Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps
This work addresses path planning challenges for robots in constrained environments, but it appears incremental as it builds on existing constraint displacement methods.
The paper tackles the problem of enabling feasible robot paths by displacing obstacles, presenting a two-stage process that computes locally optimal obstacle displacements to achieve collision-free trajectories.
We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stage process that returns locally optimal obstacle displacements to enable a feasible path for the robot. The first stage proceeds by computing a trajectory through the obstacles while minimizing an appropriate objective function. In the second stage, these obstacles are displaced to make the computed robot trajectory feasible, that is, collision-free. Several examples are provided that successfully demonstrate our approach on two distinct classes of constraint displacement problems.