Impedance control of redundant manipulators for safe human-robot collaboration

In this paper, the impedance control paradigm is used to design control algorithms for safe human-robot collaboration. In particular, the problem of controlling a redundant robot manipulator in task space, while guaranteeing a compliant behavior for the redundant degrees of freedom, is considered first. The proposed approach allows safe and dependable reaction of the robot during deliberate or accidental physical interaction with a human or the environment, thanks to null-space impedance control. Moreover, the case of control for co-manipulation is considered. In particular, the role of the kinematic redundancy and that of the impedance parameters modulation are investigated. The algorithms are verified through experiments on a 7R KUKA lightweight robot arm

Toward Enabling Safe & Efficient Human-Robot Manipulation in Shared Workspaces

When humans interact, there are many avenues of physical communication available ranging from vocal to physical gestures. In our past observations, when humans collaborate on manipulation tasks in shared workspaces there is often minimal to no verbal or physical communication, yet the collaboration is still fluid with minimal interferences between partners. However, when humans perform similar tasks in the presence of a robot collaborator, manipulation can be clumsy, disconnected, or simply not human-like. The focus of this work is to leverage our observations of human-human interaction in a robot\u27s motion planner in order to facilitate more safe, efficient, and human-like collaborative manipulation in shared workspaces. We first present an approach to formulating the cost function for a motion planner intended for human-robot collaboration such that robot motions are both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot\u27s motion planner: (1) Avoidance of the workspace previously-occupied by the human, so robot motion is safe as possible, and (2) Consistency of the robot\u27s motion, so that the motion is predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human\u27s previous motion. We find using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms. Next, we present an approach to human-attention aware robot motion generation which attempts to convey intent of the robot\u27s task to its collaborator. We capture human attention through the combined use of a wearable eye-tracker and motion capture system. Since human attention isn\u27t static, we present a method of generating a motion policy that can be queried online. Finally, we show preliminary tests of this method

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control Retargetting Human Commands to Feasible Robot Control References

This article presents a robust and reliable human–robot collaboration (HRC) framework for bimanual manipulation. We propose an optimal motion adaptation method to retarget arbitrary human commands to feasible robot pose references while maintaining payload stability. The framework comprises three modules: 1) a task-space sequential equilibrium and inverse kinematics optimization ( task-space SEIKO ) for retargeting human commands and enforcing feasibility constraints, 2) an admittance controller to facilitate compliant human–robot physical interactions, and 3) a low-level controller improving stability during physical interactions. Experimental results show that the proposed framework successfully adapted infeasible and dangerous human commands into continuous motions within safe boundaries and achieved stable grasping and maneuvering of large and heavy objects on a real dual-arm robot via teleoperation and physical interaction. Furthermore, the framework demonstrated the capability in the assembly task of building blocks and the insertion task of industrial power connectors

Specifying task allocation in automotive wire harness assembly stations for Human-Robot Collaboration

Wire harness assembly is normally a manual assembly process that poses\ua0ergonomic\ua0challenges. As a consequence of the rapidly expanding electrification of vehicles and transportation systems, the demand for wire harnesses can be expected to grow radically, further increasing assembly operator challenges. Thus, automating this assembly process is highly prioritised by production engineers. The rapid development of industrial robot technology has enabled more human-robot collaboration possibilities, simplifying the automation of wire harness process tasks. However, successful automation applications involving humans require efficient and safe allocation of tasks between humans and technology. Unfortunately, present assembly system design methods may be obsolete and insufficient in light of the capabilities of emerging automation technologies such as collaborative robots. This paper presents a design and specification methodology for human-centred\ua0manufacturing systems\ua0and focuses on collaborative assembly operations in complex production systems. A case study on human-robot collaboration provides an application example from a wire-harness collaborative assembly process. The proposed design methodology combines\ua0hierarchical task analysis\ua0with assessments of cognitive and physical Levels of Automation (LoAc\ua0and LoAp). The assessments are then followed by evaluations of the Levels of human-robot Collaboration (LoC) and the Levels of operator Skill requirements (LoSr) respectively. A task allocation\ua0matrix supports\ua0the identification of possible combinations of automation and collaboration solutions for a human-centred and collaborative wire harness assembly process. System designers and integrators may utilise the design and specification methodology to identify the potential and extent of human-robot collaboration in collaborative manufacturing assembly operations

Evaluation of Human Robot Collaboration in Masonry Work Using Immersive Virtual Environments

With the advent of collaborative robots, there is a great potential to improve work performance by human-robot collaboration in engineering tasks. Construction is no exception. Many construction tasks are based on the movement of objects (e.g., material), which are viable candidates for human-robot collaboration. However, due to the physically imposing nature of robot operations and the unstructured environments typical in construction, it is crucial to provide a safe and reliable environment for human workers when performing collaborative work with robots. In this paper, we use Immersive Virtual Environments (IVEs) to evaluate a human response to robots (e.g. perceived safety, trust, and team identification) while performing collaborative construction tasks with robots. By adopting IVEs, various types of robots, interactions, and tasks can be easily tested and evaluated to determine the best HRC practice, without the need to build and evaluate a physical prototype. Several experimental scenarios simulating collaborative masonry tasks were implemented using the Unity3D Game Engine and an Oculus Rift 3D Head-Mounted Display (HMD). The results demonstrate that it is important to take into account work environment of human-robot collaboration in order to understand how humans perceive robots when working with them.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116277/1/CONVR2015_Final.pd

Modeling of physical human–robot interaction : admittance controllers applied to intelligent assist devices with large payload

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload

Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning

Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors

Kollaboratív robotok ipari alkalmazása - áttekintés

Az emberek és a robotok közötti együttműködésre már az első ipari robotkarok megjelenése óta igény jelentkezik. Ennek az együttműködésnek számos fizikai, jogszabályi, illetve szabványi feltétele van. Ebben a cikkben bemutatásra kerül milyen arányban használják az iparban az együttműködő robotkarokat, továbbá leírja milyen formái léteznek az ilyen jellegű ember-gép kapcsolatnak. Megvizsgálja, hogy mely szabályozásoknak kell megfelelnie a kollaboratív robot biztonságos és szabványos tervezése és üzemeltetése esetén, alapul véve az “ISO/TS 15066 Robotok és robotszerkezetek. Kollaboratív robotok” technikai specifikációt. Ezt követően alkalmazási példákon keresztül kerülnek bemutatásra különböző megoldások, esettanulmányok. Végezetül értékelésre kerül milyen lehetséges irányok mutatkoznak az ember-robot együttműködés során, figyelembevéve az átalakulóban lévő „2006/42/EK” Gépdirektívát. Abstract: There is a demand to collaborate between the human and robot, from the appearance of the first industrial robots. This collaboration has a lot of physical, legal, and standards aspects. This paper describes the proportion of collaborative robotic arms used in the industry, moreover it presents the forms of this type of human-machine relationship. Examination of the regulations to be followed is presented for the safe, standard design and operation of a collaborative robot, based on “ISO / TS 15066 Robots and Robotics. Collaborative robots." technical specification. After that, various solutions and case studies are presented through application examples. Finally, the possible directions for human-robot collaboration will be assessed, taking into account the evolving "Machinery Directive - 2006/42/EC"

Vision-Based Safety System for Barrierless Human-Robot Collaboration

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.Comment: Accepted for publication at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS