Path Optimization
AUTONOMOUS_UAV_ROUTING // DYNAMIC_OBSTACLE_AVOIDANCE
Computational Navigation
AI-driven path optimization ensures that autonomous delivery drones navigate complex agricultural landscapes with maximum efficiency. By solving the "Traveling Salesman Problem" in 3D space and in real-time, fleets can deliver supplies or gather multispectral data while dynamically adjusting for wind patterns, power consumption, and shifting terrain.
Navigation Compute Stack
Real-time aerial orchestration requires high-speed spatial reasoning and parallel trajectory modeling:
- MULTI-OBJECTIVE TRAJECTORY OPTIMIZATION
- WIND-VECTOR COUPLING & ENERGY MODELING
- VOXEL-BASED SPATIAL MAPPING (Octomap)
- EDGE-HPC GPU ACCELERATION (Jetson/TPU)
- DECENTRALIZED SWARM RE-ROUTING
Leading Research Institutions
ETH Zurich (IDSC)
Global leaders in dynamic systems and control, specializing in high-speed drone racing and autonomous obstacle avoidance.
UPenn GRASP Lab
Pioneering multi-robot coordination and lightweight navigation algorithms for autonomous micro-aerial vehicles.
MIT (ACL)
The Aerospace Controls Lab focuses on robust planning and control for autonomous vehicles in complex, uncertain environments.
TU Delft MAVLab
Specializing in bio-inspired navigation and swarm intelligence for small, highly maneuverable autonomous drones.