By Wolfram Burgard, Cyrill Stachniss, Dirk Hähnel (auth.), Christian Laugier, Raja Chatila (eds.)
The function of this e-book is to deal with the not easy challenge of self sustaining Navigation in Dynamic Environments, and to offer new principles and methods during this newly rising technical area. The publication surveys the cutting-edge, discusses intimately a variety of comparable not easy technical features, and addresses upcoming applied sciences during this box. the purpose of the booklet is to set up a origin for a large type of cellular robotic mapping and navigation methodologies for indoor, outdoors, 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 global knowing and modelling for secure navigation, main issue avoidance and movement making plans in dynamic environments, and Human-robot actual interactions. a number of types and ways are proposed for fixing difficulties similar to Simultaneous Localization and Mapping (SLAM) in dynamic environments, cellular situation detection and monitoring, international nation estimation and movement prediction, secure navigation in dynamic environments, movement making plans in dynamic environments, strong choice making below uncertainty, and Human-Robot actual interactions.
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Extra info for Autonomous Navigation in Dynamic Environments
L. Hingorani. An efficient implementation of reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Transactions on PAMI, 18(2):138–150, February 1996. 12. F. Dellaert, D. Fox, W. Burgard, and S. Thrun. Monte carlo localization for mobile robots. In Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), Leuven, Belgium, 1998. 13. G. Dissanayake, H. Durrant-Whyte, and T. Bailey. A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem.
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 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages 521–526, Lausanne, Switzerland, 2002. 5. V. Barnett and T. Lewis. Outliers in Statistical Data.
This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: Characterization and motion prediction of the observed moving entities using bayesian programming; On-line goaloriented navigation decisions using the Partial Motion Planning (P M P ) paradigm. 1 Outline of the Problem To some extent, autonomous navigation for robotic systems placed in stationary environments is no longer a problem.
Autonomous Navigation in Dynamic Environments by Wolfram Burgard, Cyrill Stachniss, Dirk Hähnel (auth.), Christian Laugier, Raja Chatila (eds.)