Convex strategies for trajectory optimisation: application to the Polytope Traversal Problem

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

Abstract

Non-linear trajectory optimisation methods require good initial guesses to converge to a locally optimal solution. A feasible guess can often be obtained by allocating a large amount of time for the trajectory to be complete. However for unstable dynamical systems such as humanoid robots, this quasi-static assumption does not always hold.

We propose a conservative formulation of the trajectory problem that simultaneously computes a feasible path and its time allocation. The problem is solved as a convex optimisation problem guaranteed to converge to a feasible local optimum.

The approach is evaluated with the computation of feasible trajectories that traverse sequentially a sequence of polytopes. We demonstrate that on instances of the problem where quasi static solutions are not admissible, our approach is able to find a feasible solution with a success rate above 80% in all the scenarios considered, in less than 10ms for problems involving traversing less than 5 polytopes and less than 1s for problems involving 20 polytopes, thus demonstrating its ability to reliably provide initial guesses to advanced non linear solvers.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Robotics and Automation (ICRA 2022)
Number of pages6
Publication statusAccepted/In press - 31 Jan 2022
EventIEEE International Conference on Robotics and Automation - Philadelphia, United States
Duration: 23 May 202227 May 2022
https://www.icra2022.org/

Conference

ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2022
Country/TerritoryUnited States
CityPhiladelphia
Period23/05/2227/05/22
Internet address

Keywords

  • Trajectory Optimization
  • Robot control

Fingerprint

Dive into the research topics of 'Convex strategies for trajectory optimisation: application to the Polytope Traversal Problem'. Together they form a unique fingerprint.

Cite this