SEV ER E W EATH ER AVOID AN CE USING IN FO R M E D HEURISTIC SEAR C H

Transcription

1 SEV ER E W EATH ER AVOID AN CE USING IN FO R M E D HEURISTIC SEAR C H AIAA Sangeeta Bokadia and Dr. John Valasek Aerospace Engineering AIAA GN&C Conference, Montreal, Canada 7 August Aerospace Engineering

2 SEV ER E W EATH ER AVOID AN CE outline of presentation FREE FLIGHT Concept and advantages Need for a weather agent SEVERE WEATHER Examples THUNDERSTORM AVOIDANCE ALGORITHM A * Search and Modified A * Search Development of algorithm RESULTS CONCLUSIONS Aerospace Engineering

3 FREE FLIG H T concept and advantages CONCEPT A new way of managing air traffic Departure from the highly structured system to a more flexible system Pilot allowed to change route, speed and altitude, within limits Ultimate decision making authority lies with the air traffic controllers ADVANTAGES Reduced workload for Air Traffic Control (ATC) More flexibility in determining optimal flight path significant economic benefits Pilots equipped with more flexibility in the event of changing conditions Aerospace Engineering

4 W EATH ER AGEN T benefit for G.A. sector REDUCE NUMBER OF WEATHER RELATED ACCIDENTS Weather is a cause or factor in a significant percentage of aviation accidents In 990, weather was a factor in 25.9% of all General Aviation accidents. ASSIST THE G.A. PILOT Absence of airway structure in free flight Agent for detecting severe weather conditions and guiding the airplane safely through them: Weather Agent Traffic and terrain intelligent agents could: improve safety reduce pilot workload realize benefits of free flight Aerospace Engineering

5 RESEARCH OBJECTIVES Cockpit Data Fusion with Fixed-base Simulation Validation for Free-Flight Guidance Funded by State of Texas Advanced Technology Program ( ) Development and simulator validation of: Conflict resolution guidance software Weather restrictions guidance software Executive Agent Weather Radar Data Other Traffic Info... Ground Weather Service Weather Agent Traffic Agent ADS-B Other Weather Info.... Flight Plan Info. ATC Radar Overall Architecture of Agent Based Hierarchical System Aerospace Engineering

6 W EATH ER AGEN T severe weather DEFINITION OF SEVERE WEATHER Any destructive weather event Usually refers to localized storms: microburst tornado squall line MICROBURST Severe localized wind blasting down from thunderstorm Area: less than 2.5 miles in diameter Duration: less than 5 minutes Aerospace Engineering

7 W EATH ER AGEN T more examples of severe weather TORNADO Violent rotating column of air Extend between a convective cloud and the surface of the earth SQUALL LINE Convective storms organized into linear or bowed structures Hundreds of kilometers in length with life span of several hours Associated problems are gusty surface winds, thunderstorms, and heavy showers Aerospace Engineering

8 W EATH ER AGEN T squall line and its dangers DANGERS POSED BY A SQUALL LINE Strong wind shears within clouds can damage the structure of airplanes Downdrafts and updrafts in clouds are dangerous to light aircraft Flight under the squall line can be subject to rain or hail BEST FLIGHT PATH The safest flight path may be around the squall line, but the best flight path is through the weak spots in the squall line Aerospace Engineering

9 IN P U T TO W EATH ER AGEN T radar image of a thunderstorm Color I(x,y) (Intensity (dbz)) Weather Type none I(x,y)<5 none blue 5 < I(x,y) < 20 light green 20 <I(x,y) < 30 moderate yellow 30 < I(x,y) < 40 heavy red 40 < I(x,y) intense DANGEROUS ZONES Yellow and red regions are to be avoided Aerospace Engineering

10 DEVELOPM ENT OF ALGORITH M objective : safest and shortest route OTHER CONSTRAINTS Minimum segment length length of any segment in flight path cannot be less than this Maximum turning angle turns of angles greater than this are not allowed Minimum number of turns METHOD USED Modified A * Search Regions with intensity greater than 25 dbz are set as inaccessible End point θ L Starting point L = segment length θ = turning angle Aerospace Engineering

