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Radar Systems for Planetary Exploration

Radar Systems for Planetary Exploration. Mike Taylor taylor_michael_a5@cat.com. Perception in Offroad Environments. Offroad environment as well as robots themselves are very harsh on sensors and sensor performance. Radar Positives Impervious to rain, mud, fog, dust.

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Radar Systems for Planetary Exploration

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  1. Radar Systems for Planetary Exploration Mike Taylor taylor_michael_a5@cat.com

  2. Perception in Offroad Environments • Offroad environment as well as robots themselves are very harsh on sensors and sensor performance.

  3. Radar Positives Impervious to rain, mud, fog, dust. Few interference concerns Generally physically tough Radar Negatives Costly Wide beams Slow scan speeds Very hard to determine target size or shape False Alarms Why Radar? • Key Points: • Radar provides a generally robust sensing solution. • Sensor choice: push against technology or push against physics.

  4. Uses • Object detection • Terrain mapping • Object tracking • Sensor Fusion • Camera + radar • Automotive groups exploring this area • Laser + radar • Many robot systems use this. Boss, etc.

  5. Basics • Standard echo-location • Radar emits specific radio frequency and detects reflected waves • Separate transmit and receive antennas • Single transmit/receive antenna • Scan the beam to look in different directions • Air traffic control radar • Scanning determines the Field of View (FOV) • Air traffic control radar: 360° • Roving from North to East and back: 90°

  6. RF Propagation • Returned energy proportional to range-4 • Double the range, get only 1/16 the power returned • RF propagation on transmit: • Same amount of power would hit each target • Target 1: 1 W ·m-2 • Target 2: ¼ W ·m-2 • Double the range, ¼ the incident energy • P αrange-2 • Reflected energy suffers same degradation • Round trip: range-2 · range-2 = range-4 Target 2: 2 x Range 4 x Area 10 m 20 m

  7. Beam Shape • Beam shape is function of antenna • High gain vs. omni-directional antennas • Gain developed by interference • “Beam Width” estimates • 3 dB typical • Contains vast majority of energy • Relationships: • Beam Width α frequency -1 • Beam Width α (antenna width) -1 • Applies in both height and width

  8. Side Lobes • Result of same interference pattern that created the main beam. • Generally much weaker than main beam. • Objects receiving energy from side lobes can be detected. • Car off to right as we’re driving down the road. • Major issue for terrain mapping. • Affect confidence of detection. • See Alex Foessel’s PhD thesis for further discussion. Image from appolo.lsc.vsc.edu 10 m 20 m

  9. Beam Shape vs. Resolution • Beam width affects angular accuracy and ability to separate targets • Correlates to ‘resolution’ • Comparison to laser • Laser beam size: usually < 1° • Radar beam size: most 3 ° to 5° • Down sides to smaller beams: • Higher frequency: vegetation opacity & line of site • Larger antenna: hard to scan, larger form factor Can the truck fit through? Only one object reported with high angular error.

  10. Additional Specs: Detection range for certain objects Horizontal and vertical beam width Horizontal and vertical FOV Scan rate Number of targets per scan Range and angle resolution Radar Types

  11. Continental ARS-300 • Long range, dual mode ACC-style radar • Spinning cylindrical reflector • Grating on cylinder causes different interference patterns • Specs: • Long Range: 200 m, 17° • Mid Range: 60 m, 60° • Beamwidth ~3 degrees • Return limit varies by version • Reference Information: • Tartan Racing publications • Example of steered beam system • Unique antenna design • Emitted energy focused on a particular area • Prone to ‘ghost velocities’ • Far reaches of FOV have limitations

  12. Delphi ESR • Long range, dual mode ACC-style radar • Specs • Long range: 200 m • Medium range: 60 m • Return limit varies by version • ESR: Electrically-steered radar • Volvo S60: ESR + Mobileye camera • Launches in 2010 • Reference Information: • http://delphi.com/news/featureStories/fs_2008_06_02_001/ • Example of beam forming on return • Beam is not ‘steered’, wide emission pattern • Bearing calculated by phase difference between multiple receive antennas • Provides locations of returns above threshold • Limits available information for processing

  13. ACC Comments • Cheap, useful, feature-filled radars • Can be hard to acquire • Limited to manufacturer’s tools and code • Not tuned for offroad: • Incorrect thresholds • Improper motion models • Ghost Velocities • Imperfect noise handling • Wide beam angles • Good first step

  14. M/A-Com • Low cost, low range radar for collision prevention and blind spot coverage • Specs: • Single Mode • Range: 27 m • FOV: +100° • Limited returns • Particularly good at picking up moving objects • Reference Information: • http://www.macom.com/macom_prodnews.asp?ID=1094 • Example of Dipole Radar • Two receive antennas • Returns signals are compared to determine bearing • Potential ambiguity in bearing Path length difference determines bearing

  15. Angular Ambiguity • Simple dipole radars have a weakness: • Both objects below are at roughly the same range • Simple systems report seeing a single target along the centerline

  16. NavTech • Spin-off from ACFR • Specs • FMCW • 360 Degree FOV • 2 degree beam • 2.5 Hz • 0.03 meter range accuracy??? • 200 meter range • Initial models could not measure velocity • Reference Information: • http://www.nav-tech.com

  17. FMCW Quirk • Relative velocity causes vertical (frequency) shift in signal • Range causes horizontal (temporal) shift in signal • Up and down ramp allows separation of range and Dopper • Up: Delta = R + D • Down: Delta = R - D R + D R + D R - D R - D

