Collaborative architecture for human-robot assembly tasks using multimodal sensors

Abstract Individuals with disabilities usually have difficulty in finding and maintaining employment prospects and thus, they are extremely underrepresented in the workforce. These challenges are even greater when the person has both cognitive and physical disabilities. While there is evidence supporting the benefits of employing individuals with disabilities in the workforce, employers are usually unprepared to hire individuals with disabilities. They are also concerned that the work productivity may be impacted by the employee with a disability. Thus, technology can play an important role in helping a person with cognitive and /or physical impairment work on tasks that require memorization and assembly performance. We will present a mobile technology system that was planned and piloted with working adults with physical and cognitive impairments. Founded on our pilot study, mobile technologies hold the potential to help people with disabilities to perform jobs that require memorization as well as systematic assembly tasks.

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Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Electronics ◽

10.3390/electronics10111317 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1317

Author(s):

Alejandro Chacón ◽

Pere Ponsa ◽

Cecilio Angulo

Keyword(s):

Interaction Analysis ◽

Mental Workload ◽

Cognitive Task ◽

Physical Work ◽

Work Skills ◽

Dexterous Manipulation ◽

Assembly Task ◽

Cognitive Interaction ◽

Human Operators ◽

Assembly Tasks

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

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Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-020-01303-z ◽

2021 ◽

Vol 101 (3) ◽

Author(s):

Korbinian Nottensteiner ◽

Arne Sachtler ◽

Alin Albu-Schäffer

Keyword(s):

Autonomous Robots ◽

Vision System ◽

Dynamic Environments ◽

Bayesian Framework ◽

Experimental Results ◽

Robotic Assembly ◽

Tactile Sensing ◽

Task Execution ◽

Industrial Assembly ◽

Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

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Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Autonomous Robots ◽

10.1007/s10514-021-09970-z ◽

2021 ◽

Author(s):

Loris Roveda ◽

Dario Piga

Keyword(s):

Impedance Control ◽

Interaction Force ◽

Industrial Robots ◽

Force Estimation ◽

Interaction Control ◽

Stiffness Properties ◽

Implementation Effort ◽

Coupling Dynamics ◽

And Control ◽

Assembly Tasks

AbstractIndustrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology.

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