Name
Title | ||
|---|---|---|
Convex strategies for trajectory optimisation: application to the Polytope Traversal Problem |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICRA46639.2022.9811719 | IEEE International Conference on Robotics and Automation |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steve Tonneau | 1 | 0 | 1.69 |