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Cockpit System Design for General Aviation
Cockpit System Design for General Aviation Free Flight Using a Cognitive Engineering Approach AIAA-2003-5774 Jie Rong,Yuanyuan Ding, and John Valasek Aerospace Engineering AIAA Guidance, Navigation, & Control Conference Austin, TX 14 August 2003 OVERVIEW ¾ New Challenges and Issues General Aviation in Free Flight Higher Volume Operation (HVO) in Small Aircraft Transportation System (SATS) SATS Multi-layer Air Traffic Space (MATS) Concept ¾ Previous Efforts General Aviation Pilot Advisory/Training System (GAPATS) Smart Flight Guidance System ¾ New Ideas Aircraft Approach and Landing Assistant (AALA) ¾ Facility Real-Time Engineering Flight Simulator (EFS) Development in progress ¾ Summary Rong, Ding, Valasek 2003-5774- 2 Aerospace Engineering GENERAL AVIATION: THE FUTURE Aircraft “Highway-in-the-Sky” ¾ GA Free Flight More flexibility for pilots in choosing flight paths, compared to current IFR More responsibilities for pilots in ensuring aircraft safety Reduced work load for ATC and significant economic benefits Auto Landing Sequencing ¾ Small Aircraft Transportation System (SATS) Expand use of smaller airports and smaller aircraft for public transportation Reduce congestion at large hub airports and on major highways Satisfy the public demand for safe, high-speed mobility and increased accessibility Rong, Ding, Valasek 2003-5774- 3 Aerospace Engineering SATS HIGH VOLUME OPS (1) NON-CONTROLLED AIRPORTS ¾ NASA LaRC Team Concept Establish Self Controlled Area (SCA) during IMC conditions around designated airports Airport management module (AMM) only provides landing sequence for aircraft in SCA Pilots are responsible for aircraft safety for the whole approach and landing process DF 7 Runway 6 MAP SCA 3 FAF IAF L IF 2 4 IAF R 5 Rong, Ding, Valasek 2003-5774- 4 Aerospace Engineering SATS HIGH VOLUME OPS (2) NON-CONTROLLED AIRPORTS ¾ Texas A&M Team Concept New airport terminal area infrastructure—Multi-layer Air Traffic Space (MATS) Airport agent only takes charge of aircraft inside the terminal layer. Negotiation, and coordination among aircraft occur in the Negotiation Layer The last resort - Traffic management agent Aircraft Agent En Route Layer En Route Layer Traffic Management Agent Negotiation Layer Aircraft Agent Negotiation Layer Airport Terminal Layer Airport Terminal Layer Airport Airport Agent 1Ding, Rong, Valasek , “Automation capability analysis methodology for non-controlled airports”, AIAA Paper 2003-5601 Rong, Ding, Valasek 2003-5774- 5 Aerospace Engineering PILOT DECISION MAKING ¾ A critical issue in realizing SATS HVO at non-controlled airports Today, faulty pilot decision-making becomes most cited caused of GA accidents Of all the flight phases, approach and landing have least margin of safety Pilot Capabilities M argin of Safety T ask Requirements Preflight T axi Take off Cruise Approach & Landing Taxi The problem of high pilot workload and low pilot situation awareness will worsen in SATS HVO • • Both LaRC and TAMU concepts are based on a distributed decision making environment, with most of the decision making left to the pilot More flight safety responsibilities ->More demanding flight tasks->More raw flight information Rong, Ding, Valasek 2003-5774- 6 Aerospace Engineering RESEARCH OBJECTIVE ¾ Develop real-time, intelligent decision-aid tool for SATS pilots during approach and landing phases Aircraft Approach and Landing Assistant (AALA) Apply multi-disciplinary technologies to the design of cockpit systems for future GA aircraft Increase pilot situation awareness, decrease pilot workload, and improve flight safety Use high-fidelity simulation to develop advanced aviation systems in a university laboratory ¾ Related Work: Autonomous Operations Planner developed by NASA Langley Commercial air transports and en-route airspace Rong, Ding, Valasek 2003-5774- 7 Aerospace Engineering GA PILOT ADVISOR SYSTEM (1) 1995 - 1998 ¾ Artificial Intelligence in the Cockpit Explore Cockpit Applications of Fuzzy Logic, Expert Systems, etc. ¾ Fuzzy Logic for Flight Mode Interpretation Modes: Cruise, Initial Approach, Hold, etc. Automates Display Management: MFD Map Scale, HUD ILS, etc. ¾ Rule-based Pilot Advisor Automated Cautions and Warnings for Non-Nominal Conditions Flight Operations: Airspeed, Altitude, Navigation, etc. Aircraft Configuration: Gear, Flaps, Power, etc. ¾ Design Goals User friendly and affordable by GA pilots 1 STTR: NAS1-20290, Knowledge-Based Systems, Inc. & TAMU of College Station, TX, 1995-1998. Rong, Ding, Valasek 2003-5774- 8 Aerospace Engineering GA PILOT ADVISOR SYSTEM (2) 1995 - 1998 2D projection of 9D function Multi-Functional Head Down Display Flight Mode Interpreter Test Results Landing pilot FMI Final app Init app Cruise Climbout Takeoff Taxi 0 100 200 300 400 500 600 700 800 Flight time [sec] Rong, Ding, Valasek 2003-5774- 9 Aerospace Engineering SMART COCKPIT COMPUTING (1) 1999 – 2002 ¾ Cockpit Data Fusion with Fixed-base Simulation Validation for Free Flight