A model-based residual approach for human-robot collaboration during manual polishing operations

A fully robotized polishing of metallic surfaces may be insufficient in case of parts with complex geometric shapes, where a manual intervention is still preferable. Within the EU SYMPLEXITY project, we are considering tasks where manual polishing operations are performed in strict physical Human-Robot Collaboration (HRC) between a robot holding the part and a human operator equipped with an abrasive tool. During the polishing task, the robot should firmly keep the workpiece in a prescribed sequence of poses, by monitoring and resisting to the external forces applied by the operator. However, the user may also wish to change the orientation of the part mounted on the robot, simply by pushing or pulling the robot body and changing thus its configuration. We propose a control algorithm that is able to distinguish the external torques acting at the robot joints in two components, one due to the polishing forces being applied at the end-effector level, the other due to the intentional physical interaction engaged by the human. The latter component is used to reconfigure the manipulator arm and, accordingly, its end-effector orientation. The workpiece position is kept instead fixed, by exploiting the intrinsic redundancy of this subtask. The controller uses a F/T sensor mounted at the robot wrist, together with our recently developed model-based technique (the residual method) that is able to estimate online the joint torques due to contact forces/torques applied at any place along the robot structure. In order to obtain a reliable residual, which is necessary to implement the control algorithm, an accurate robot dynamic model (including also friction effects at the joints and drive gains) needs to be identified first. The complete dynamic identification and the proposed control method for the human-robot collaborative polishing task are illustrated on a 6R UR10 lightweight manipulator mounting an ATI 6D sensor

Sensorless Physical Human-robot Interaction Using Deep-Learning

Physical human-robot interaction has been an area of interest for decades. Collaborative tasks, such as joint compliance, demand high-quality joint torque sensing. While external torque sensors are reliable, they come with the drawbacks of being expensive and vulnerable to impacts. To address these issues, studies have been conducted to estimate external torques using only internal signals, such as joint states and current measurements. However, insufficient attention has been given to friction hysteresis approximation, which is crucial for tasks involving extensive dynamic to static state transitions. In this paper, we propose a deep-learning-based method that leverages a novel long-term memory scheme to achieve dynamics identification, accurately approximating the static hysteresis. We also introduce modifications to the well-known Residual Learning architecture, retaining high accuracy while reducing inference time. The robustness of the proposed method is illustrated through a joint compliance and task compliance experiment.Comment: 7 pages, ICRA 2024 Submissio

Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review

Abstract Human–robot collaboration is a main technology of Industry 4.0 and is currently changing the shop floor of manufacturing companies. Collaborative robots are innovative industrial technologies introduced to help operators to perform manual activities in so called cyber-physical production systems and combine human inimitable abilities with smart machines strengths. Occupational health and safety criteria are of crucial importance in the implementation of collaborative robotics. Therefore, it is necessary to assess the state of the art for the design of safe and ergonomic collaborative robotic workcells. Emerging research fields beyond the state of the art are also of special interest. To achieve this goal this paper uses a systematic literature review methodology to review recent technical scientific bibliography and to identify current and future research fields. Main research themes addressed in the recent scientific literature regarding safety and ergonomics (or human factors) for industrial collaborative robotics were identified and categorized. The emerging research challenges and research fields were identified and analyzed based on the development of publications over time (annual growth)

Contact Estimation in Robot Interaction

In the paper, safety issues are examined in a scenario in which a robot manipulator and a human perform the same task in the same workspace. During the task execution, the human should be able to physically interact with the robot, and in this case an estimation algorithm for both interaction forces and a contact point is proposed in order to guarantee safety conditions. The method, starting from residual joint torque estimation, allows both direct and adaptive computation of the contact point and force, based on a principle of equivalence of the contact forces. At the same time, all the unintended contacts must be avoided, and a suitable post-collision strategy is considered to move the robot away from the collision area or else to reduce impact effects. Proper experimental tests have demonstrated the applicability in practice of both the post-impact strategy and the estimation algorithms; furthermore, experiments demonstrate the different behaviour resulting from the adaptation of the contact point as opposed to direct calculation

Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.Comment: Published at ACC 201

Collision Detection and Contact Point Estimation Using Virtual Joint Torque Sensing Applied to a Cobot

In physical human-robot interaction (pHRI) it is essential to reliably estimate and localize contact forces between the robot and the environment. In this paper, a complete contact detection, isolation, and reaction scheme is presented and tested on a new 6-dof industrial collaborative robot. We combine two popular methods, based on monitoring energy and generalized momentum, to detect and isolate collisions on the whole robot body in a more robust way. The experimental results show the effectiveness of our implementation on the LARA5 cobot, that only relies on motor current and joint encoder measurements. For validation purposes, contact forces are also measured using an external GTE CoboSafe sensor. After a successful collision detection, the contact point location is isolated using a combination of the residual method based on the generalized momentum with a contact particle filter (CPF) scheme. We show for the first time a successful implementation of such combination on a real robot, without relying on joint torque sensor measurements

Development of a Virtual Collision Sensor for Industrial Robots

Collision detection is a fundamental issue for the safety of a robotic cell. While several common methods require specific sensors or the knowledge of the robot dynamic model, the proposed solution is constituted by a virtual collision sensor for industrial manipulators, which requires as inputs only the motor currents measured by the standard sensors that equip a manipulator and the estimated currents provided by an internal dynamic model of the robot (i.e., the one used inside its controller), whose structure, parameters and accuracy are not known. The collision detection is achieved by comparing the absolute value of the current residue with a time-varying, positive-valued threshold function, including an estimate of the model error and a bias term, corresponding to the minimum collision torque to be detected. The value of such a term, defining the sensor sensitivity, can be simply imposed as constant, or automatically customized for a specific robotic application through a learning phase and a subsequent adaptation process, to achieve a more robust and faster collision detection, as well as the avoidance of any false collision warnings, even in case of slow variations of the robot behavior. Experimental results are provided to confirm the validity of the proposed solution, which is already adopted in some industrial scenarios

Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection

Parallel robots provide the potential to be lever-aged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9 m/s for detection within 9–58 ms and a reaction in the form of a zero-g mode.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Variable Stiffness Link (VSL): Toward inherently safe robotic manipulators

© 2017 IEEE. Nowadays, the field of industrial robotics focuses particularly on collaborative robots that are able to work closely together with a human worker in an inherently safe way. To detect and prevent harmful collisions, a number of solutions both from the actuation and sensing sides have been suggested. However, due to the rigid body structures of the majority of systems, the risk of harmful collisions with human operators in a collaborative environment remains. In this paper, we propose a novel concept for a collaborative robot made of Variable Stiffness Links (VSLs). The idea is to use a combination of silicone based structures and fabric materials to create stiffness-controllable links that are pneumatically actuated. According to the application, it is possible to change the stiffness of the links by varying the value of pressure inside their structure. Moreover, the pressure readings from the pressure sensors inside the regulators can be utilised to detect collisions between the manipulator body and a human worker, for instance. A set of experiments are performed with the aim to assess the performance of the VSL when embedded in a robotic manipulator. The effects of different loads and pressures on the workspace of the manipulator are evaluated together with the efficiency of the collision detection control system and hardware

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations