Abstract
Mobile robots capable of moving autonomously in more or less structured environments are being increasingly employed in the automation of certain industrial processes. Along these lines, the authors constructed a platform, on the base of a commercial industrial truck, provided with sufficient autonomy to carry out tasks within an industrial environment (VIA: Autonomous Industrial Vehicle).
One of the sensor systems used in the truck is a system of artificial vision which enables it to move on asphalted surfaces both in open environments (roads) and closed ones, seeking the markings which most easily allow it to determine the path marked in the images. The system for following roads is capable of following painted lines, determining the sides of the road by texture analysis or determining the minimum width of the road for the robot to pass, according to the circumstances. A model of the road predicts its situation and enables a decision to be made on whether the information provided by the algorithm is reliable or not. At the same time, a neural network is trained with the results obtained by any of the previous algorithms, in such a way that when the training process converges the network takes over the steering of the truck.
The vision system, composed of a CCD colour camera and a “frame grabber” installed in a PCI slot of a Pentium 120 PC, provides a path every 100 ms, which allows the industrial truck to be steered at its maximum speed of 10 m/s.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Crisman, J.D. and Thorpe, C.E. 1988. Color vision for road following. In Proc. of Mobile Robots III.
Dickmanns, E.D., Behringer, R., Dickmanns, D., Hildebrant, T., Mauer, M., Thomanek, F., and Shielhlen, J. 1994. The seeing passenger car ‘VaMoRs-P’. In Proc. of Int. Symp. on Intelligent Vehicles, Paris.
ECHELON. 1993. LonWorks Technology: Intelligent Distributed Control.
Koller, D., Luong, T., and Jitendra, M. 1995. An integrated stereo-based approach to automatic vehicle guidance. In Proc. Int. Conference on Computer Vision, Boston.
Mazo, M. and Maravall, D. 1990. Guidance of an autonomous vehicle by visual feedback. Cybernetics and Systems, 21:257-266.
Mazo, M., Rodríguez, F.J., Lázaro, J.L., Ureña, J., García, J.J., and Santiso, S. 1995. Electronic control of a wheelchair guided by voice commands. Control Eng. Practice, 3(5):665-674.
Oelen, W., Berghuis, H., Nijmeijer, H., Canudas de Wit, C. 1995. Hybrid stabilizing control on a real mobile robot. IEEE Robotics & Automation Magazine, 2(2):16-23.
Pomerleau, D. 1993. Neural Network Perception for Mobile Robot Guidance, Kluwer: Boston.
Sarkar, N., Yun, X., and Kumar, V. 1994. Control of mechanical systems with rolling constraints: Application to dynamic mobile robots. Int. Journal of Robotics Research, 13(1).
Schneiderman, H. and Nashman, M. 1994. A discriminating feature tracker for vision based autonomous driving. IEEE Trans. on Robotics and Automation, 10(6):769-775.
Thorpe, C. 1990. Vision and Navigation: The Carnigie Mellon Navlab, Kluwer: Boston.
Turk, A.M., Morgenthaler, D., Gremban, D., and Marra, M. 1988. VITS—A vision system for autonomous land vehicle navigation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 10(3):342-361.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Rodríguez, F.J., Mazo, M. & Sotelo, M.A. Automation of an Industrial Fork Lift Truck, Guided by Artificial Vision in Open Environments. Autonomous Robots 5, 215–231 (1998). https://doi.org/10.1023/A:1008826306614
Issue Date:
DOI: https://doi.org/10.1023/A:1008826306614