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Hybrid Systems in Robotics

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6 Author(s)
Jerry Ding ; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1770, USA. ; Jeremy H. Gillula ; Haomiao Huang ; Michael P. Vitus
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Robotics has provided the motivation and inspiration for many innovations in planning and control. From nonholonomic motion planning [1] to probabilistic road maps [2], from capture basins [3] to preimages [4] of obstacles to avoid, and from geometric nonlinear control [5], [6] to machine-learning methods in robotic control [7], there is a wide range of planning and control algorithms and methodologies that can be traced back to a perceived need or anticipated benefit in autonomous or semiautonomous systems.

Published in:

IEEE Robotics & Automation Magazine  (Volume:18 ,  Issue: 3 )