Hybrid Systems in Robotics

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.

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[35]  Pieter Abbeel,et al.  Apprenticeship learning for helicopter control , 2009, CACM.

[36]  Claire J. Tomlin,et al.  Design of guaranteed safe maneuvers using reachable sets: Autonomous quadrotor aerobatics in theory and practice , 2010, 2010 IEEE International Conference on Robotics and Automation.

[37]  Paulo Tabuada,et al.  Approximately Bisimilar Symbolic Models for Incrementally Stable Switched Systems , 2008, IEEE Transactions on Automatic Control.

[38]  Sonia Martínez,et al.  Unicycle Coverage Control Via Hybrid Modeling , 2010, IEEE Transactions on Automatic Control.

[39]  Claire J. Tomlin,et al.  A differential game approach to planning in adversarial scenarios: A case study on capture-the-flag , 2011, 2011 IEEE International Conference on Robotics and Automation.

[40]  Claire J. Tomlin,et al.  Applications of hybrid reachability analysis to robotic aerial vehicles , 2011, Int. J. Robotics Res..

[41]  Claire J. Tomlin,et al.  Reachability-based synthesis of feedback policies for motion planning under bounded disturbances , 2011, 2011 IEEE International Conference on Robotics and Automation.

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