Sensorless Localization and Estimation of External Contact Force

Effectively managing unintended contacts between humans and collaborative robots is critical for ensuring safety in industrial and service environments. Sensorless methods, relying solely on proprioceptive measurements, such as joint torque sensors and encoders, and mathematical models provide a practical and cost-effective alternative to external sensing. This paper explores a hybrid strategy that integrates the classical momentum-based residual method using pseudoinversion with a contact particle filter (CPF) to enhance estimation of an external contact force and its localization across all links of a 7-DOF manipulator. For contacts on a distal robot link (i ≥ 6), the residual method yields accurate estimates of both a pure external force and its contact point, with CPF further refining the solution. Conversely, when contact occurs on a proximal link (i < 6), the associated link Jacobian loses rank and the pseudoinverse method alone fails. Instead, the proposed combination enables reliable estimation of contact force and location. Experimental validation on a KUKA LWR4+ robot highlights robustness and practical applicability of this approach.