Trajectory planning and tracking of robotic fish using ergodic exploration

Maria L. Castano, Anastasia Mavrommati, Todd D. Murphey, Xiaobo Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


In recent years, underwater robots that propel and maneuver themselves like real fish, often called robotic fish, have emerged as promising mobile sensing platforms for freshwater and marine environments. For these active monitoring applications, efficient exploration along with economical locomotion is highly important, in order to optimize sensing coverage and guarantee long field operation time. As a result, optimization of the sensing trajectory and energy-saving tracking of the planned trajectory are of interest. In this paper we adopt an ergodic exploration method to calculate an optimal sensing trajectory for a tail-actuated robotic fish, and propose a nonlinear model predictive control (NMPC) approach for tracking the generated trajectory. A high-fidelity, averaged nonlinear dynamic model is used for trajectory planning and control. In particular, the bias and amplitude of the tail-beat pattern are treated as the control inputs, and their physical bounds and the constraints on their changing rates are properly accounted for in the optimization process. Finally, simulation results are presented to illustrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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