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.

Overview of Presentation

• Abstract

• How did STP come about

• Goals of STP

• Basic architecture

• Heuristics

• Overview of STP

• Conclusion and future developments

• 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.

How STP came about

Overview of Presentation

• Abstract

• How did STP come about

• Goals of STP

• Basic architecture

• Heuristics

• Overview of STP

• Conclusion and future developments

• 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

Goals of STP

airport

blocks

airport

blocks

• 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

Goals of STP

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

Overview of Presentation

• Abstract

• How did STP come about

• Goals of STP

• Basic architecture

• Heuristics

• Overview of STP

• Conclusion and future developments

The evaluation model flowchart

Overview of Presentation

• Abstract

• How did STP come about

• Goals of STP

• Basic architecture

• Heuristics

• Overview of STP

• Conclusion and future developments

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 thenspray along the longest side of rectangles

else spray along the longest side of the

polygon endif

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

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

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

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.

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

• 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

Justification

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

• 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

Capacitated Vehicle Routing Problem for Multiple Blocks Serviced by Single Airport

Overview of Presentation

• Abstract

• How did STP come about

• Goals of STP

• Basic architecture

• Heuristics

• Overview of STP

• Conclusion and future developments

Overview of Presentation

• Abstract

• How did STP come about

• Goals of STP

• Basic architecture

• Heuristics

• Overview of STP

• Conclusion and future developments

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