definition WP2 - Technology and system capabilities assessment and specification WP3 - Features research and development WP4 - Features integration WP5 - Experimentation and demonstrations.