Research Programme

The research vision is to critically examine the energy reduction potential, using a case study and live trial based around NHS patient diagnostic sample, pharmacy and blood transportation, of integrated logistics solutions involving UAVs operating in shared airspace and alongside traditional and sustainable last-mile delivery solutions (vans, cargo cycles, walking porters via micro-consolidation points).  

 

Measurable objectives: 

 

  1. Investigate the collective transport and energy impacts of current 'business-as-usual' NHS patient diagnostic sample, pharmacy and blood transportation logistics across the Solent region. 

  2. Develop new simulation tools to quantify the energy consumption of UAVs and land logistics systems resulting from: i) potential new types of traffic regulation for shared airspace; ii) UAV collision and dynamic automated path re-planning stipulations; iii) conflict-resolution rules; iv) types of permitted coordination; v) the availability and positioning of ground logistics systems and infrastructure to effectively interact with and service UAVs. 

  3. Evaluate using the simulation tools and live trials the impact on air space and energy use of a large scale take-up of UAVs for medical logistics across the Solent region. 

  4. Develop fundamental new understandings of stakeholder concerns and the regulatory and governance needs associated with UAV interventions that realise energy benefits in logistics.

Modelling - Emerging  Solution Workflow

One of the core problems tackled by the E-Drone project is how to optimally manage the NHS’ complex logistics network in the Solent region. More specifically, this involves minimising the cost and time taken to deliver samples from surgeries in the region to Southampton General Hospital via either van, cargo bike or drone.

Figure 1 below depicts a global view of how four areas of research within the E-Drone project operate and communicate in order to solve the problem. The four areas of research, shown in the four columns, are:

  • Land-Air Logistics Optimisation (LALO) lead by University of Southampton (UoS) – This branch optimises logistics resources within a target area given date, weather conditions and sample location and quantity. The given solution decides which form of transport should be used for which samples across the region. It also calculates time optimised bike routes.

  • Land Energy and Route Optimisation (LERO) lead by University College London (UCL) – This branch takes a given origin, destination, and departure time to calculate the most energy and time efficient route using vans.

  • Air Energy and Route Optimisation (AERO) lead by UCL – This branch takes a given origin, destination, and departure time to calculate the most energy, risk, and time efficient route using drone.

  • Risk Optimisation (RO) lead by UoS – This branch generates a risk map for the target area, which is used to generate a route through the region.

Figure 1 also contains the assumptions which have been applied to the problem to allow the solution to be reached.

workflow.jpg

Figure 1: E-Drone Problem Proposed Solution Workflow and Constraints

 

 

Workflow Explanation

The workflow starts within the LALO branch and, as a large quantity of deliveries will be completed by land, this branch immediately communicates with the LERO branch, requesting routes between all major surgeries and the hospital.

LALO then makes a basic weather check to see if drone flight is possible. If so, a series of requests are sent to AERO for routes between all relevant surgeries and the hospital. Aero begins by performing a more thorough weather check, considering the wind direction and battery capacity of the drone. If flight is still possible, AERO proceeds to request a risk map of the region at that moment in time from RO and using this, proceeds to complete its initial guess of the optimal drone route between an origin and destination. AERO then refines the route via a locally bounded route optimisation method, and then returns the routes back to LALO.

Finally, LALO takes all routes generated by LERO and AERO and determines the most optimal set of routes to complete all deliveries of samples to the hospital from the set of surgeries, using a mixture of van, bike, and drone.