Research EngineerContact Andreas
The project aims at providing a logistic platform to be deployed at logistics operator premises. The platform will leverage on a fleet of autonomous vehicles coordinated by advanced planning and scheduling techniques to improve the productivity and reduce cost of logistic operators dealing with goods within a warehouse and for last mile delivery.
Aim and goal
Develop a logistic platform leveraging on AI techniques to coordinate a fleet of autonomous vehicles (unmanned aerial and terrestrial vehicles) for moving goods within a warehouse and for last mile delivery in areas where traditional solutions are expensive.
The challenge is to optimize the flow and use of resources within a warehouse using automated ground vehicles (AGV) and advanced planning and scheduling algorithms. In the extension drones will be used using similar techniques to deliver goods to nearby customers (hubs). For the drone operations there are many challenge such as performance prediction, collision avoidance, safety at take-off and landing areas. A great challenge is also to approach the issue of getting a flight permit to fly beyond visual line of sight (BVLOS).
By studying the warehouse process and understanding the limitations a scheduling and planning algorithm is developed together with AGV’s. A machine learning component helps refining/generating data to the planner. The drone section focuses on performance and feasibility if missions and will also start researching collision detection and avoidance.
The AWARD platform will help warehouses to improve quality, effectiveness and profit.
Bright Cape Holdings, University of Trento, Fundazione Bruno Kessler, Budapest University of Technology and Economics