Human-robot collaboration, whereby the human and the robot join their forces to achieve a task, opens new application opportunities in manufacturing. Robots can perform precise and repetitive operations while humans can execute tasks that require dexterity and problem-solving abilities. Moreover, collaborative robots can take over heavy-duty tasks. Musculoskeletal disorders (MSDs) are a serious health concern and the primary cause of absenteeism at work. While the role of the human is still essential in flexible production environment, the robot can help decreasing the workload of workers. This paper describes a novel framework for task allocation of human-robot assembly applications based on capabilities and ergonomics considerations. Capable agents are determined on the basis of agent characteristics and task requirements. Ergonomics is integrated by measuring the human body posture and the related workload. The developed framework was validated on a gearbox assembly use case using the collaborative robot Baxter.

Physical human-robot interfaces are a challenging aspect of exoskeleton design, mainly due to the fact that interfaces tend to migrate relatively to the body leading to discomfort and power losses. Therefore, the key is to develop interfaces that optimize attachment stiffness, i.e. reduce relative motion, without compromising comfort. To that end, we propose a method to obtain the optimal attachment pressure in terms of connection stiffness and comfort. The method is based on a soft robotic interface capable of actively controlling strapping pressure which is coupled to a cobot. Hereby the effects of strapping pressure on energetic losses, connection stiffness, and perceived comfort are analyzed. Results indicate a trade-off between connection stiffness and perceived comfort for this type of interface. An optimal strapping pressure was found in the 50 to 80 mmHg range. Connection stiffness was found to increase linearly over a pressure range from 0 mmHg (stiffness of 1139 N/m) to 100 mmHg (stiffness of 2232 N/m). And energetic losses were reduced by 42% by increasing connection stiffness. This research highlights the importance of strapping pressure when attaching an exoskeleton to a human and introduces a new adaptive interface to improve the coupling from an exoskeleton to an individual.

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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 converges more 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.

Research towards (compliant) actuators, especially redundant ones like the Series Parallel Elastic Actuator (SPEA), has led to the development of drive trains, which have demonstrated to increase efficiency, torque-to-mass-ratio, power-to-mass ratio, etc. In the field of robotics such drive trains can be implemented, enabling technological improvements like safe, adaptable and energy-efficient robots. The choice of the used motor and transmission system, as well as the compliant elements composing the drive train, are highly dependent of the application and more specifically on the allowable weight and size. In order to optimally design an actuator adapted to the desired characteristics and the available space, scaling laws governing the specific actuator can simplify and enhance the reliability of the design process. Although scaling laws of electric motors and links are known, none have been investigated for a complete redundant drive train. The present study proposes to fill this gap by providing scaling laws for electric motors in combination with their transmission system. These laws are extended towards parallelization, i.e. replacing one big motor with gearbox by several smaller ones in parallel. The results of this study show that the torque/mass ratio for a motor-gearbox can not be increased by parallelization, but that it can increase the torque/volume ratio. This is however only the case if a good topology is chosen.

The number of collaborative robots that perform different tasks in close proximity to humans is increasing. Previous studies showed that enabling non-expert users to program a cobot reduces the cost of robot maintenance and reprogramming. Since this approach is based on an interaction between the cobot and human partners, in this study, we investigate whether making this interaction more transparent can improve the interaction and lead to better performance for non-expert users. To evaluate the proposed methodology, an experiment with 67 participants is conducted. The obtained results show that providing explanation leads to higher performance, in terms of efficiency and efficacy, i.e., the number of times the task is completed without teaching a wrong instruction to the cobot is two times higher when explanations are provided. In addition, providing explanation also increases users’ satisfaction and trust in working with the cobot.

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