Feng Yichang

Feng Yichang, Wang Jin, Lu Guodong

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.

Feng Yichang, Zhang Haiyun, Wang Jin et al.

We propose a new formulation of optimal motion planning (OMP) algorithm for robots operating in a hazardous environment, called adaptive Gaussian-process based stochastic trajectory optimization (AGP-STO). It first restarts the accelerated gradient descent with the reestimated Lipschitz constant (L-reAGD) to improve the computation efficiency, only requiring 1st-order momentum. However, it still cannot infer a global optimum of the nonconvex problem, informed by the prior information of Gaussian-process (GP) and obstacles. So it then integrates the adaptive stochastic trajectory optimization (ASTO) in the L-reestimation process to learn the GP-prior rewarded by the important samples via accelerated moving averaging (AMA). Moreover, we introduce the incremental optimal motion planning (iOMP) to upgrade AGP-STO to iAGP-STO. It interpolates the trajectory incrementally among the previously optimized waypoints to ensure time-continuous safety. Finally, we benchmark iAGP-STO against the numerical (CHOMP, TrajOpt, GPMP) and sampling (STOMP, RRT-Connect) methods and conduct the tuning experiment of key parameters to show how the integration of L-reAGD, ASTO, and iOMP elevates computation efficiency and reliability. Moreover, the implementation of iAGP- STO on LBR-iiwa, multi-AGV, and rethink-Baxter demonstrates its application in manipulation, collaboration, and assistance.