Smooth Curve Fitting of Mobile Robot Trajectories Using Differential Evolution

Victor Parque*, Tomoyuki Miyashita

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Mobile robots have recently attracted the attention and applicability in field areas ubiquitously. Within the context of autonomous navigation, path planning is relevant for comfortability, safety, execution time and energy savings. In this paper, we propose an approach to suggest smooth paths from observed robot trajectories by optimizing fitting and smoothness criteria using Differential Evolution with distinct modes of initialization, selection pressure, exploration and exploitation. Our rigorous computational experiments using a relevant set of real-world robot trajectories from the Boe-Bot mobile robot architecture show the feasibility and efficiency of our approach in computing smooth curves, suggesting the superior performance of the greedy initialization scheme based on the triangular convex hull of the robot trajectory, and Differential Evolution with exploitative and parameter adaptation schemes such as Rank-Based Differential Evolution (RBDE), Adaptive Differential Evolution with External Archive (JADE) and Strategy Adaptation Differential Evolution (SADE). Our obtained results offer the building blocks to further advance towards developing data-driven curve fitting and path planning algorithms, which may find use in several real-world applications in Robotics and Operations Research.

Original languageEnglish
Article number9079813
Pages (from-to)82855-82866
Number of pages12
JournalIEEE Access
Publication statusPublished - 2020


  • Smooth curve fitting
  • differential evolution
  • mobile robots
  • optimization
  • path smoothing

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


Dive into the research topics of 'Smooth Curve Fitting of Mobile Robot Trajectories Using Differential Evolution'. Together they form a unique fingerprint.

Cite this