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Navigation Team Indy Robotics Racing. Mission Inputs from sensors –GPS, heading, distance, speed, time Compute position using multiple methods –DR (primary)

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Presentation on theme: "Navigation Team Indy Robotics Racing. Mission Inputs from sensors –GPS, heading, distance, speed, time Compute position using multiple methods –DR (primary)"— Presentation transcript:

1 Navigation Team Indy Robotics Racing

2 Mission Inputs from sensors –GPS, heading, distance, speed, time Compute position using multiple methods –DR (primary) –GPS (secondary – primary for correction) –Visual (secondary – secondary for correction) Determine when DR position correction is appropriate Output –Current position –Vector to next waypoint –CDI (course deviation indication) –Distance traveled, distance remaining –Time elapsed, time remaining – ETE (est time enroute), ETA at projected speed – Avg speed, +/- from planned speed, avg speed to complete, +/- from planned

3 Definitions CDI=Course Deviation Indication=lateral distance off centerline course ETA=Estimated Time of Arrival at the finish line ETE=Estimated Time Enroute to the finish=sum of remaining leg times ATE=Actual Time Enroute for the course traversed to date Expected time=sum of reference leg times traversed to date

4 What questions does the navigator answer? Where are we? –Lat/long/error estimate –CDI (course deviation indicator = lateral distance offset from current vector) Where do we go next? –Direct course from current position to next waypoint –Next vector: Heading, distance, reference speed from nxt wpt to nxt wpt+1 –Second vector: Heading, distance, reference speed from nxt wpt+1 to nxt wpt+2 How are we doing? –Elapsed time, elapsed time expected, delta –Average speed, expected average speed, delta –Distance traveled, distance to go –Time remaining to deadline, estimated travel time remaining, delta –ETA, delta to deadline –Average speed required, projected avg speed, delta

5 Position definition Primary method: Dead Reckoning (internal reference calculations) –Computes position 4 times per second –Heading-speed or heading-distance as input Error correction: Progressive error of DR is corrected from GPS, inertial navigation, other –No time delay if external reference systems are lost or delayed

6 Data Flow Dead Reckoning Calculator Current Position/error CDI Next Wpt Vector Second Wpt vector ETE/ETA Avg/Required speed Wpt DB Past Position DB Heading, distance, speed, time GPS Position Position and error comparator Current position Correct position Current position

7 Error Correction Process Error calculator reports that at time t-x the position determined was in error. In the past position database, the position at time t-x is corrected All subsequent DR vectors and end positions are recalculated and updated in the past position database DR calculator re-computes the output series

8 Sensors: –GPS (Lat/Long/error): Navcom –Heading (magnetic course): Garmin – AHRS –Speed: TBD –Time: Internal clock –Distance: Odometer or other

9 Polling and Compute cycles GPS: 1 second (or device standard) Heading: 1 second intervals Speed: 1 second intervals Time: na Distance: 1 second DR Position computation: 4 Hz Error comparator: 1 minute

10 External Reference GPS Visual ID of waypoint Visual ID of surface features Visual or other ID of structures like towers. –Cellphone –Power grid

11 GPS Best accuracy in motion: assume 1 foot Update cycle: 1 second Messaging method:

12 Internal Position Reference

13 Heading Reference Garmin AHRS (primary) –Inertial reference for both heading and attitude (pitch and roll) Aircraft gyrocompass (secondary) –Slaved w/ magnetic correction for precession Open Issues –Initial reference setting prior to the race

14 Odometer Trailing or wheel based Other

15 Speed GPS speed input Direct speed input from the vehicle

16 Data Management Waypoint database and Past Position database Telemetry database during testing Ancelus v1.3/Linux Totally memory resident Fast –Lookups @ 5 mil TPS, insert/delete @ 360,000 tps. Minimum footprint –About 20kb + dataspace –Dataspace compression of about 5:1 vs typical relational. The price is right


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