Combining Compliance Control, CAD Based Localization, and a Multi-Modal Gripper for Rapid and Robust Programming of Assembly Tasks

2020 ◽  
Author(s):  
Gal Gorjup ◽  
Geng Gao ◽  
Anany Dwivedi ◽  
Minas Liarokapis
Keyword(s):  
Compliance Control ◽  
Robust Programming ◽  
Assembly Tasks

Adaptive Reinforcement Learning and Its Application to Robot Compliance Learning

1995 ◽  
Vol 7 (3) ◽  
pp. 250-262 ◽  
Author(s):  
Boo-Ho Yang ◽  
◽  
Haruhiko Asada
Keyword(s):  
Reinforcement Learning ◽  
Force Feedback ◽  
Learning Algorithm ◽  
Robotic Assembly ◽  
Control Law ◽  
Learning Method ◽  
Compliance Control ◽  
Connectionist Networks ◽  
New Learning ◽  
Assembly Tasks

A new learning algorithm for connectionist networks that solves a class of optimal control problems is presented. The algorithm, called Adaptive Reinforcement Learning Algorithm, employs a second network to model immediate reinforcement provided from the task environment and adaptively identities it through repeated experience. Output perturbation and correlation techniques are used to translate mere critic signals into useful learning signals for the connectionist controller. Compared with the direct approaches of reinforcement learning, this algorithm shows faster and guaranteed improvement in the control performance. Robustness against inaccuracy of the model is also discussed. It is demonstrated by simulation that the adaptive reinforcement learning method is efficient and useful in learning a compliance control law in a class of robotic assembly tasks. A simple box palletizing task is used as an example, where a robot is required to move a rectangular part to the corner of a box. In the simulation, the robot is initially provided with only predetermined velocity command to follow the nominal trajectory. At each attempt, the box is randomly located and the part is randomly oriented within the grasp of the end-effector. Therefore, compliant motion control is necessary to guide the part to the corner of the box while avoiding excessive reaction forces caused by the collision with a wall. After repeating the failure in performing the task, the robot can successfully learn force feedback gains to modify its nominal motion. Our results show that the new learning method can be used to learn a compliance control law effectively.

Strategies for using instructions in assembly tasks

2007 ◽  
Author(s):  
Elsa Eiriksdottir ◽  
Richard Catrambone
Keyword(s):  
Assembly Tasks

Controllable Compliance Joint For Human Oriented Robots

2013 ◽  
Vol 43 (1) ◽  
pp. 47-60
Author(s):  
Mihail Tsveov ◽  
Dimitar Chakarov
Keyword(s):  
Numerical Experiments ◽  
Position Control ◽  
Joint Stiffness ◽  
Joint Motion ◽  
Compliance Control ◽  
Leaf Springs ◽  
Antagonistic Actuation

Abstract In the paper, different approaches for compliance control for human oriented robots are revealed. The approaches based on the non- antagonistic and antagonistic actuation are compared. In addition, an approach is investigated in this work for the compliance and the position control in the joint by means of antagonistic actuation. It is based on the capability of the joint with torsion leaf springs to adjust its stiffness. Models of joint stiffness are presented in this paper with antagonistic and non-antagonistic influence of the spring forces on the joint motion. The stiffness and the position control possibilities are investigated and the opportunity for their decoupling as well. Some results of numerical experiments are presented in the paper too.

scholarly journals Workplace Adaptive Technologies for Working Adults With Cognitive and Physical Disabilities

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 861-861
Author(s):  
Patricia Heyn
Keyword(s):  
Physical Disabilities ◽  
Cognitive Impairments ◽  
Work Productivity ◽  
Mobile Technologies ◽  
Individuals With Disabilities ◽  
Technology System ◽  
Working Adults ◽  
Assembly Performance ◽  
Adaptive Technologies ◽  
Assembly Tasks

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.

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

Electronics ◽  
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.

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

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.

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

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.

Sign in / Sign up

Export Citation Format

Share Document