Multi-cost robotic motion planning under uncertainty

R. Simpson, J. Revell, A. Johansson, A. Richards

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


This paper describes an algorithm for robotic motion planning that is capable of optimising several cost functions simultaneously to provide optimised, feasible and collision-free paths. The algorithm is based on the best-first graph search algorithm using a Pareto frontier to evaluate costs at each node. Additionally, we include a calculation of the distribution of robot trajectories when the path is realised using a LQR based controller. This ensures that the possibility of collisions is greatly reduced. Results are provided that show multi-cost robotic path planning under position uncertainty and control constraints whilst simultaneously optimising distance travelled and fuel spent.
Original languageEnglish
Title of host publicationIntelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on
Number of pages6
Publication statusPublished - 1 Sep 2014


  • Pareto optimisation
  • collision avoidance
  • graph theory
  • linear quadratic control
  • mobile robots
  • multi-robot systems
  • search problems
  • trajectory control
  • uncertain systems
  • LQR based controller
  • Pareto frontier
  • best-first graph search algorithm
  • collision-free path
  • control constraint
  • cost function
  • distance travel
  • fuel spent
  • multicost robotic motion planning
  • multicost robotic path planning
  • position uncertainty
  • robot trajectory
  • Cost function
  • Covariance matrices
  • Heuristic algorithms
  • Robots
  • Trajectory
  • Uncertainty
  • Vectors


Dive into the research topics of 'Multi-cost robotic motion planning under uncertainty'. Together they form a unique fingerprint.

Cite this