A. Sankaranarayanan and M. Vidyasagar. Path planning for moving a point object amidst unknown obstacles in a plane: The universal lower bound on the worst case path lengths, and a classification of algorithms. In Proceedings of IEEE Conference on Robotics and Automation, pages 1734--1741, December 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....touch and vision, that have been studied in literature. 4 Class Subclassification Representative References Class A maze searching Shannon s mouse [79] Tarry and Tremaux [57] Fraenkel [27] Pledge algorithm [1] touch sensor Lumelsky [45] Cox and Yap [18] Sankaranarayanan and Vidyasagar [73] continuous vision Sutherland [79] Lumelsky et al. [50] Lumelsky and Skewis [51] discrete vision Rao [61] Choo et al. [15] Foux et al. 26] Class B searching in plane Baeza Yates [3] Kao et al. [38] figure of merit Papadimitriou and Yannakakis [58] Blum et al. [5] Bar Eli et al. [4] Deng ....

....algorithms based on such sensors have been extensively studied by Lumelsky [45] and by many other researchers. Early use of touch sensors goes back to the Pledge algorithm [1] Some of the more recent works based on these sensors are due to Cox and Yap [18] and Sankaranarayanan and Vidyasagar [72, 71, 73]. B) Vision Sensors: A vision sensor typically provides the information visible to the robot; there are two basic characterizations of a vision sensor: continuous and discrete sensors. As the robot navigates along a path, a continuous sensor can detect all parts of the terrain that are visible. ....

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A. Sankaranarayanan and M. Vidyasagar. Path planning for moving a point object amidst unknown obstacles in a plane: The universal lower bound on the worst case path lengths, and a classification of algorithms. In Proceedings of IEEE Conference on Robotics and Automation, pages 1734--1741, December 1991.

....algorithms based on such sensors have been extensively studied by Lumelsky [45] and by many other researchers. Early use of touch sensors goes back to the Pledge algorithm [1] Some of the more recent works based on these sensors are due to Cox and Yap [18] and Sankaranarayanan and Vidyasagar [72, 71, 73]. B) Vision Sensors: A vision sensor typically provides the information visible to the robot; there are two basic characterizations of a vision sensor: continuous and discrete sensors. As the robot navigates along a path, a continuous sensor can detect all parts of the terrain that are visible. ....

....treatment including a general lower bound and correctness proofs of the algorithms of Lumelsky and his associates is given in [45] 4. 2 Sankaranarayanan s Algorithms Several extensions to the early versions of Lumelsky s algorithms have been proposed by Sankaranarayanan and Vidyasagar [72, 71, 73] and Sankaranarayanan and Masuda 13 H H S T L L L 1 1 2 2 3 3 H Figure 8: Execution of algorithm Alg1 [70] these algorithms lead to generalized solutions. We discuss Alg1 and Alg2 of [71, 72] and Curve1 of [70] in this section. As a nice intermediary between the algorithms Bug1 and ....

[Article contains additional citation context not shown here]

A. Sankaranarayanan and M. Vidyasagar. Path planning for moving a point object amidst unknown obstacles in a plane: A new algorithm and a general theory for algorithm developments. In Proceedings of IEEE Conference on Decision and Control, pages 1111--1119, December 1990.

....the obstacles or on the environment. Keywords: Cell Decomposition, Computational Geometry, Framed Octrees, Octrees, Robotics, Shortest Paths 1 1 Introduction Planning collision free paths of shortest distances through a known two dimensional (2 D) environment has been studied intensively [1, 2, 5, 10, 19, 23, 24, 26, 27, 31, 37]. The shortest path planning problem becomes significantly harder in a three dimensional (3 D) environment or when the environment is not completely known a priori [9 11, 18, 39] In practical applications, it is often the case that the environmental information is not completely known when a ....

A. Sankaranarayanan and M. Vidyasagar. Path planning for moving a point object amidst unknown obstacles in a plane: The universal lower bound on worst case path lengths and a classification of algorithms. Proceedings of the IEEE International Conference on Robotics and Automation, pages 1734--1741, 1991.

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