Guidance Texas Advanced Technology Program ¾ Research Goals: a new Conflict Detection & Resolution algorithm for GA Free Flight Designed for multiple conflicts situation, currently for weather and traffic conflicts Pre-assumptions: on-board weather radar, ADS-B system, CD&R, and FMS Implemented as a guidance software in FMS Alert Zone Protected Zone Rong, Ding, Valasek 2003-5774- 10 Aerospace Engineering SMART COCKPIT COMPUTING (2) 1999 – 2002 ¾ Weather Agent Executive Agent Weather Radar Data Ground Weather Service Other Weather Info. ... Input: onboard weather radar images Computes an optimal flight path to avoid the detected weather conflicts Modified A* Search method Other Traffic Info... Weather Agent Flight Plan Info. Traffic Agent ¾ Traffic Agent ADS-B Inputs: ADS-B state vectors of aircraft in its immediate airspace Calculates an evasion maneuver to keep the aircraft out of other protected zones Knowledge based expert system and optimal control ATC Radar Overall Structure of Hierarchical Agent System Separate, independent, intelligent agents ¾ Executive Agent 1 Rong, J., Bokadia, S., Valasek, J., Shandy, S., “Hierarchical agent based for general aviation CD&R under free flight, AIAA Paper 2002-4553 Rong, Ding, Valasek 2003-5774- 11 One high-level arbitrator, coordinates lower-level agents Fuzzy evaluation method Determines final flight guidance Aerospace Engineering SMART COCKPIT COMPUTING (3) 1999 – 2002 ¾ Flight Simulator Test Case Example: One squall line, two bogey aircraft Squall line: moving SW at 30 KTAS Bogey aircraft: flying from KACT to KCLL, at 150 KTAS Bogey Aircraft Thunderstorms Radar Image Subject Agent System Issues a New Flight Path to Avoid Aircraft Passes Two Bogey Aircraft Aircraft Flies Along a New Path to SquallSide Line Incoming Bogey Aircraft From theAvoid Left Hand Subject Aircraft Started From KCLL to KACT Rong, Ding, Valasek 2003-5774- 12 Aerospace Engineering INTELLIGENT PILOT DECSION AID TOOL (1) ¾ Aircraft Approach and Landing Assistant (AALA) GAPATS: • • Flight Control System Flight Situation Interpretation Rule-based Pilot Advisor Aircraft States Smart Cockpit Computing: • • • Weather Info. Multi-agents System Conflict Detection Conflict Resolution Traffic Info. Interpret raw data into more useful information Decrease pilot monitoring tasks and simple repetitive tasks Terrain Info. Pilot Rong, Ding, Valasek 2003-5774- 13 Weather Agent Traffic Agent Terrain Agent Pilot Adviosr Interface Manager Approach & Landing Manager Aircraft Approach and Landi ng Assistant Head-down Display Aerospace Engineering INTELLIGENT PILOT DECSION AID TOOL (2) 1. Develop a high-fidelity, real-time, human-in-the-loop simulation system Multiple aircraft simulation system for traffic scenario generation Adding more features to visual environment of the EFS 2. Specify basic functions of decision aids Preprocessing Information Reducing Pilot Monitoring Tasks Issuing Warning and Advice Managing Information Display Rong, Ding, Valasek 2003-5774- 14 Aerospace Engineering INTELLIGENT PILOT DECSION AID TOOL (3) 3. Select Interface Soft Pilot/FMS Interface 4. Highway In The Sky Display Implement in Software 5. Mult-Functional Head Down Display Encode Expert Systems/Knowledge-based System Utilize existing codes Integrate with existing simulation system Evaluate on EFS Human Factors Testing Rong, Ding, Valasek 2003-5774- 15 Aerospace Engineering REAL-TIME ENGINEERING FLIGHT SIMULATOR (1) ¾ Fixed-base: Commander 700; AV-8A Harrier, F-5A Freedom Fighter SGI Onyx Reality II sim engine Networked bank of PC’s Center stick; sidestick ¾ 155o projected field of view 30 Hz refresh rate ¾ Programmable Head Up Display Rong, Ding, Valasek 2003-5774- 16 Aerospace Engineering REAL-TIME ENGINEERING FLIGHT SIMULATOR (2) ¾ Head Down Displays (HDD) Reconfigurable CRT; touchscreen LCD Moving Map NAV Display FMS & Autopilot Interface Touch--Sensitive Screen ¾ Autopilot Glide slope capture Heading Altitude Pitch attitude Gear Handle ¾ Flight Management System (FMS) Interface between pilot and autopilot Pre-flight planning and enroute updating Moving map display Jeppesen data base Rong, Ding, Valasek 2003-5774- 17 Aerospace Engineering Rong, Ding, Valasek 2003-5774- 18 Aerospace Engineering Rong, Ding, Valasek 2003-5774- 19 Aerospace Engineering REAL-TIME ENGINEERING FLIGHT SIMULATOR (4) NAV/MAP DISPLAY Rong, Ding, Valasek 2003-5774- 20 Aerospace Engineering UPGRADES IN PROGRESS air and ground traffic 3D actual terrain and airports actual weather displays Rong, Ding, Valasek 2003-5774- 21 Aerospace Engineering SUMMARY ¾ Previous research efforts demonstrate that intelligent cockpit systems can be effective and efficient aids to pilot information processing and decision making for current GA operations. ¾ An intelligent pilot decision support tool, AALA, is proposed to facilitate realization of SATS HVO at nontowered, not-radar airports. ¾ Realization of GA Free Flight and SATS pose major challenges for future intelligent cockpit system design. Rong, Ding, Valasek 2003-5774- 22 Aerospace Engineering
