Neurodynamics-Based Distributed Receding Horizon Trajectory Generation for Autonomous Surface Vehicles

Jiasen Wang, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper presents a neurodynamics-based distributed algorithm for trajectory generation for a group of autonomous surface vehicles (ASVs). By means of convexification, the trajectory generation problem is formulated as a distributed optimization problem with affine constraints and quadratic objectives. Neurodynamic approach and receding horizon mechanism are used for solving the distributed optimization problem. Simulation results on generating trajectories for four fully-actuated and under-actuated ASVs are reported to substantiate the efficacy of the algorithm.

Original languageEnglish
Title of host publicationNeural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
EditorsSeiichi Ozawa, Andrew Chi Sing Leung, Long Cheng
PublisherSpringer Verlag
Pages155-167
Number of pages13
ISBN (Print)9783030042387
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia
Duration: 13 Dec 201816 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11307 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Neural Information Processing, ICONIP 2018
Country/TerritoryCambodia
CitySiem Reap
Period13/12/1816/12/18

Keywords

  • Autonomous surface vehicles
  • Receding horizon planning
  • Recurrent neural networks

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