The ARVIN / R3EP Project

ARVIN is an acronym for Autonomous Robotic Vehicle INstrument, and R3EP is an acronym for Rapidly Reconfigurable Robotics Evaluation Platform. This project, funded by the National Science Foundation, provides for a flexible platform on which to experiment with sub- and super-systems on autonomous vehicles.

This project, developing a platform for research and training to serve as an instrument for evaluating robotic subsystem and supersystem performance, accelerates and enhances the evolution of autonomous vehicle subsystems and component parts by providing a baseline instrument to measure and assess the performance and capabilities of the various portions of a given autonomous vehicle design. This Autonomous Robotic Vehicle Instrument (ARVIN) is designed to allow simultaneous, parallel operation of multiple instruments, actuators, and software/hardware subsystems. Due to the common environment in which the items are operating, the ARVIN yields a precise, robust metric of comparative performance and capability of the item in question. Inherently rapidly reconfigurable by design of its network-centric architecture, the ARVIN enables quick, easy substitutions and augmentations of sensors and actuators on the evaluation platform; it is not a set of identical robot structures that join and reform to move or climb. The ARWIN tests, simulates, and validates subsystems and components in three tightly coupled hierarchical areas acknowledge as critical to advances in robotics vehicles: -Guidance, Navigation, and Control (GNC), -Sensor and Actuators Suites, and -Software and Network Architectures.

The Autonomous Robotic Vehicle Instrument consists of an Off-road vehicle, heavily modified, and a Hardware-in-the-Loop (HIL) simulator for this vehicle. The vehicle, based on the Overbot chassis, is a modified Polaris 6x6 Ranger, with an overhead gimbal mounted Sick LiDAR unit, Unibrain firewire color CCD camera, GPS/INS, Vorad radar, wheel encoders, and several custom actuators. In general, due to the incredibly high cost (in time and money) of constructing an autonomous vehicle of any kind, designs are rarely modified unless the mission cannot be completed by the original vehicle design. Very rarely are the initial trade-off studies revisited and explored to see what might have been done differently. Thus, the ARVIN should have profound impact on the kind of trade-off studies that most autonomous vehicle design programs have to-date been missing.

At this point, in 2008, the Overbot has become our de facto testbed platform, and further research is being conducted on the topics of obstacle detection and avoidance, path planning, and sensor fusion for a ground vehicle.

Broader Impact: ARVIN allows students and researchers to gain experience on an actual, physical, autonomous rover. This kind of practical training in systems integration, sensor fusion, software architecture for real-time systems, and actual control systems implementations has much to offer. The Naval Postgraduate School and the Intelligent Robotics Group (IRG) at NASA-Ames has indicated interest and enthusiasm in collaborating and experimenting with the modular chassis that will result of the ARVIN. Contributing to further understand trade-offs for mobile robotics, the platforms will also be used for outreach. The ARVIN infrastructure should become a cost-effective general robotics toll that may be easily replicated at other institutions.

National Science Foundation Award Link


  1. Video of Overbot made for during summer 2007 spine-based obstacle avoidance tests, shown at AIAA GNC conference 2008.

  2. Video of Overbot made for 2005 DARPA Grand Challenge, along with autonomous driver testing at UC Santa Cruz.


  1. (1)Choi, J., Curry, R., Elkaim, G., “Minimizing maximum curvature of quadratic Bézier curves with a tetragonal concave polygonal boundary constraint,” Journal of Computer Aided Design, Vol. 44, No. 4, April 2012, pp. 311-319, doi:10.1016/j.cad.2011.10.008 (pdf)

  2. (2)Choi, J., Curry, R., Elkaim, G., “Continuous Curvature Path Generation Based on Bezier Curves for Autonomous Vehicles,” IAENG International Journal of Applied Mathematics, Vol. 40, No. 2, May 2010, pp. 91-101. (pdf)

  1. (3)Choi, J., Curry, R., Elkaim, G., “Piecewise Bezier Curves Path Planning with Continuous Curvature Constraint for Autonomous Driving, Machine Learning and Systems Engineering,” Lecture Notes in Electrical Engineering 68, Springer Science+Business Media B.V., 2010. (pdf)

  1. (4)Choi, J., Curry, R., Elkaim, G., “Path Planning based on Bezier Curve for Autonomous Ground Vehicles,” in Proceedings of the Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008 (WCECS 2008), pp. 158-166, IEEE Computer Society 2009. (pdf)

  1. (5)Loh, J., Elkaim, G., “Roughness Map for Autonomous Rovers” American Control Conference, ACC13, Washington D.C., 17-19 June 2013, submitted (pdf)

  2. (6)Choi, J., Curry, R., Elkaim, G., “Curvature-Continuous Trajectory Generation with Corridor Constraint for Autonomous Ground Vehicles,” The 49th IEEE Conference on Decision and Control, CDC 2010, Atlanta, Georgia, USA, Dec. 15-17, 2010. (pdf)

  3. (7)Choi, J., Curry, R., Elkaim, G., “Real-Time Obstacle Avoiding Path Planning for Mobile Robots,” AIAA Guidance, Navigation and Control Conference, AIAA GNC 2010, Toronto, Ontario, Canada, Aug. 2-5, 2010. (pdf)

