STP: An Aerial Spray Treatment Planning System

STP: An Aerial Spray Treatment Planning System W.D. Potter, Ramyaa, J. Li Artificial Intelligence Center, GSRC 111 University of Georgia, Athens, GA 30602 (Contact: potter@uga.edu or 706-542-0361) And J. Ghent, D. Twardus, H. Thistle USDA Forest Service

Overview of Presentation • Abstract • How did STP come about • Goals of STP • Basic architecture • Heuristics • Overview of STP • Conclusion and future developments

Abstract • The Spray Treatment Planner – an intelligent decision support system for aerial spray treatment. • A tool to schedule spraying pesticides aerially - a capacitated vehicle router • STP schedules the spraying operation of selected blocks from selected airports using single or multiple aircraft. • The scheduling is done to maximize the spray efficiency and spray productivity by minimizing the total time and distance flown. • It uses heuristics to obtain a near optimal solution.

How STP came about • The gypsy moth (Lymantria dispar L.) has been one of north American’s most devastating forest pests. • Application of pesticides by aircraft. • Determining the production needs - guess work or heuristics from other projects. • Over-estimating contract needs - larger than needed aircraft or more aircraft than needed. • Under-estimating contract needs - treatment at less than optimal timing. • Needs careful preparation and planning, as well as comparing different spray application strategies. • A classic problem to be solved by AI techniques.

Goals of STP • STP – a capacitated vehicle router • Attempts to give an optimal schedule for spraying • Schedule is restricted by fuel and pesticide tank capacity • Comparison of different schedules • Gives a realistic estimate of productivity and needs • Comparison of productivity of various aircraft explain

airport blocks

Goals of STP • STP – a capacitated vehicle router • Gives an optimal schedule for spraying • Schedule is restricted by fuel and pesticide tank capacity • Comparison of different schedules • Gives a realistic estimate of productivity and needs • Comparison of productivity of various aircraft explain

Optimal schedule : Quantitative measures of effectiveness Spray productivity = area sprayed total aerial spray operation time Spray efficiency = time spent spraying total aerial spray operation time Total aerial = time spent + ferry spray time spraying time Optimize : ferry time ; time spent spraying

The evaluation model flowchart

Heuristics Minimize total flying distance Minimize time spent in a block : “Flight Advisor” for a single block Minimize ferry time representation core of the heuristics justification implementation

Flight Advisor If the block = polygon of rectangles then spray along the longest side of rectangles else spray along the longest side of the polygon endif

Representation • G = {V,E} – a connected graph • V = {V1-Vn} a block set • E = {(Vi,Vi)} a set of flight lines • Lij – length of flight line Vi-Vj • Qi – load associated with Vi • Minimize a linear combination of total distance traveled by different aircraft • Restricted by pesticide and fuel capacity

Case 1 (typical case): The blocks are on the same side of the airport but the airport and the blocks are not in the same line. The total saved flight distance is: D1+D2-D3. Distance saved =D1+D2-D3

Case 2 (worst case): The blocks are on different sides of the airport, and the airport and blocks are in one line so that the total reduced distance is 0. Nothing saved

Case 3 (best case): The blocks are on the same side and in the same line with respect to the airport. The total saved distance in this case is D1+D2+D3.

Justification • The Capacitated Vehicle Routing Problem is the Traveling Salesperson Problem with additional constraints of capacity • Exact calculation is not possible for large inputs • Basnet (1997) gives 2 heuristics and shows that heuristics give reasonably close answers to the exact ones

Heuristics Minimize total flying distance Minimize time spent in a block : Flight Advisor” for a single block Minimize ferry time representation core of the heuristics justification implementation

Implementation CVRPS -- for multiple blocks serviced by a single airport CVRPM -- multiple airports

Capacitated Vehicle Routing Problem for Multiple Blocks Serviced by Single Airport • Forms initial runs such that each run services a single block and associates runs to blocks • For each run • 1) try combining the closest block • 2) combination successful if the capacity constraints are met • for each run (new combined run) calculate the full schedule as a collection of runs and choose the best

Implementation CVRPS -- for multiple blocks serviced by a single airport CVRPM -- for multiple airports

Capacitated Vehicle Routing Problem for Multiple Blocks Serviced by Single Airport • An extension of CVRPS using one airport as base or home airport and the rest for fueling or restocking pesticides. • Works by relaxing the constraints in capacity

Conclusion & future work • The spray advisor uses heuristic methods to find near optimal schedules for spraying selected blocks • This project is in progress • Some of the future areas of development involve • 1)Considering the terrain to be sprayed • 2)Considering mixed aircraft • 3)Considering preferred direction of flight

Thanks

Overview of the Presentation • Abstract • How did STP come about • Goals of STP • Basic architecture • Heuristics • Overview of STP • Conclusion and future developments

STP: An Aerial Spray Treatment Planning System