Combined Admittance Control With Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives

Abstract

This paper addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human-robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: i) the force exerted on the human must be controlled and limited, and ii) Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the Dynamic Movement Primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises.

Robot Learning Method for Human-like Arm Skills Based on the Hybrid Primitive Framework

“…This enhancement enables precise and flexible shape modifications within the target trajectory range [ 19 ]. This article addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human–robot interaction where parallel robots (PRs) are involved [ 20 ]. Khansari-Zadeh et al proposed a stable estimator of the dynamical systems algorithm as a demonstration learning algorithm.…”