Finding a Feasible Initial Solution for Flatness-Based Multi-Link Manipulator Motion Planning under State and Control Constraints
This addresses motion planning for multi-link manipulators in robotics, but it is incremental as it focuses on initialization within existing optimization frameworks.
The paper tackles the problem of finding a feasible initial solution for motion planning of multi-link manipulators under state and control constraints, achieving this by constructing an initial feasible trajectory through time-optimal linear programming and scaling, with numerical experiments demonstrating practical applicability.
In this paper, we present a method to initialize at a feasible point and unfailingly solve a non-convex optimization problem in which a set-point motion is planned for a multi-link manipulator under state and control constraints. We construct an initial feasible solution by analyzing the final time effect for feasibility problems of flatness based motion planning problems. More specifically, we first find a feasible time-optimal trajectory under state constraints without a control constraint by solving a linear programming problem. Then, we find a feasible trajectory under control constraints by scaling the trajectory. To evaluate the practical applicability of the proposed method, we did numerical experiments to solve a multi-link manipulator motion planning problem by combining the method with recursive inverse dynamics algorithms.