This paper proposes an efficient greedy algorithm to estimate the object shape within a limited number of sample data (i.e. the touch-down points). Specifically, we treat the object shape as an implicit surface which is defined as the zero levelset of an unknown function and apply Gaussian process to estimate the surface. The mutual information criteria is utilized to decide which point should be sampled in the next iteration. To expedite the estimation process, we implement sequential Gaussian process for computational efficiency and significantly reduce the computational cost by selecting search area based on the estimated level-set variance. We present some simulation results to compare the performance of the proposed algorithm with the random sampling algorithm and demonstrate the improvements in both speed and accuracy over the random sampling algorithm.
|Title of host publication||Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control|
|Publisher||American Society of Mechanical Engineers|
|Publication status||Published - 2016|
|Event||ASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States|
Duration: 2016 Oct 12 → 2016 Oct 14
|Name||ASME 2016 Dynamic Systems and Control Conference, DSCC 2016|
|Other||ASME 2016 Dynamic Systems and Control Conference, DSCC 2016|
|Period||16/10/12 → 16/10/14|
Bibliographical notePublisher Copyright:
Copyright © 2016 by ASME.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Mechanical Engineering