Agrawal, A and Bhise, A and Arasanipalai, R and Tony, LA and Jana, S and Ghose, D (2023) Accurate Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method for High-speed Target Interception. [Book Chapter]
Full text not available from this repository.Abstract
Accurate estimation of trajectory is essential for the capture of any high-speed target. This chapter estimates and formulates an interception strategy for the trajectory of a target moving in a repetitive loop using a combination of estimation and learning techniques. An extended Kalman filter estimates the current location of the target using the visual information in the first loop of the trajectory to collect data points. Then, a combination of Recurrent Neural Network (RNN) with least-square curve-fitting is used to accurately estimate the future positions for the subsequent loops. We formulate an interception strategy for the interception of a high-speed target moving in a three-dimensional curve using noisy visual information from a camera. The proposed framework is validated in the ROS-Gazebo environment for interception of a target moving in a repetitive figure-of-eight trajectory. Astroid, Deltoid, Limacon, Squircle, and Lemniscates of Bernoulli are some of the high-order curves used for algorithm validation.
| Item Type: | Book Chapter |
|---|---|
| Publication: | Studies in Computational Intelligence |
| Series.: | Studies in Computational Intelligence |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH |
| Keywords: | 3D-repetitive-trajectory; Estimation; Extended kalman filter; High-speed interception; Least-square curve-fitting; RNN |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 15 Jun 2023 09:26 |
| Last Modified: | 15 Jun 2023 09:26 |
| URI: | https://eprints.iisc.ac.in/id/eprint/82040 |
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