  18. Other Suppliers • Research Houses (for semi-custom radars) • Militech • http://www.millitech.com/ • Epsilon Lambda • http://www.epsilonlambda.com/ • Manufacturers • Eaton-Vorad • http://www.roadranger.com/Roadranger/productssolutions/collisionwarningsystems/index.htm • Bosch http://rb-kwin.bosch.com/us/en/safety_comfort/driving_comfort/driverassistancesystems/ adaptivecruisecontrolacc/index.html

  19. Reflectivity and RCS • All objects reflect energy. Two questions: • How much? • In which direction? • Units: dBsm • Reflected power relative to one square meter of flat metal sheet • Human: -10 to 0 dBsm • Car: +10 dBsm • Energy reflected depends on • Material • Surface structure (clothing wrinkles) • Size…. • Shape- Specularity

  20. Radar Return vs. XY Position

  21. Radar Tuning Scene 16” Rock Senor Origin 6” Dia. Pipe

  22. Radar Return vs. XY Position

  23. Radar Return vs. XY Position

  24. Radar Target Amplitude Curves • Ground return is terrible • Objects are specular • “Coke can challenge” Key Trucks Human +Ground Noise Amp(dB) Range(m)

  25. Boss • Vehicle Tracking • Radar + Lidar Fusion • Direct velocity measurements key • Orientation is challenging • Veggie Cars

  26. Motion Free Scanning Radar (consortium with CMU) Motion Free Scanning Radar Sensor • Narrow beam • High reliability • Low cost • Small (30cm  x 20cm L) High Resolution Range Map:

  27. Cat AMT • Radar-based autonomous mining truck (AMT) circa 1995 • Millitech-developed 3D scanning FMCW radar • Multi-sensor AMT under development with CMU

  28. SSOD • SSOD: Slow Speed Obstacle Detection • Blind spot detection system • Option on some Caterpillar mining trucks • M/A-Coms compliment WAVS in-cab camera system • Turns off after short distance

  29. Researchers: ACFR • Australian Center for Field Robotics. • University of Sydney • Rare radar research group • Focused on mining applications • Semi-stationary terrain mapping • Assemble custom systems based on needs • Purchase and fabricate components • Develop own processing • Paper repository: • http://www.cas.edu.au/publications

  30. ACFR Radar Mapping • Stope fill monitoring • Filling large, mined out voids in underground mines • Visibility very limited • Fill monitoring as well • Beam width: 1.12° • 77 Ghz • 30 cm range resolution

  31. ACFR Radar Mapping • Drag-line Monitoring • Poor visibility limits productivity • Provides ‘situational awareness’ for operator • Terrain • Bucket • Ropes • Allows digging in “zero” vis

  32. Researchers: • Steve Shedding, ACFR • Former Postdoc at R.I • Working in interesting mobile terrain mapping and map fusion • Graham Brooker, ACFR • Major push behind designing new radar systems at ACFR • Alex Foessel • R.I. PhD, now at John Deere company • Research Houses • Millitech • Epsilon Lambda • NavTech • Automotive Suppliers

  33. Improvements • Lower Prices • Automotive industry: Delphi, Continental, Bosch • Improved performance • ACRF, automotive industry • Sensor fusion • Automotive, ACFR • Delphi: Volvo S60 + ESR + Mobileye • Velodyne for radar • ABM radar?

  34. Radar Layout Method • Calculate the number of radars required to cover all potential movement. • Vehicle specs: • Top speed • Minimum turning radius • Minimum deceleration • Calculate envelope • Radar specs: • Field of view • Detection Range • Depends on target • May vary with heading

  35. Radar Layout Method • Radar specs • 60 m range • 90° FOV • This radar has sufficient range but insufficient FOV. • Two radars will suffice

  36. Homework • Design a radar layout for a ground vehicle exploring a desert region • Given: • Two radars: • Option 1: ACC-style, $5,000. 60° FOV, 150 m range for vehicles. • Option 2: Raw data, $35,000. 90° FOV, 90 m range for vehicles. • Truck: • 12 meters long • Rear differential is 2 meters from rear of machine • 5 meters wide • Turning radius: 15 meter • Top speed: 12 m · s-1 (assume independent of turning radius) • Deceleration: 1.5 m · s-2 • Questions: • How many of each radar would you need to handle the vehicle? • Which radar would you choose? Write a short blurb on why. • Factors to consider: number of sensors, adjustability, cost, computing and personnel resources. • Assume your team is a typical CMU robotics team in the FRC with the normal skill sets, funding issues, and compressed timeline. There is no right answer- the key is going through the decision process and weighting each issue as you see fit. • Extra Credit • Which radar would work better for avoiding humans? Think about: • Ability to detect lower power returns • Ability to develop detection algorithms • Stopping distance

  37. References • Textbooks: Introduction to Radar Systems by Skolnik • http://search.barnesandnoble.com/Introduction-to-Radar-Systems/Merrill-I-Skolnik/e/9780070579095/?itm=4 • ACFR Publication Depot • http://www.cas.edu.au/publications • Overview of Delphi ACC systems including ESR Radar: • http://delphi.com/news/featureStories/fs_2008_06_02_001/ • M/A-Com • http://www.macom.com/macom_prodnews.asp?ID=1094 • NavTech • http://www.nav-tech.com • Boss / Urban Challenge Papers (Continental radar): • http://www.darpa.mil/GRANDCHALLENGE/TechPapers/Tartan_Racing.pdf • http://www.ri.cmu.edu/pub_files/pub4/darms_michael_2008_1/darms_michael_2008_1.pdf • http://www.tartanracing.org/press/boss-glance.pdf

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