By Wolfram Burgard, Cyrill Stachniss, Dirk Hähnel (auth.), Christian Laugier, Raja Chatila (eds.)
The goal of this booklet is to handle the tough challenge of self sufficient Navigation in Dynamic Environments, and to offer new rules and ways during this newly rising technical area. The publication surveys the state of the art, discusses intimately a variety of similar difficult technical facets, and addresses upcoming applied sciences during this box. the purpose of the publication is to set up a starting place for a huge type of cellular robotic mapping and navigation methodologies for indoor, open air, and exploratory missions.
Three major subject matters situated at the innovative of the cutting-edge are addressed, from either the theoretical and technological element of perspectives: Dynamic international knowing and modelling for secure navigation, quandary avoidance and movement making plans in dynamic environments, and Human-robot actual interactions. numerous versions and techniques are proposed for fixing difficulties resembling Simultaneous Localization and Mapping (SLAM) in dynamic environments, cellular crisis detection and monitoring, international nation estimation and movement prediction, secure navigation in dynamic environments, movement making plans in dynamic environments, powerful selection making lower than uncertainty, and Human-Robot actual interactions.
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Additional resources for Autonomous Navigation in Dynamic Environments
Learning hierarchical object maps of non-stationary environments with mobile robots. In Proc. of the Conf. on Uncertainty in Artificial Intelligence (UAI), 2002. 3. D. Anguelov, D. Koller, E. Parker, and S. Thrun. Detecting and modeling doors with mobile robots. In Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), pages 3777–3774, New Orleans, LA, USA, 2004. 4. D. Avots, E. Lim, R. Thibaux, and S. Thrun. A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments.
In Proc. of the Int. Conf. on Artificial Intelligence (IJCAI), 2001. 29. D. Schulz and W. Burgard. Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems, 34(2-3):107–115, 2001. 30. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6(2), 1978. 31. H. Shatkay. Learning Models for Robot Navigation. PhD thesis, Computer Science Department, Brown University, Providence, RI, 1998. 32. R. O. Arras, S. Bouabdallah, D. Burnier, G.
Morales, and E. Sucar. A hybrid approach to solve the global localozation problem for indoor mobile robots considering sensor’s perceptual limitations. In Proc. of the Int. Conf. on Artificial Intelligence (IJCAI), 2001. 29. D. Schulz and W. Burgard. Probabilistic state estimation of dynamic objects with a moving mobile robot. Robotics and Autonomous Systems, 34(2-3):107–115, 2001. 30. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6(2), 1978. 31. H. Shatkay. Learning Models for Robot Navigation.
Autonomous Navigation in Dynamic Environments by Wolfram Burgard, Cyrill Stachniss, Dirk Hähnel (auth.), Christian Laugier, Raja Chatila (eds.)