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Paper TuP209.6

Maeda, Guilherme Jorge (The University of Sydney), Singh, Surya (University of Sydney), Durrant-Whyte, Hugh (The University of Sydney)

A Tuned Approach to Feedback Motion Planning with RRTs under Model Uncertainty

Scheduled for presentation during the Regular Sessions "Motion and Path Planning II" (TuP209), Tuesday, May 10, 2011, 16:40−16:55, Room 5D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Motion and Path Planning, Motion Control of Manipulators, Robust/Adaptive Control of Robotic Systems

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.

 

 

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