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• W2181587980 abstract "his special issue on computational geometry in navi- gation and path planning presents a collection of state- of-the-art articles reporting on the unique aspects of topology-based approaches in robotics. This issue presents the applications of computational geometry in obstacle avoidance, path planning with minimum clearance, autonomous mobile robot navigation, exploration of unknown polygonal environ- ments, and it explores the practical relevance to the geographi- cal information systems, mobile networks, risk avoidance, security, games, and online system design. The six contribu- tions carefully selected for this special issue are connected by a common theme: the application of recent theoretical advances in computational geometry to the important robot- ics and path planning problems. The article by Ichiro Suzuki and Paweo _ Zylipresents a number of new ap- proaches to capturing an evader in a con- strained space. The authors provide several new strategies for a group of mobile robots on a three-dimensional grid such as a randomized algorithm for detecting an evader by one robot having the same maximum speed as the evader, a randomized algorithm for capturing an evader using two or more robots who can move slightly faster than the evader, and a determin- istic algorithm for capturing an evader under specific constraints. The article critically compares all the proposed algo- rithms on their efficiency and robustness and makes a unique contribution to the areas of pursuit game design, randomized algorithms, and mobile robot planning. The article by David Rawlinson and Ray Jarvis explores a challenging problem of teaching a robot to navigate directly to goals in unfamiliar or unknown environ- ments. Language allows people to exploit other people's knowl- edge in such situations, by transferring all necessary information from one person to the other, whereas what means can assist robots in doing the same is the question asked by this article. The proposed solution directs an autonomous robot using effi- cient and universal topological instructions that can be incre- mentally interpreted by a moving robot that does not initially have its own map of the environment. Many real-world experi- ments featuring autonomous exploration and mapping led to the remarkable conclusion that for this type of navigation, better object recognition capabilities are more important than better mapping capabilities. Another approach to autonomous robot navigation is pre- sented in the article by Chunlin Chen, Han-Xiong Li, and Daoyi Dong. In this work, the authors present a control method based on hierarchical Q-learning for mobile robot navigation in unknown environments. Hybrid control here refers to the integration of reactive control for local navigation and deliberative control for global navigation. The environ- ment is represented by grid-topological maps that are con- structed online during the process of learning to achieve the cooperative optimization of global and local naviga- tion control. The navigation controls of the mobile robots are implemented by extending Q-learning to a hierarchical setting based on the model of a hybrid Markov decision process. The simulated and real experi- mentation shows that the proposed ap- proach is capable of optimizing global navigation and avoiding the local min- imum trap, so that the method works well in unknown dynamic environ- ments and can be utilized as an effec- tive integrated control scheme for navigation." @default.
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• W2181587980 date "2008-01-01" @default.
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• W2181587980 title "Computational Geometry in Navigation and Path Planning" @default.
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