Optimal Motion Planning using Finite Fourier Series in a Learning-based Collision Field
This work addresses motion planning for robotic manipulators, but it is incremental as it builds on existing methods with a learning-based approach.
The paper tackles motion planning for manipulators by representing motion with finite Fourier series and adjusting harmonics to minimize a Hamiltonian combining collision and kinetic energy, achieving high reliability and efficiency in experiments.
This paper utilizes finite Fourier series to represent a time-continuous motion and proposes a novel planning method that adjusts the motion harmonics of each manipulator joint. Primarily, we sum the potential energy for collision detection and the kinetic energy up to calculate the Hamiltonian of the manipulator motion harmonics. Though the adaptive interior-point method is designed to modify the harmonics in its finite frequency domain, we still encounter the local minima due to the non-convexity of the collision field. In this way, we learn the collision field through a support vector machine with a Gaussian kernel, which is highly convex. The learning-based collision field is applied for Hamiltonian, and the experiment results show our method's high reliability and efficiency.