11 A * SEAR C H basics of the search technique INFORMED HEURISTIC SEARCH Aimed at minimizing evaluation function actual cost of reaching a node - g in the algorithm, g = distance traveled in reaching the node estimated cost of reaching the endpoint from this node - h in the algorithm, h = straight line distance to the end point + a deviation, if a thunderstorm lies on this straight line evaluation function - f and f(n) = g(n) + h(n) Complete and Optimal Search Time and memory intensive h = 0 g = 0 f =0 g=4 h=7 f = 3 g= h=0 h=5 f = f = 4 goal node starting node Aerospace Engineering

12 MODIFIED A * SEAR C H simplified memory bounded A*search MODIFICATIONS IN A * SEARCH Specified amount of memory is allocated to store the nodes Nodes with higher f values are dropped from the memory to make space for the new nodes Memory of best forgotten path is retained Search may not be optimal ADVANTAGES Reduces the amount of space used and time consumed By proper choice of the minimum segment length and the memory allocated, the best solution can be ensured number of nodes in the search tree in the algorithm are fixed by minimum segment length Aerospace Engineering

13 RESULTS Case - I SINGLE STATIONARY THUNDERSTORM 2 Original flight path Computed flight path PII 400 CPU time: 0.9 sec Resolution : mile * mile 2 initial point end point Radar Image With Original And New Flight Path Aerospace Engineering

14 RESULTS Case - II A LINE OF THREE STATIONARY THUNDERSTORMS 2 Original flight path Computed flight path PII 400 CPU time: 5.25 sec Resolution : mile * mile 2 initial point end point Radar Image With Original And New Flight Path Aerospace Engineering

15 RESULTS Case - III THREE THUNDERSTORMS MOVING ALONG +x AXIS y (miles) Speed of thunderstorm : 40 mph Speed of aircraft : 50 KTAS Dotted: intensity at point Solid: intensity at point Resolution : mile * mile x (miles) Final Flight Path With Contour Plot of Radar Image 2 initial point end point Aerospace Engineering

16 RESULTS Case - IV A LINE OF THUNDERSTORMS MOVING ALONG -x AXIS Speed of thunderstorm : 50 mph Speed of aircraft : 50 KTAS y (miles) Dotted: intensity at point Solid: intensity at point 2 Resolution : mile * mile x (miles) Final Flight Path With Contour Plot of Radar Image 2 initial point end point Aerospace Engineering

17 ALGORITH M S T A B IL IT Y pitfalls NO SOLUTION Starting point lies inside the region of intense thunderstorm activity Starting point is too close to the thunderstorm 2 Original flight path Line along which level nodes are chosen A case where the algorithm does not give a solution Resolution : mile * mile 2 initial point end point Aerospace Engineering

18 ALGORITH M S T A B IL IT Y pitfalls INADMISSABLE SOLUTION In some cases, computed path comes very close to the avoidable regions of thunderstorm set the lower intensity limit of inaccessible region as 20dBZ that would give larger deviations In cases of moving thunderstorms, algorithm sometimes generates sharp turns algorithm should have capability to generate smooth and feasible turns algorithm should take into account the direction in which thunderstorm is moving Aerospace Engineering

19 IM PLEM EN TATIO N requirements INPUTS TO THE ALGORITHM Onboard weather Doppler radar Weather information transmitted from ground IMPLEMENTING THE OUTPUT OF THE ALGORITHM Lateral and longitudinal autopilot Flight Management System Aerospace Engineering

20 CONCLUSIONS PRESENT ALGORITHM Computes safest and shortest route in most of the cases Computation time is within acceptable limits Finer grid sizes will give shorter routes, but can make the algorithm computationally too expensive Grid Size (miles) CPU Time (sec) no solution Aerospace Engineering

21 RECOM M ENDATIONS FUTURE WORK Functionality of the algorithm in a large sample area needs to be established. Algorithm should be capable of changing minimum segment length depending upon thunderstorm size. Algorithm is currently being implemented in real-time flight simulator Aerospace Engineering

22 RELATED TEXAS A& M PAPERS AIAA (GNC-6) Monday :00 AM Intelligent Agent for Aircraft Collision Avoidance S. Shandy and J. Valasek AIAA (GNC-8) Tuesday 9:30 AM Integrating the Evolving Modern Cockpit J. Painter, J. Valasek, D.T. Ward AIAA (GNC-25) Tuesday 2:00 PM Severe Weather Avoidance Using Informed Heuristic Search S. Bokadia and J. Valasek AIAA (Aero. Sciences, Reno NV) 4-7 January 02 Intelligent Executive Guidance Agent for Free Flight J. Rong Aerospace Engineering