Rough Terrain Autonomous Mobility—Part 1: A Theoretical Analysis of Requirements
Tóm tắt
A basic requirement of autonomous vehicles is that of guaranteeing the safety of the vehicle by avoiding hazardous situations. This paper analyses this requirement in general terms of real-time response, throughput, and the resolution and accuracy of sensors and computations. Several nondimensional expressions emerge which characterize requirements in canonical form. The automatic generation of dense geometric models for autonomously navigating vehicles is a computationally expensive process. Using first principles, it is possible to quantify the relationship between the raw throughput required of the perception system and the maximum safely achievable speed of the vehicle. We derive several useful expressions for the complexity of terrain mapping perception under various assumptions. All of them can be reduced to polynomials in the response distance. The significant time consumed by geometric perception degrades real-time response characteristics. Using our results, several strategies of active geometric perception arise which are practical for autonomous vehicles and increasingly important at higher speeds.
Tài liệu tham khảo
Bajcsy, R. 1988. Active perception. In Proc. IEEE, Vol. 76, No.8, pp. 996-1005.
Bekker, M.G. 1956. The Theory of Land Locomotion, The University of Michigan Press.
Bekker, M.G. 1960. Off-the-Road Locomotion, The University of Michigan Press.
Blochl, B. 1994. System requirements and architectures for vision-guided autonomous robots. In Proc. of the 12th European meeting on Cybernetics and Systems Research, Vienna, Austria, pp. 5-8.
Brumitt, B., Coulter, R.C., and Stentz, A. 1992. Dynamic trajectory planning for a cross-country navigator. In Proc. of the SPIE Conference on Mobile Robots, Boston, MA.
Chang, T.S., Qui, K., and Nitao, J.J. 1986. An obstacle avoidance algorithm for an autonomous land vehicle. In Proc. of the SPIE Conference on Mobile Robots, pp. 117-123.
Daily, M. et al. 1988. Autonomous cross country navigation with the ALV. In Proc. of the IEEE International Conference on Robotics and Automation, Philadelphia, PA, pp. 718-726.
Dickmanns, E.D. 1988. Dynamic computer vision for mobile robot control. In Proc. of the 19th International Symposium and Exposition on Robots, pp. 314-327.
Dickmanns, E.D. and Zapp, A. 1986. A curvature-based scheme for improving road vehicle guidance by computer vision. In Proc. of the SPIE Conference on Mobile Robots.
Dixon, J.C. 1988. Linear and non-linear steady state vehicle handling. In Proc. of Instn. Mechanical Engineers, Vol. 202, No.D3.
Dunlay, R.T. and Morgenthaler, D.G. 1986. Obstacle avoidance on roadways using range data. In Proc. of SPIE Conference on Mobile Robots, Cambridge, MA.
Feng, D., Singh, S., and Krogh, B. 1990. Implementation of dynamic obstacle avoidance on the CMU navlab. In Proc. of IEEE Conference on Systems Engineering.
Franke, U., Fritz, H., and Mehring, S. 1991. Long distance driving with the Daimler-Benz autonomous vehicle VITA. In PROMETHEUS Workshop, Grenoble, France.
Gowdy, J., Stentz, A., and Hebert, M. 1990. Hierarchical terrain representation for off-road navigation. In Proc. SPIE Mobile Robots.
Grandjean, P. and Matthies, L. 1993. Perception control for obstacle detection by a cross-country rover. In Proc. of the IEEE International Conference on Robotics and Automation.
Hebert, M., Kanade, T., and Kweon, I. 1988. 3-D vision techniques for autonomous vehicles. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Technical Report CMU-RI-TR-88-12.
Hebert, M. and Krotkov, E. 1991. Imaging laser radars: How good are they? IROS.
Hebert, M., Thorpe, C., and Stentz, A. (Eds.) 1997. Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon, Kluwer Academic: Boston, MA.
Horn, B.K. and Harris, J.G. 1991. Rigid body motion from range image sequences. Image Understanding, 53(1):1-13.
Hotz, B., Zhang, Z., and Fua, P. 1993. Incremental construction of local DEM for an autonomous planetary rover. Workshop on Computer Vision for Space Applications, Antibes.
Keirsey, D., Payton, D., and Rosenblatt, J. 1988. Autonomous navigation in cross-country terrain. In Proc. of Image Understanding Workshop.
Kelly, A. 1995. An intelligent predictive control approach to the high-speed, cross country autonomous navigation problem. Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
Kelly, A., Stentz, A., and Hebert, M. 1992. Terrain map building for fast navigation on rough terrain. In Proc. of the SPIE Conference on Mobile Robots, Boston, MA.
Kelly, A., Stentz, A., and Hebert, M. 1998. Rough terrain autonomous mobility: Part 2: An active vision, predictive control approach. Autonomous Robots, 5(2).
Keuthe, A.M. and Chow, C.W. 1950. Foundations of Aerodynamics, Wiley: New York, NY.
Krotkov, E. and Hebert, M. 1995. Mapping and positioning for a prototype lunar rover. In Proc. of the International Conference on Robotics and Automation.
Kuan, D.T. and McTamaney, L.S. 1987. Autonomous land vehicle development at FMC. In Proc. of 3rd Annual AI and Advanced Computer Technology Conference, Long Beach, CA.
Kweon, I.S. 1990. Modelling rugged terrain by mobile robots with multiple sensors. Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
Matthies, L. 1992. Stereo vision for planetary rovers. International Journal of Computer Vision, 8(1):71-91.
Matthies, L. and Grandjean, P. 1994. Stochastic performance modelling and evaluation of obstacle detectability with imaging range sensors. IEEE Transactions on Robotics and Automation, 10(6):783-791.
Matthies, L., Kelly, A., and Litwin, T. 1995. Obstacle detection for unmanned ground vehicles: A progress report. 7th International Symposium on Robotics Research, Munich, Germany.
Olin, K. and Tseng, D. 1991. Autonomous cross-country navigation. IEEE Expert, 16-30.
Shin, D.H., Singh, S., and Shi, W. 1991.A partitioned control scheme for mobile robot path planning. In Proc. IEEE Conference on Systems Engineering, Dayton, Ohio.
Singh, S. et. al. 1991. Fast Nav: A system for fast navigation. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Technical Report CMU-RI-TR-91-20.
Spofford, J. and Gothard, B. 1994. Stopping distance analysis of ladar and stereo for unmanned ground vehicles. In Proc. of the International Society of Optical Engineering, Mobile Robots IX, Boston, MA.