Probablistic robotics is a growing area in the subject, concerned with perception and control in the face of uncertainty and giving robots a level of robustness in real-world situations. This book introduces techniques and algorithms in the field.
This book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot?
Example 9.17(A Deterministic Nash Equlibrium) Consider the game specified by the cost matrices A and B: (9.68) By applying (9.66) and (9.67), or by using the patterns in Figure 9.5, it can be seen that u = 3 and v = 1 is a Nash ...
arXiv:1905.06113 Trautman, P., Krause, A.: Unfreezing the robot: Navigation in dense, interacting crowds. ... Springer International Publishing, Cham (2016) Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., ...
The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details.
To encourage further engagement, experimentation, and course and lesson design, The Robotics Primer is accompanied by a free robot programming exercise workbook that implements many of the ideas on the book on iRobot platforms.
This second edition is a major expansion and reorganization of the first edition, reflecting the dramatic advances made in AI over the past fifteen years.
This book contains the proceedings of the 9th WAFR, held on December 13-15, 2010 at the National University of Singapore. The 24 papers included in this book span a wide variety of topics from new theoretical insights to novel applications.
A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.
Introduction -- Math fundamentals -- Numerical methods -- Dynamics -- Optimal estimation -- State estimation -- Control -- Perception -- Localization and mapping -- Motion planning
A modern and unified treatment of the mechanics, planning, and control of robots, suitable for a first course in robotics.