Enhancing Robustness in Robot-Environment Interactions through Passive Compliant Degrees of Freedom: A Hybrid Position-Force Control Approach with Feedback Linearization
2026-07-01 • Robotics
Robotics
AI summaryⓘ
The authors address problems robots face when touching or hitting objects, like unexpected shocks and vibrations. They suggest adding a spring-and-damper component at the robot's hand to physically absorb some of these impacts before they affect the robot’s motors and control system. Their tests with a simple robot arm showed that this setup reduces errors and makes movements smoother compared to a rigid or spring-only end-effector. This means the new design helps robots handle changing environments better by damping out sudden forces.
hybrid position-force controlforce sensingspring-dampercompliant mechanismimpact attenuationrobot manipulatorphysical complianceMATLAB/Simulinkcontact dynamicsforce error
Authors
Rahman Ardakanian, Iman Kardan, AliAkbar Akbari, Ali Mousavi
Abstract
Robot-environment interactions in dynamic or unstructured settings are often degraded by impact shocks, vibrations, and uncertainties in contact geometry and mechanical properties. This paper proposes an interaction architecture that combines feedback-linearized hybrid position-force control with a passive compliant degree of freedom embedded at the end-effector. Unlike conventional hybrid position-force control, which relies mainly on active feedback, force sensing, and gain tuning, the proposed architecture uses a physical spring-damper interface to store and dissipate impact energy at the contact point before high-frequency shocks propagate to the actuated joints and force-control loop. The approach is evaluated in MATLAB/Simulink on a 2-DOF planar manipulator with three end-effector configurations: rigid, spring-only, and spring-damper. Results under fixed and time-varying interaction conditions show that the spring-damper configuration provides stronger attenuation of contact-induced oscillations, lower force and velocity error variance, and smoother joint-torque response. Representative reductions include 36.5% in fixed-environment tangential force-error standard deviation, 25.4% in variable-environment normal force-error standard deviation, and 41.1% in variable-environment normal velocity-error standard deviation.