Motion planning with movement primitives for cooperative aerial transportation in obstacle environment

Abstract

This paper presents a motion planning approach for cooperative transportation using aerial robots. We describe a framework based on Parametric Dynamic Movement Primitives (PDMPs) for coordinating multiple aerial robots and their manipulators quickly in an environment cluttered with obstacles. In order to emulate the optimal motion, we combine PDMPs and Rapidly Exploring Randomized Trees star (RRT*) by using the results of RRT* as demonstrations for PDMPs. For efficient description of the motions corresponding to the environment, we utilize Gaussian Process Regression (GPR) to acquire of the explicit relationship between environmental parameters and style parameters of PDMPs which decide the motions. Simulation and experiment results are attached to validate the proposed framework.