hasan arshad nasir - cyphynets · rrt algorithm for generating large set of candidate paths....
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Hasan Arshad Nasir
Motion Uncertainty.
Imperfect state information.
Quantify these uncertainties in advance.. Develop optimized path planning..
LQG-MP (Linear Quadratic Gaussian- Motion Planning).
RRT algorithm for generating large set of candidate paths.
Generating and Linearizing Dynamics and Observation model.
Kalman filter for optimal state estimation and LQR for optimal control.
Probability distribution of state and control input.
Based on above choose the best path.
RRT algorithm for generating large set of candidate paths…………….
Its “Rapidly Exploring Random Tree”. It’s the best for this context. Its probabilistic complete algorithm.
1) http://www.kuffner.org/james/plan/algorithm.php
Generating and LinearizingDynamics and Observation model.…………….
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Kalman filter for optimal state estimation and LQR for optimal control..…………….
Process update (prediction). Measurement update (update/filtering).
Cost function:
“lqr” command in MATLAB, generates feedback matrix.
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Red star = estimated stateGreen circle = true stateMagenta star = measured state
Probability distribution of state and control input........
Designing a planning objective and selecting the best path........
Path serial number Color Weight/Ratio/Priority
1 Red 2.769
2 Blue 2.8095
3 Black 2.872
4 Yellow 2.880
5 Green 2.795
Jur V.Berg, P. Abbeel, K. Goldberg, “LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information”. International Journal of Robotics, 2011.
Dr. Abubakr Muhammad. Talha Manzoor.
hasan.nasir@lums.edu.pk
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