An ergonomic motion planner for industrial human-robot co-transportation of flexible materials

In recent years, human-robot collaboration in transportation tasks has gained popularity in industrial contexts as it allows for increased process efficiency. However, inadequate design and implementation of tasks result in uncomfortable body postures and physical strain for human operators, thus decreasing workers' comfort and compromising overall performance. This thesis is dedicated to enhancing the ergonomic conditions of industrial workers involved in human-robot collaborative transportation tasks by mitigating the risks of poor ergonomics, which can lead to Muscoloskeletal Disorders if persistent over time. In order to meet this challenge, an innovative method for generiting efficient trajectories is presented. It consists of integrating ergonomic principles into a path-planning algorithm: through the use of a computational model of the operator created using a skeleton identification software and RULA evaluation tool, a cost function is built and subsequently combined using T-RRT algorithm, thus providing a personalized ergonomic trajectory. Simulations and experiments are implemented on a dataset of 6 individuals and 5 types of environment setup, demonstrating the effectiveness of the algorithm in improving ergonomics, especially with the proximity of obstacles.