G. Dudek and M. Jenkin. Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge, 2000.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....field of computer vision continues to play an important role in the development of autonomous robotic agents. Autonomous navigating robots should ideally be able to move through an unknown environment unaided. This involves path planning, obstacle detection, and scene recognition interpretation [18]. Such tasks are all dependent on the robot s ability to quickly build sufficiently complete models of its environment. Thus, surface reconstruction remains an important motivator in the field of computer vision. The human visual system provides consistent proof that 2 D image sensing is ....

....monocular observer. The relationship between subsequent still images in a video stream provides a wealth of information in the form of spatio temporal change. The temporal integration of such velocity fields is essential for solving shape from motion [6, 16, 13, 42, 46, 54, 70] timeof collision [18], object tracking [51] object recognition [4] and figure ground separation problems. At first glance the problem of 3 D reconstruction from motion images seems trivial as it is intuitively sound to suggest that changes in intensity on an image plane are somewhat coupled with the projection of ....

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Dudek, G. and Jenkins, M., Computational Principles of Mobile Robotics, Cambridge University Press, Cambridge, 2000.

....and land survey or reconnaissance. Each case study covers strategies implemented on wheeled robot research prototypes designed for field operations. 1 Introduction Hard computing methods which address robot perception and control issues rely upon strong mathematical modeling and analysis [1,2]. The various approaches proposed to date are suitable for control of industrial robots and automatic guided vehicles that operate in structured environments and perform relatively well defined repetitive tasks, such as manipulator positioning or tracking fixed pre programmed trajectories. ....

....surfaces such as paved roadways. For the lat ter case (as with indoor environments) the motion controls for robot mobility Tunstel et al. systems can often be designed based on linear system models, or simpified nonlinear models of vehicle kinematics or dynamics assuming motion on a 2 D plane [2]. Practical mobility control systems for use in outdoor rugged terrain must account for a wider array of real world complexities [6] the most fundamental of which is the fact that complex motions in the third dimension occur quite frequently. The situation is similar for rovers that interact with ....

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Dudek, G., Jenkin, M. (2000): Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge, UK

....and accumulated. Triangulation techniques were also used, where various sensors were used to extract landmarks from the environment and the landmarks triangulated the robot s positions. This works when the sensors are reliable and relatively noise free but leaves several problems unaddressed [6]. Kalman filters were also introduced to the problem of determining position [21, 17] where various sensor data are fused to obtain a new position estimate. This method relies on a linearity assumption or on 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Probability of Registering Strength 0 ....

G. Dudek and M. Jenkins. Computational Principles of Mobile Robotics. Cambridge University Press, 2000.

....become unstable for many positions and landmark arrangements and lead to significant loss of precision. Typically, multiple measurements are merged over time to try to compensate for this, however some care must be taken in choosing the method of merging or poor results will be obtained [11]. In some cases where the sensors are fairly reliable and have simple noise distributions, direct triangulation or triangulation with differential windowing can produce excellent results. Noisy sensors, however, complicate triangulation adding uncertainty to the results. GPS [29] is perhaps the ....

G. Dudek and M. Jenkins. Computational Principles of Mobile Robotics. Cambridge University Press, 2000.

....triangulation become unstable for many positions and landmark arrangements and lead to significant loss of precision. Typically, multiple measurements are merged over time to try to compensate for this, however some care must be taken choosing the method of merging or poor results will be obtained [11]. In some cases where the sensors are fairly reliable and have simple noise distributions, direct triangulation or triangulation with differential windowing can produce excellent results. GPS [27] is based on triangulation. Noisy sensors, however, complicate triangulation adding uncertainty to ....

G. Dudek and M. Jenkins. Computational Principles of Mobile Robotics. Cambridge University Press, 2000.

....information over larger ranges, it is not without its problems. For example, it cannot be used in environments in which no clear line of sight exists to the satellites such as indoors, near mountain ranges, in forests, and builtup urban areas. It also has a relatively slow update rate. See [4] for a description of GPS and its limitations. That being said, GPS can be used to correct drift in other sensors. Various alternatives have been proposed to extend the range over which an immersive visual display can operate. Perhaps the most extreme version of these has been the development of ....

....In order to update the rider s position with respect to the world it is necessary to construct a kinematic model of the vehicle. Fortunately bicycles and tricycles have a straightforward kinematic model. Full details of the kinematic models of these and other wheeled vehicles can be found in [4] but the basic concept is sketched here. 2 Front View Side View Figure 1. The TRIKE rideable computer x y ICC d # Figure 2. Bicycle tricycle kinematics. The shaded ovals represent the three wheels of Trike. The instantaneous centre of curvature (ICC) must lie at the intersection of ....

G. Dudek and M. Jenkin. Computational Principles of Mobile Robotics. Cambridge University Press, New York, NY, 1999.

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G. Dudek and M. Jenkin. Computational Principles of Mobile Robotics. Cambridge University Press, 2000.

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G. Dudek and M. Jenkin. Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge, 2000.

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Dudek, G., Jenkin, M.: Computational principles of mobile robotics. Cambridge University Press (2000)

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G. Dudek and M. Jenkin. Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge, UK, 2000.

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Dudek, G. and Jenkins, M.: Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge, 2000.

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