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Environmental Feature Exploration With a Single Autonomous Vehicle
- Source :
- IEEE Transactions on Control Systems Technology. 28:1349-1362
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- In this paper, a sliding mode-based guidance strategy is proposed for the control of an autonomous vehicle. The aim of the autonomous vehicle deployment is the study of unknown environmental spatial features. The proposed approach allows the solution of both boundary tracking and source-seeking problems with a single autonomous vehicle capable of sensing the value of the spatial field at its position. The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the collected measurements without the necessity of preplanning or human intervention. Moreover, no a priori knowledge about the field and its gradient is required. The proposed strategy is based on the so-called suboptimal sliding mode controller. The guidance strategy is demonstrated by computer-based simulations and a set of boundary tracking experimental sea trials. The efficacy of the algorithm to autonomously steer the C-Enduro surface vehicle to follow a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper.
- Subjects :
- 0209 industrial biotechnology
Computer science
Sea trial
Boundary (topology)
02 engineering and technology
Sliding mode control
Vehicle dynamics
020901 industrial engineering & automation
Control and Systems Engineering
Control theory
Feature (computer vision)
0202 electrical engineering, electronic engineering, information engineering
Trajectory
A priori and a posteriori
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
Subjects
Details
- ISSN :
- 23740159 and 10636536
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Control Systems Technology
- Accession number :
- edsair.doi...........8daf898a0358c2554edec938f216d9a3