A Tuned Approach to Feedback Motion Planning with RRTs under Model Uncertainty. Maeda, G. J., Singh, S. P. N., & Durrant-Whyte, H. 2011.
doi  abstract   bibtex   
Model uncertainty complicates most kinodynamic motion planning and control approaches due to their reliance on accurate forward prediction. If the model uncertainty is significant, a generated path or control strategy based on forward simulation of this model is potentially invalid and expensive to track (if possible). This paper explores the use of system identification/estimation to tune model parameters. Framed as an extension to rapidly exploring random tree (RRT) methods, it updates the model so that reachable actions added to the tree have more fidelity. This can be viewed as a mixture of a model predictive control (MPC) for local planning with an approximate-model global planner providing sub-goals and thus overcoming the limited lookahead caused by model uncertainty. The benefits of this approach are illustrated for a 3 DOF serial manipulator controlled by computed torque control operating under large external disturbances. In this case, the approach provides operation under intermittent feedback and disturbance observation. Tracking and actuator utilization are also improved over solutions found via conventional methods.
@CONFERENCE{icra11.gjm,
  author = {Guilherme J. Maeda and Surya P. N. Singh and Hugh Durrant-Whyte},
  title = {A Tuned Approach to Feedback Motion Planning with RRTs under Model
	Uncertainty},
  booktitle = {International Conference on Robotics and Automation},
  year = {2011},
  pages = {2288-2294},
  abstract = {Model uncertainty complicates most kinodynamic motion planning and
	control approaches due to their reliance on accurate forward prediction.
	If the model uncertainty is significant, a generated path or control
	strategy based on forward simulation of this model is potentially
	invalid and expensive to track (if possible). This paper explores
	the use of system identification/estimation to tune model parameters.
	Framed as an extension to rapidly exploring random tree (RRT) methods,
	it updates the model so that reachable actions added to the tree
	have more fidelity. This can be viewed as a mixture of a model predictive
	control (MPC) for local planning with an approximate-model global
	planner providing sub-goals and thus overcoming the limited lookahead
	caused by model uncertainty. The benefits of this approach are illustrated
	for a 3 DOF serial manipulator controlled by computed torque control
	operating under large external disturbances. In this case, the approach
	provides operation under intermittent feedback and disturbance observation.
	Tracking and actuator utilization are also improved over solutions
	found via conventional methods.},
  doi = {10.1109/ICRA.2011.5979834},
  pdf = {ICRA2011.0649.pdf}
}

Downloads: 0