  4. (8)Choi, J., Curry, R., Elkaim, G., “Smooth Path Generation Based on Bezier Curves for Autonomous Vehicles,” World Congress on Engineering and Computer Science, WCECS 2009, San Francisco, CA, Oct. 20-22, 2009. (Best Student Paper) (pdf)

  5. (9)Choi, J., Curry, R., and Elkaim, G., “Collision Free Real-Time Motion Planning for Omnidirectional Vehicles,” European Control Conference, ECC’09, Budapest, Hungary, Aug. 23-26, 2009 (pdf)

  6. (10)Choi, J., Curry, R., and Elkaim, G., “Obstacle Avoiding Real-Time Trajectory Generation of Omnidirectional Vehicles,” American Control Conference, ACC 2009, St. Louis, MI, June 10-12, 2009, pp. 5510-5515, doi:10.1109/ACC.2009.5160683 (pdf)

  7. (11)Choi, J., Elkaim,G., “Bézier Curves for Trajectory Guidance,” World Congress on Engineering and Computer Science, WCECS 2008, San Francisco, CA, Oct. 22-24, 2008 (pdf)

  8. (12)Connors, J., and Elkaim, G., “Trajectory Generation and Control Methodology for an Autonomous Ground Vehicle,” AIAA Guidance, Navigation and Control Conference, AIAA GNC 2008, Honolulu, HI, Aug. 18-21, 2008 (pdf)

  9. (13)Connors, J., Elkaim,G., “Experimental Results for Spline Based Obstacle Avoidance of an Off-Road Ground Vehicle,” ION Global Navigation Satellite Systems Conference, ION GNSS 2007, Fort Worth, TX, Sept. 25-28, 2007, pp. 1484-1490 (pdf)

  10. (14)Connors, J., Elkaim, G., “Analysis of a Spline Based, Obstacle Avoiding Path Planning Algorithm,” IEEE Vehicle Technology Conference, IEEE VTC 2007, Dublin, Ireland, Apr. 22-25, 2007, pp. 2565-2569 (pdf)

  11. (15)Connors, J., Elkaim, G., “Manipulating B-Spline Based Paths for Obstacle Avoidance in Autonomous Ground Vehicles,” ION National Technical Meeting, ION NTM 2007, San Diego, CA, Jan. 22-24, 2007, pp. 1081-1088 (pdf)

  12. (16)Elkaim, G., Connors, J., and Nagel, J., “The Overbot: An Off-Road Autonomous Ground Vehicle Testbed,” ION Global Navigation Satellite Systems Conference, ION GNSS 2006, Fort Worth, TX, Sept. 22-24, 2006, pp. 1449-1456 (pdf)

  13. (17)Nagel, J., ‘The Overbot: DARPA Grand Challenge 2005 Technical Paper,” 2005. (pdf).

  14. (18)Bell, T., O’Connor, M., Elkaim, G., Parkinson, B., “Realistic Autofarming: Closed-Loop Tractor Control over Irregular Paths using Kinematic GPS,” ION Global Positioning System Conference, ION GPS 1998, Nashville, TN, Sept. 17-20, 1998, pp. 452-461 (pdf)

  15. (19)Elkaim, G., O’Connor, M., Parkinson, B., “System Identification and Robust Control of a Farm Vehicles using CDGPS,” ION Global Positioning System Conference, ION GPS 1997, Kansas City, MO, Sept. 16-19, 1997, pp. 1415-1426 (pdf)

  16. (20)O'Connor, M., Elkaim, G., Bell, T., Parkinson, B., “Real-Time CDGPS Initialization for Land Vehicles Using a Single Pseudolite,” ION National Technical Meeting, ION NTM 1997, Santa Monica, CA, Jan. 14-16, 1997, pp. 717-730 (pdf)

  17. (21)Elkaim, G., O'Connor, M., Bell, T., Parkinson, B., “System Identification of a Farm Vehicle Using Carrier-Phase Differential GPS,” ION Global Positioning System Conference, ION GPS 1996, Kansas City, MO, Sept. 17-20, 1996, pp. 485 - 494 (pdf)

  18. (22)O'Connor, M., Bell, T., Elkaim, G., Parkinson, B., “Automatic Steering of Farm Vehicles Using GPS,” 3rd International Conference on Precision Agriculture, Minneapolis, Minnesota, June 1996 (pdf)

  19. (23)O’Connor, M., Elkaim, G., Parkinson, B., “Kinematic GPS for Closed-Loop Control of Farm and Construction Vehicles,” ION Global Positioning System Conference, ION GPS 1995, Palm Springs, CA, Sept. 12-15, 1995, pp.1261-1268 (pdf)


  1. Gabriel Elkaim, Associate Professor, Computer Engineering, UCSC, 831.459.3054

  2. Ji-Wung “Karl” Choi, Ph.D. Student (graduated), now a postdoc at TUT, Finland.

  3. John Connors, Masters Student (graduated), CE UCSC, now at WrightSpeed

ARVIN/R3EP Project