The complexity of robot motion planning
The complexity of robot motion planning
Robot Motion Planning
Controlled passive dynamic running experiments with the ARL-monopod II
IEEE Transactions on Robotics
Randomized algorithms for robust controller synthesis using statistical learning theory
Automatica (Journal of IFAC)
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The paper suggests a new approach to navigation of mobile robots, based on nonlinear model predictive control and using a navigation function as a control Lyapunov function. In this approach, the nonlinear optimal control problem is treated using randomized algorithms. The advantage of the proposed combination of navigation functions for robot motion planning with randomized algorithms within an MPC framework, is that the control design offers stability by design, is platform independent, and allows the designer to trade-off performance for (computation) speed, according to the application requirements.