A collision-free motion planning method by integrating complexity-reduction SLAM and learning-based artificial force design

L. Liu, R. Guo, J. Wu

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

In order to generally deal with the rotor-type UAV's collision-free motion planning problem in the unknown static environment, we propose a non-holonomic solution via integration of the KF-based SLAM technique and governing force design. The traditional SLAM is modified and reduced as a low-complexity form according to the fact that too early detected obstacle information can be regarded as nearly frozen after sufficient correction. The artificial force terms are designed in a intuitive and smart way, through employment of the wall-following rule and lessons from historical and current experience, which are taught by the bat's predation process. Further, they are converted to the real-time thrust vector expectation. Multiple simulation tests in both continuous and discrete scenes indicate that: (1) using slight sacrifice on the state estimate covariance can exchange pronounced reduction on structural complexity of the complete SLAM in return; (2) the LBAFD can not only mitigate limitations on the path oscillation, no passage between closely spaced obstacles and goal unreachability, but also lead to a high flying and exploration efficiency; (3) the integrated method demonstrates a relatively stable performance under different parameter settings and is even unconcerned to the surrounding characteristics.

Original languageEnglish
Pages (from-to)132-149
Number of pages18
JournalRobotics and Autonomous Systems
Volume100
Issue numberFebruari 2018
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Artificial force
  • Collision-free planning
  • Complexity reduction
  • Learning strategy
  • SLAM
  • Wall-following rule

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