| 1. |
- Hajieghrary, Hadi, 1983, et al.
(författare)
-
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation
- 2022
-
Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2022-August, s. 1009-1016
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator's joint variables while carrying an unspecified heavy load and an abrupt base movement occurs.
|
|
| 2. |
- Hajieghrary, Hadi, 1983, et al.
(författare)
-
Bayesian Optimization based Nonlinear Adaptive PID Design for Robust Control of the Joints at Mobile Manipulators
- 2022
-
Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; , s. 1009-1016
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator’s joint variables while carrying an unspecified heavy load and an abrupt base movement occurs.
|
|
| 3. |
- Murvanidze, Zuka, et al.
(författare)
-
Enhanced GPIS Learning Based on Local and Global Focus Areas
- 2022
-
Ingår i: IEEE Robotics and Automation Letters. - 2377-3766. ; 7:4, s. 11759-11766
-
Tidskriftsartikel (refereegranskat)abstract
- Implicit surface learning is one of the most widely used methods for 3D surface reconstruction from raw point cloud data. Current approaches employ deep neural networks or Gaussian process models with the trade-offs across computational performance, object fidelity, and generalization capabilities. We propose a novel method based on Gaussian process regression to build implicit surfaces for 3D surface reconstruction (GPIS), which leads to better accuracy in comparison to the standard GPIS formulation. Our approach encodes local and global shape information from the data to maintain the correct topology of the underlying shape. The proposed pipeline works on dense, sparse, and noisy raw point clouds and can be parallelized to improve computational efficiency. We evaluate our approach on synthetic and real point cloud datasets obtained from laser scans, synthetic CAD objects and robot visual and tactical sensors. Results show that our approach leads to high accuracy compared to baselines.
|
|
| 4. |
- Tekden, Ahmet Ercan, 1994, et al.
(författare)
-
Affordance Transfer based on Self-Aligning Implicit Representations of Local Surfaces
- 2022
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objects we interact with and manipulate often share similar parts, e.g. handles, that allow us to transfer our actions flexibly due to their shared functionality. This corresponds to affordances, i.e. set of action possibilities offered by the environment [1]. In this work, we propose to learn affordances associated with implicit models of local shapes shared across object categories. Our approach takes an expert grasp demon- stration on a given object, extracts the local geometry, and uses it as an anchor to align corresponding parts of objects from the same category. We show that the proposed implicit representation method can align objects within the same category under random pose perturbation. In addition, our general approach can align the local geometry to find grasp poses similar to the one demonstrated in the reference local shape. Finally, we show that we can identify the shared local geometry on novel objects from a different object category for affordance transfer.
|
|
