Routing optimisation for Automated Guided Vehicle (AGV) systems
Automated guided vehicles (AGVs) are used for various industrial applications, e.g. in container ports and warehouses. Most existing AGV systems use traffic control based on pre-determined vehicle paths. While being reliable and easy to implement, this approach is inefficient compared to real-time vehicle routing. For example, many pre-determined paths are based on Manhattan distance instead of direct point-to-point distance. Our reserach focuses on routing optimisation for free-ranging AGV systems which allow vehicles to move without pre-determined paths. A fleed trajectory planning model is developed with consideration of vehicle kinematic constraints and a vehicle steering theory. This guarantees that the resulting paths are feasible with real AGVs. Routing safety is achieved by implementing a collision avoidance method that also takes into account trajectory uncertainty. Using a genetic algorithm approach, the model provides optimal or nearly optimal trajectories for an entire vehicle fleet in real-time.
Deployment of Unmanned Aerial Vehicles (UAVs) for humanitarian logistics
Post-disaster relief requires fast deployment of goods to people in need. Relief is a time-critical process, decisions must be taken quickly with very little information and all incorrect and inaccurate decisions lag the supply chain process. Unmanned Aerial Vehicles (UAVs) are becoming a cheap and accessible technology that may accelerate logistical processes involved in humanitarian relief. The aim of our research is to investigate the use of UAVs in humanitarian logistics and to create a model for the distribution of goods that can be used in the planning process of humanitarian aid. As such, the model encompasses a series of optimisation steps including the scheduling of UAV tasks, the generation of least energy consuming paths and air traffic management. The model also takes into account the limitations of UAVs, such as vulnerability to adverse weather conditions, limited payload capacity, and energy consumed during flight.
Shared autonomous vehicle based mobility
The concept of shared autonomous vehicles (SAV) could significantly reduce the number of cars in future cities and therefore improve traffic safety, mitigate congestion, free up parking space, increase energy efficiency and fundamentally change the nature of urban mobility. The planning of SAV based mobility requires some strongly coordinated decisions, with parameters such as fleet size, location of vehicle stations and vehicle repositioning strategies. Our research sets out to develop a methodology for the scheduling and routing optimizsation in SAV systems. This methodology takes into account stochastic demand patterns and is applied to design a resilient urban transport system with minimal waiting times and maximum profitability.