Assisting Operators in Heavy Industrial Tasks: On the Design of an Optimized Cooperative Impedance Fuzzy-Controller With Embedded Safety Rules

Abstract

Human-robot cooperation is increasingly demanded in industrial applications. Many tasks require the robot to enhance the capabilities of humans. In this scenario, safety also plays an important role in avoiding any accident involving humans, robots, and the environment. With this aim, the paper proposes a cooperative fuzzy-impedance control with embedded safety rules to assist human operators in heavy industrial applications while manipulating unknown weight parts. The proposed methodology is composed by four main components: (i) an inner Cartesian impedance controller (to achieve the compliant robot behavior), (ii) an outer fuzzy controller (to provide the assistance to the human operator), (iii) embedded safety rules (to limit force/velocity during the human-robot interaction enhancing safety), and (iv) a neural network approach (to optimize the control parameters for the human-robot collaboration on the basis of the target indexes of assistance performance defined for this purpose). The main achieved result refers to the capability of the controller to deal with uncertain payloads while assisting and empowering the human operator, both embedding in the controller safety features at force and velocity levels and minimizing the proposed performance indexes. The effectiveness of the proposed approach is verified with a KUKA iiwa 14 R820 manipulator in an experimental procedure where human subjects evaluate the robot performance in a collaborative lifting task of a 10 kg part.

Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

“…An important area of pHRC research focuses on a human and robot jointly manipulating a single tool [6]- [8]. These problems require a form of impedance control, which allows stiffnesses to be varied to achieve improved trajectory tracking while decreasing the human task effort.…”

Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation

“…al. [2,10] also propose the use of a neural network to optimize the control parameters, implementing a cooperative fuzzy-impedance control with embedded safety rules to assist human operators in heavy industrial applications while manipulating unknown weight parts. Moreover, Deep Learning algorithms have also been used for the identification of robot tool dynamics [11], allowing a fast computation and adding noise robustness.…”

“…The emergence of collaborative robotics changed the development of robotic solutions drastically for cooperative tasks. Industrial environments offer an interesting scenario for collaborative robotics, an environment where robots could act as assistants to operators [1,2], helping them in their usual tasks. Even so, the successful development of cooperative operations between the operators and robots gives rise to many challenges.…”

Compliance Control of Slave Manipulator Using EMG Signal for Telemanipulation

“…From this transformation, the position and orientation of the end effector can be presented in the Cartesian space. Moreover, the slave-actuator torque can be transformed using Equation (11). Furthermore, the Jacobian can be obtained from p(θ), as follows: Figure 16 shows the experimental block diagram and the posture, respectively.…”

“…For the safety of human-robot cooperation in heavy industrial applications, Roveda et al proposed cooperative fuzzy impedance control with safety rules. To optimize the performance of heavy tasks through human safety-based empowerment, minimal muscle activity using EMG sensors was used to assess the performance cooperation between the human and robot [11]. Roveda et al introduced a manual guidance controller with force tracking requirements which allows the human operator to perform both fine and large positioning of the robot system to improve the human capabilities while executing cooperative industrial tasks [12].…”