The adoption of small and aerial vehicles endowed with cameras and other sensory system has improved the management of several near-earth remote sensing applications. A commercial platform is able to cover a set of waypoints and trigger an individual action in each. An important limitation when using unmanned aerial vehicles for image surveying is the platforms short working cycles. The UAV are just able to fly for a few minutes when fully shipped with sensors and instrumentation. This mean that the mission must be interrupted several times, for take-off and landing and battery replacement. Therefore, the mission time will increase and the mission safety decrease because it will involve further manual operations, e.g., human in the mission loop. If the waypoints are sorted in a specific manner the aerial mission can be optimized.
This work aims to address the problem of minimizing the UAV waypoint trajectories cost subject to a set of workspace and safety restrictions. This problem will be addressed using ACO metaheuristics.
The goals of this work are fourfold:
- Address the problem of planning an aerial mission for multiple unmanned aerial vehicles shipped with different image sensors, and subject to typical operation and safety restrictions
- Carry out experiments with different UAVs
- Combine heterogeneous imagery from several platforms to generate image products
- Writing report
- Sara Perez-Carabaza, Eva Besada-Portas, Jose A. Lopez-Orozco, Jesus M. de la Cruz, Ant colony optimization for multi-UAV minimum time search in uncertain domains,Applied Soft Computing,Volume 62, 2018.
- Liseth Viviana Campo, Agapito Ledezma, Juan Carlos Corrales, Optimization of coverage mission for lightweight unmanned aerial vehicles applied in crop data acquisition, Expert Systems with Applications, Volume 149, 2020.
- Willing to learn about nature-inspired algorithms
- Enthusiast about working with UAVs/drones
- Curious about field work
Theme(s): Sensing & measuring