Improving productivity and worker conditions in assembly : part 2 : rapid deployment of learnable robot skills

Collaborative robots (cobots) have a strong potential to improve both productivity as well as the working conditions of assembly operators by assisting in their tasks and by decreasing their physical and cognitive stress. The use of cobots in factories however introduces multiple challenges: how should the overall assembly architecture look like? How to allocate specific (sub)tasks to the operator or the cobot? How to program and deploy the cobot? How to make changes to the robot program? In this paper dilogy, we briefly highlight our recent contributions to this field. In part I we presented our collaborative architecture for human-robot assembly tasks and discussed the working principles of our task allocation framework, based upon agent capabilities and ergonomic measurements. In this second part we focus on our programming by demonstration approach targeted at expediting the deployment of learnable robot skills

Accelerating interactive reinforcement learning by human advice for an assembly task by a cobot

The assembly industry is shifting more towards customizable products, or requiring assembly of small batches. This requires a lot of reprogramming, which is expensive because a specialized engineer is required. It would be an improvement if untrained workers could help a cobot to learn an assembly sequence by giving advice. Learning an assembly sequence is a hard task for a cobot, because the solution space increases drastically when the complexity of the task increases. This work introduces a novel method where human knowledge is used to reduce this solution space, and as a result increases the learning speed. The method proposed is the IRL-PBRS method, which uses Interactive Reinforcement Learning (IRL) to learn from human advice in an interactive way, and uses Potential Based Reward Shaping (PBRS), in a simulated environment, to focus learning on a smaller part of the solution space. The method was compared in simulation to two other feedback strategies. The results show that IRL-PBRS convergesmore quickly to a valid assembly sequence policy and does this with the fewest human interactions. Finally, a use case is presented where participants were asked to program an assembly task. Here, the results show that IRL-PBRS learns quickly enough to keep up with advice given by a user, and is able to adapt online to a changing knowledge base.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Improving Postural Ergonomics during Human–Robot Collaboration Using Particle Swarm Optimization: A Study in Virtual Environment

Musculoskeletal disorders caused by poor work posture are a serious concern in the industry since they lead to absenteeism and medical leave from work. In the context of human–robot collaboration, this issue can be mitigated if collaborative robots support human workers to perform their tasks more ergonomically. In this work, we propose a method to optimize human posture during human–robot collaboration using the Particle Swarm Optimization (PSO) algorithm. Our approach involves assigning an appropriate location to the robot’s end-effector to minimize the distance between the optimized posture of the human and their current posture in the working space. To measure human posture, we use the Rapid Entire Body Assessment score (REBA) calculated from body joint angles captured by a Kinect camera. To validate the effectiveness of our proposed method, we conducted a user study with 20 participants in a virtual reality environment. The PSO algorithm could position the robot end-effector to the optimal position close to real time. Our results showed that our method could improve ergonomics by 66%, indicating its potential for use in human–robot collaborative applications

Improving productivity and worker conditions in assembly Part 2: rapid deployment of learnable robot skills

status: Published onlin

Improving productivity and worker conditions in assembly : part 2 : rapid deployment of learnable robot skills

Collaborative robots (cobots) have a strong potential to improve both productivity as well as the working conditions of assembly operators by assisting in their tasks and by decreasing their physical and cognitive stress. The use of cobots in factories however introduces multiple challenges: how should the overall assembly architecture look like? How to allocate specific (sub)tasks to the operator or the cobot? How to program and deploy the cobot? How to make changes to the robot program? In this paper dilogy, we briefly highlight our recent contributions to this field. In part I we presented our collaborative architecture for human-robot assembly tasks and discussed the working principles of our task allocation framework, based upon agent capabilities and ergonomic measurements. In this second part we focus on our programming by demonstration approach targeted at expediting the deployment of learnable robot skills